- A() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the homogeneous part, the coefficient matrix, of the linear system.
- a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
Gets the width (x-axis) of the rectangular region.
- a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
-
Gets the size of the one-dimensional space, that is, the range of x,
(0 < x < a).
- a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
-
Get the size of the two-dimensional space along the x-axis, that is, the range of x,
(0 < x < a).
- a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the size of the one-dimensional space, that is, the range of
x, (0 < x < a).
- a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
-
Gets the size of the one-dimensional space, that is, the range of x,
(0 < x < a).
- a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
-
Gets the size of the two-dimensional space along the x-axis, that is, the range of x,
(0 < x < a).
- a() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.QuadraticSyntheticDivision
-
Get a as in the remainder (b * (x + u) + a).
- A(int, double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
-
Compute an.
- a() - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Get a.
- A() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Gets A as in eq.
- A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
Get the constraint coefficients.
- A - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
This is either [A] or
[ A]
[-C]
- A(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Gets Ai.
- A(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
-
Gets Ai.
- A(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets Ai.
- A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
\[
A = [A_1, A_2, ...
- A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets A.
- A() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the coefficients, A, of the greater-than-or-equal-to constraints A * x ≥ b.
- A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the coefficients of the inequality constraints: A as in \(Ax \geq b\).
- a - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- a() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- A() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Gets the state transition probabilities.
- A() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the estimated sequence of A.
- A() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Gets the regressor matrix.
- A - Variable in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159.RandomMatrix
-
a random matrix constructed by AS159
- a0() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Gets the constant coefficient.
- a0() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the constant term.
- a1() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
-
Gets the ARCH coefficient.
- AbelianGroup<G> - Interface in com.numericalmethod.suanshu.algebra.structure
-
An Abelian group is a group with a binary additive operation (+), satisfying the group axioms:
closure
associativity
existence of additive identity
existence of additive opposite
commutativity of addition
- ABMPredictorCorrector - Interface in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector pair.
- ABMPredictorCorrector1 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 1.
- ABMPredictorCorrector1() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
-
- ABMPredictorCorrector2 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 2.
- ABMPredictorCorrector2() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
-
- ABMPredictorCorrector3 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 3.
- ABMPredictorCorrector3() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
-
- ABMPredictorCorrector4 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 4.
- ABMPredictorCorrector4() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
-
- ABMPredictorCorrector5 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 5.
- ABMPredictorCorrector5() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
-
- abs(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the absolute values of a vector, element-by-element.
- abs(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the absolute values.
- ABSOLUTE_ZERO_T0 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The absolute zero temperature in Celsius (°C).
- absoluteError(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Compute the absolute difference between x1 and x0.
- AbsoluteErrorPenalty - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the absolute error penalties.
- AbsoluteErrorPenalty(EqualityConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
-
Construct an absolute error penalty function from a collection of equality constraints.
- AbsoluteErrorPenalty(EqualityConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
-
Construct an absolute error penalty function from a collection of equality constraints.
- AbsoluteErrorPenalty(EqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
-
Construct an absolute error penalty function from a collection of equality constraints.
- AbsoluteTolerance - Class in com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance
-
The stopping criteria is that the norm of the residual r is equal to
or smaller than the specified tolerance, that is,
||r||2 ≤ tolerance
- AbsoluteTolerance() - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.AbsoluteTolerance
-
- AbsoluteTolerance(double) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.AbsoluteTolerance
-
Construct an instance with specified tolerance.
- AbstractBivariateEVD - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
- AbstractBivariateEVD() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.AbstractBivariateEVD
-
- AbstractBivariateProbabilityDistribution - Class in com.numericalmethod.suanshu.stats.distribution.multivariate
-
- AbstractBivariateProbabilityDistribution() - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.AbstractBivariateProbabilityDistribution
-
- AbstractBivariateRealFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
-
A bivariate real function takes two real arguments and outputs one real value.
- AbstractBivariateRealFunction() - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractBivariateRealFunction
-
- AbstractHybridMCMC - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid
-
Hybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional
Metropolis algorithm, with molecular dynamics simulation.
- AbstractHybridMCMC(Vector, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
-
Constructs a new instance with the given parameters.
- AbstractMetropolis - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis
-
The Metropolis algorithm is a Markov Chain Monte Carlo algorithm, which requires only a function
f proportional to the PDF from which we wish to sample.
- AbstractMetropolis(Vector, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
Constructs a new instance with the given parameters.
- AbstractR1RnFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2rm
-
This is a function that takes one real argument and outputs one vector value.
- AbstractR1RnFunction(int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2rm.AbstractR1RnFunction
-
- AbstractRealScalarFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
-
- AbstractRealScalarFunction(int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractRealScalarFunction
-
Construct an instance with the dimension of the domain.
- AbstractRealVectorFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2rm
-
- AbstractRealVectorFunction(int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2rm.AbstractRealVectorFunction
-
- AbstractTrivariateRealFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
-
A trivariate real function takes three real arguments and outputs one real value.
- AbstractTrivariateRealFunction() - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractTrivariateRealFunction
-
- AbstractUnivariateRealFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
A univariate real function takes one real argument and outputs one real value.
- AbstractUnivariateRealFunction() - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction
-
- acceptanceProbability(Vector, double, Vector, double, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.BoxGSAAcceptanceProbabilityFunction
-
- acceptanceProbability(Vector, double, Vector, double, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.GSAAcceptanceProbabilityFunction
-
- acceptanceProbability(Vector, double, Vector, double, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.MetropolisAcceptanceProbabilityFunction
-
- acceptanceProbability(Vector, double, Vector, double, double) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.TemperedAcceptanceProbabilityFunction
-
Computes the probability that the next state transition will be accepted.
- acceptanceRate() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
Gets the acceptance rate, i.e.
- acceptanceTemperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction
-
- acceptanceTemperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
-
- acceptanceTemperature(int) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.TemperatureFunction
-
Gets the acceptance temperature \(T^A_t\) at time t.
- ACERAnalysis - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
Average Conditional Exceedance Rate (ACER) method is for estimating the cdf of the maxima \(M\)
distribution from observations.
- ACERAnalysis() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Create an instance with the default values.
- ACERAnalysis(int, int, double, boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Create an instance with various options listed below.
- ACERAnalysis.Result - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
- ACERByCounting - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical
-
Estimate epsilons by counting conditional exceedances from the observations.
- ACERByCounting(double[], int) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.ACERByCounting
-
Create an instance for estimating epsilon for each of the given barrier levels.
- ACERConfidenceInterval - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
Using the given (estimated) ACER function as the mean, find the ACER parameters at the lower and
upper bounds of the estimated confidence interval of ACER values.
- ACERConfidenceInterval(ACERFunction.ACERParameter, EmpiricalACER, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- ACERFunction - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
The ACER (Average Conditional Exceedance Rate) function \(\epsilon_k(\eta)\) approximates the
probability
\[
\epsilon_k(\eta) = Pr(X_k > \eta | X_1 \le \eta, X_2 \le \eta, ..., X_{k-1} \le \eta)
\]
for a sequence of stochastic process observations \(X_i\) with a k-step memory.
- ACERFunction(ACERFunction.ACERParameter) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction
-
- ACERFunction.ACERParameter - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
- ACERInverseFunction - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
The inverse of the ACER function.
- ACERInverseFunction(ACERFunction.ACERParameter) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERInverseFunction
-
- ACERLogFunction - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
The ACER function in log scale (base e), i.e., \(log(\epsilon_k(\eta))\).
- ACERLogFunction(ACERFunction.ACERParameter) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERLogFunction
-
Create an instance with the ACER function parameter.
- ACERParameter(double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
Create an instance with a double[] which contains
q, b, a, c.
- ACERParameter(double, double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- ACERReturnLevel - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
Given an ACER function, compute the return level \(\eta\) for a given return period \(R\).
- ACERReturnLevel(ACERFunction.ACERParameter, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERReturnLevel
-
Create an instance with the (estimated) ACER function parameter and the total number of
events.
- ACERUtils - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical
-
Utility functions used in ACER empirical analysis.
- ACERUtils() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.ACERUtils
-
- acos(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Inverse of cosine.
- acosh(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc hyperbolic cosine of a value; the returned hyperbolic angle is positive.
- acot(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc cotangent of a value; the returned angle is in the range -pi/2 through
pi/2.
- acot2(double, double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar
coordinates (r, theta).
- acoth(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc hyperbolic cotangent of a value.
- acovers(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc coversine of a value; the returned angle is in the range -pi/2 through
pi/2.
- acsc(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc cosecant of a value; the returned angle is in the range -pi/2 through
pi/2.
- acsch(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc hyperbolic cosecant of a value.
- actions() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the the sequence of actions taken.
- ActiveList - Interface in com.numericalmethod.suanshu.misc.algorithm.bb
-
This interface defines the node popping strategy used in a branch-and-bound algorithm, e.g., depth-first-search, best-first-search.
- ActiveSet - Class in com.numericalmethod.suanshu.misc.algorithm
-
This class keeps track of the active and inactive indices.
- ActiveSet(boolean) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Construct a working set of active/inactive indices.
- ActiveSet(boolean, Collection<Integer>) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Construct a working set of active/inactive indices.
- ActiveSet(boolean, int[]) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Construct a working set of active/inactive indices.
- activeSize() - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Get the number of active indices.
- AdamsBashforthMoulton - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
This class uses an Adams-Bashford predictor and an Adams-Moulton corrector of the specified
order.
- AdamsBashforthMoulton(ABMPredictorCorrector, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.AdamsBashforthMoulton
-
Create a new instance of the Adams-Bashforth-Moulton method using the given
predictor-corrector pair.
- AdamsBashforthMoulton(ABMPredictorCorrector, double, ODEIntegrator) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.AdamsBashforthMoulton
-
Create a new instance of the Adams-Bashforth-Moulton method using the given
predictor-corrector pair and the given ODE integrator.
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- add(Matrix) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixRing
-
this + that
- add(DenseData) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Add up the elements in this and that, element-by-element.
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Computes the sum of two diagonal matrices.
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- add(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- add(MatrixAccess, MatrixAccess) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A1 + A2
- add(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- add(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- add(SparseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- add(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- add(ComplexMatrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- add(GenericFieldMatrix<F>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- add(RealMatrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- add(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- add(DenseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- add(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- add(Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Adds two vectors, element-by-element.
- add(Vector, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Adds a constant to a vector, element-by-element.
- add(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- add(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- add(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- add(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
\(this + that\)
- add(double) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Add a constant to all entries in this vector.
- add(G) - Method in interface com.numericalmethod.suanshu.algebra.structure.AbelianGroup
-
+ : G × G → G
- add(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- add(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Dynamically add points to the step function.
- add(double) - Method in class com.numericalmethod.suanshu.combinatorics.Counter
-
Add a number to the counter.
- add(double...) - Method in class com.numericalmethod.suanshu.combinatorics.Counter
-
Add numbers to the counter.
- add(Interval<T>) - Method in class com.numericalmethod.suanshu.interval.Intervals
-
Add an interval to the set.
- add(Interval<T>...) - Method in class com.numericalmethod.suanshu.interval.Intervals
-
Add intervals to the set.
- add(BBNode) - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.ActiveList
-
Add a node to the active list.
- add(double, T) - Method in class com.numericalmethod.suanshu.misc.algorithm.Bins
-
Add a valued item to the bin.
- add(T) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- add(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- add(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
- add(double[], double) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Add a double value to each element in an array.
- add(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
-
Add two double arrays, entry-by-entry.
- add(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- add(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- add(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- add(SOCPGeneralConstraint) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Add an SOCP constraint.
- addActive(Collection<Integer>) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Add active indices.
- addActive(int[]) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Add active indices.
- addActive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Add an active constraint by index.
- addAll(Collection<? extends T>) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- addCheck(PairingCheck) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.RobustCointegration
-
- addColAt(int, Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Adds a column at i.
- addColAt(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Adds a column at i.
- addColumn(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Column addition:
A[, j1] = A[, j1] + c * A[, j2]
- addData(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.LinearInterpolator
-
- addData(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.NevilleTable
-
- addData(OrderedPairs) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.OnlineInterpolator
-
Add more points for interpolation.
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.KendallRankCorrelation
-
Update the statistic with more data.
- addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.KendallRankCorrelation
-
Add the given two samples.
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.SpearmanRankCorrelation
-
Update the statistic with more data.
- addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.SpearmanRankCorrelation
-
Add the given two samples.
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
-
Update the covariance statistic with more data.
- addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
-
Update the covariance statistic with more data.
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedMean
-
- addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedMean
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedVariance
-
- addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedVariance
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile
-
- addData(double...) - Method in interface com.numericalmethod.suanshu.stats.descriptive.Statistic
-
Recompute the statistic with more data, incrementally if possible.
- addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.SynchronizedStatistic
-
- addEdge(E) - Method in interface com.numericalmethod.suanshu.graph.Graph
-
Adds an edge to this graph.
- addEdge(E) - Method in class com.numericalmethod.suanshu.graph.type.SparseDAGraph
-
- addEdge(E) - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
-
- addEdge(Arc<V>) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
Add an edge to the tree, connecting v1, the parent and v2..., the children.
- addEdge(Arc<VertexTree<T>>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- addEdges(Graph<V, E>, E...) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Add a set of edges to a graph.
- addFactor(int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection
-
Adds the indexed factor.
- addInactive(Collection<Integer>) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Add inactive indices.
- addInactive(int[]) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Add inactive indices.
- addInactive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Add an inactive constraint by index.
- addIterate(S) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.CountMonitor
-
- addIterate(S) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.IteratesMonitor
-
- addIterate(S) - Method in interface com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.IterationMonitor
-
Record a new iteration state.
- addIterate(S) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.NullMonitor
-
- addIterate(Vector) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.VectorMonitor
-
- AdditiveModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
- AdditiveModel(double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.AdditiveModel
-
Construct a univariate time series by adding up the components.
- AdditiveModel(double[], double[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.AdditiveModel
-
Construct a univariate time series by adding up the components.
- addRow(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Row addition:
A[i1, ] = A[i1, ] + c * A[i2, ]
- addRow(double, double[]) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Adds a row to the table.
- addRow(S, String, double...) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Inserts a row of data into the panel.
- addRow(PanelData<S>.Row) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Inserts a row of data into the panel.
- addRowAt(int, Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Adds a row at i.
- addRowAt(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Adds a row at i.
- addRows(double[][]) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Adds rows by a double[][].
- addVertex(V) - Method in interface com.numericalmethod.suanshu.graph.Graph
-
Adds a vertex to this graph.
- addVertex(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
-
- addVertex(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- addVertex(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- addVertices(Graph<V, ?>, V...) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Add a set of vertices to a graph.
- ADFAsymptoticDistribution - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
This class computes the asymptotic distribution of the Augmented Dickey-Fuller (ADF) test
statistic.
- ADFAsymptoticDistribution(TrendType, int, int, long) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution
-
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
- ADFAsymptoticDistribution(TrendType) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution
-
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
- ADFAsymptoticDistribution1 - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
- ADFAsymptoticDistribution1(ADFAsymptoticDistribution1.Type) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1
-
Deprecated.
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
- ADFAsymptoticDistribution1(int, int, ADFAsymptoticDistribution1.Type, long) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1
-
Deprecated.
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
- ADFAsymptoticDistribution1.Type - Enum in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
Deprecated.
the available types of Dickey-Fuller tests
- ADFDistribution - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
This represents an Augmented Dickey Fuller distribution.
- ADFDistributionTable - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf.table
-
A table contains the simulated observations/values of an empirical ADF distribution for a given set of parameters.
- ADFDistributionTable(MathTable) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.table.ADFDistributionTable
-
- ADFDistributionTable_CONSTANT_lag0 - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf.table
-
- ADFDistributionTable_CONSTANT_lag0() - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.table.ADFDistributionTable_CONSTANT_lag0
-
- ADFDistributionTable_CONSTANT_TIME_lag0 - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf.table
-
- ADFDistributionTable_CONSTANT_TIME_lag0() - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.table.ADFDistributionTable_CONSTANT_TIME_lag0
-
- ADFDistributionTable_NO_CONSTANT_lag0 - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf.table
-
- ADFDistributionTable_NO_CONSTANT_lag0() - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.table.ADFDistributionTable_NO_CONSTANT_lag0
-
- ADFFiniteSampleDistribution - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
This class computes the finite sample distribution of the Augmented Dickey-Fuller (ADF) test
statistics.
- ADFFiniteSampleDistribution(int, TrendType, boolean, int, int, int, long) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
- ADFFiniteSampleDistribution(int, TrendType, boolean, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
- ADFFiniteSampleDistribution(int, TrendType) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct a finite sample distribution for the original Dickey-Fuller test
statistic.
- ADFFiniteSampleDistribution(int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
- Aeq() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the coefficients, Aeq, of the equality constraints Aeq * x ≥ beq.
- Aeq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- Aeq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the coefficients of the equality constraints: Aeq as in \(A_{eq}x = b_{eq}\).
- Aeq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- aexsec(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc exsecant of a value; the returned angle is in the range 0.0 through
pi.
- AfterIterations - Class in com.numericalmethod.suanshu.misc.algorithm.stopcondition
-
Stops after a given number of iterations.
- AfterIterations(int) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AfterIterations
-
Stops after a given number of iterations.
- AfterNoImprovement - Class in com.numericalmethod.suanshu.misc.algorithm.stopcondition
-
- AfterNoImprovement(int) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AfterNoImprovement
-
- AhatEstimation - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008
-
Estimates the coefficient of a VAR(1) model by penalized maximum likelihood.
- AhatEstimation(Matrix, Matrix, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.AhatEstimation
-
Estimates the coefficient matrix of a vector autoregressive process of
order 1.
- ahav(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc haversine of a value; the returned angle is in the range 0 to pi.
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMExponentialDistribution
-
AIC = 2 * #param - 2 * log-likelihood
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson
-
- AIC() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
Gets the Akaike information criterion (AIC).
- AIC() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
-
Gets the AIC.
- AIC() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMInformationCriteria
-
Gets the Akaike information criterion.
- AIC() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Compute the AIC of fitted model.
- AIC() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Compute the AIC, a model selection criterion.
- AICC() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Compute the AICC of fitted model.
- AICC() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Compute the AICC, a model selection criterion.
- ak - Variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
the increment in the search direction
- algebraicMultiplicity() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get the multiplicity of the eigenvalue (a root) of the characteristic
polynomial.
- allForecasts() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Gets all the predictions of the next h steps in one vector.
- allForecasts() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Gets all the predictions of the next h steps in one vector.
- AllIntegers() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers
-
- allMSEs() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Gets all the mean squared errors (MSE) of the h-step ahead predictions.
- allMSEs() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Gets all the mean squared errors (MSE) of the h-step ahead predictions.
- alpha(Vector, Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
-
Get the percentage increment along the minimizer increment direction.
- alpha(Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- alpha(Vector, Vector, Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPASVariation
-
Get the percentage increment along the minimizer increment direction.
- alpha(Vector, Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPASVariation1
-
Get the percentage increment along the minimizer increment direction.
- alpha() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the set of adjusting coefficients, by columns.
- alpha - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
-
α: the shape parameter
- alpha() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.FixedEffectsModel
-
Gets the individual/subject specific terms.
- alpha(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarlingPValue
-
Gets the p-value for a test statistic.
- alpha() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Gets the ARCH coefficient.
- alpha() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the ARCH coefficients.
- alpha0 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
- alpha1 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
- AlternatingDirectionImplicitMethod - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2
-
Alternating direction implicit (ADI) method is an implicit method for obtaining numerical
approximations to the solution of a
HeatEquation2D.
- AlternatingDirectionImplicitMethod(double) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod
-
Create an ADI method with the given precision parameter.
- AlternatingDirectionImplicitMethod(double, boolean) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod
-
Create an ADI method with the given precision parameter, and choice for using multi-core
parallel computation for higher performance.
- AndersonDarling - Class in com.numericalmethod.suanshu.stats.test.distribution
-
This algorithm calculates the Anderson-Darling k-sample test statistics and p-values.
- AndersonDarling(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarling
-
Runs the Anderson-Darling test.
- AndersonDarlingPValue - Class in com.numericalmethod.suanshu.stats.test.distribution
-
This algorithm calculates the p-value when the Anderson-Darling statistic and the number of
samples are given.
- AndersonDarlingPValue(int) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarlingPValue
-
Construct the Anderson-Darling distribution for a particular number of samples.
- AndStopConditions - Class in com.numericalmethod.suanshu.misc.algorithm.stopcondition
-
Combines an arbitrary number of stop conditions, terminating when all conditions are met.
- AndStopConditions(StopCondition...) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AndStopConditions
-
- angle(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- angle(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- angle(Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the angle between two vectors.
- angle(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- angle(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Measure the angle, \(\theta\), between this and that.
- angle(H) - Method in interface com.numericalmethod.suanshu.algebra.structure.HilbertSpace
-
∠ : H × H → F
Inner product formalizes the geometrical notions such as the length of a vector and the angle between two vectors.
- angle(double, double, double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the angle \(\alpha\) opposite the side a, given the three side-lengths of the
triangle.
- angle(Pair, Pair, Pair) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Given a the coordinates of A, B and C, the apices of triangle ABC, returns the value of the
angle \(alpha\) at apex A.
- AnnealingFunction - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction
-
An annealing function or a tempered proposal function gives the next proposal/state from the
current state and temperature.
- AntitheticVariates - Class in com.numericalmethod.suanshu.stats.random.variancereduction
-
The antithetic variates technique consists, for every sample path obtained, in taking its
antithetic path - that is given a path \(\varepsilon_1,\dots,\varepsilon_M\) to also take, for
example, \(-\varepsilon_1,\dots,-\varepsilon_M\) or \(1-\varepsilon_1,\dots,1-\varepsilon_M\).
- AntitheticVariates(UnivariateRealFunction, RandomNumberGenerator, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.AntitheticVariates
-
Estimate \(E(f(X_1))\), where f is a function of a random variable.
- AntitheticVariates(UnivariateRealFunction, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.AntitheticVariates
-
- AntoniouLu2007 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint
-
This implementation is based on Algorithm 14.5 in the reference.
- APERY - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
the Apery's constant
- AR() - Method in class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
-
Get the AR coefficients.
- AR(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the i-th AR coefficient; AR(0) = 1.
- AR(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the i-th AR coefficient; AR(0) = 1.
- AR1GARCH11Model - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch
-
An AR1-GARCH11 model takes this form.
- AR1GARCH11Model(double, double, double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
- AR2() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: adjusted R-squared
- Arc<V> - Interface in com.numericalmethod.suanshu.graph
-
An arc is an ordered pair of vertices.
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPZeroValue
-
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
-
Checks whether all SOCP constraints represented by this portfolio
constraint are satisfied.
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
Checks whether all SOCP constraints represented by this portfolio
constraint are satisfied.
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
Checks whether all SOCP constraints represented by this portfolio
constraint are satisfied.
- areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Checks whether the constraints are satisfied with a solution vector x.
- areAllSparse(Matrix...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
- areAllSparse(Vector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
- areEqual(Matrix, Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks the equality of two matrices up to a precision.
- areEqual(Vector, Vector, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if two vectors are equal, i.e., v1 - v2 is a zero vector, up to a precision.
- areOrthogonal(Vector, Vector, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if two vectors are orthogonal, i.e., v1 ∙ v2 == 0.
- areOrthogonal(Vector[], double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a set of vectors are orthogonal, i.e., for any v1, v2 in v,
v1 ∙ v2 == 0.
- areOrthogonormal(Vector, Vector, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if two vectors are orthogonormal.
- areOrthogonormal(Vector[], double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a set of vectors are orthogonormal.
- arg() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the θ of the complex number in polar representation.
- ArgumentAssertion - Class in com.numericalmethod.suanshu.misc
-
Utility class for checking numerical arguments.
- ARIMAForecast - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
Forecasts an ARIMA time series using the innovative algorithm.
- ARIMAForecast(IntTimeTimeSeries, ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast
-
Constructs a forecaster for a time series assuming ARIMA model.
- ARIMAForecast(IntTimeTimeSeries, int, int, int, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast
-
Constructs a forecaster for a time series assuming ARIMA model.
- ARIMAForecast.Forecast - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
The forecast value and variance.
- ARIMAForecastMultiStep - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
Makes forecasts for a time series assuming an ARIMA model using the innovative algorithm.
- ARIMAForecastMultiStep(IntTimeTimeSeries, ARIMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARIMA model.
- ARIMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
An ARIMA(p, d, q) process, Xt, is such that
\[
(1 - B)^d X_t = Y_t
\]
where
B is the backward or lag operator, d the order of difference,
Yt an ARMA(p, q) process, for which
\[
Y_t = \mu + \Sigma \phi_i Y_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,
\]
- ARIMAModel(double, double[], int, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model.
- ARIMAModel(double, double[], int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model with unit variance.
- ARIMAModel(double[], int, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model with zero-intercept (mu).
- ARIMAModel(double[], int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model with unit variance and zero-intercept (mu).
- ARIMAModel(ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Copy constructor.
- ARIMASim - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
This class simulates an ARIMA (AutoRegressive Integrated Moving Average) process.
- ARIMASim(ARIMAModel, double[], double[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
-
Construct an ARIMA model.
- ARIMASim(ARIMAModel, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
-
Construct an ARIMA model.
- ARIMASim(ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
-
Construct an ARIMA model, using random standard Gaussian innovations.
- ARIMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables.
- ARIMAXModel(double, double[], int, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model.
- ARIMAXModel(double, double[], int, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model with unit variance.
- ARIMAXModel(double[], int, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model with zero-intercept (mu).
- ARIMAXModel(double[], int, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model with unit variance and zero-intercept (mu).
- ARIMAXModel(ARIMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Copy constructor.
- ARMAFit - Interface in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This interface represents a fitting method for estimating φ, θ, μ, σ2 in an ARMA model.
- ARMAForecast - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
Forecasts an ARMA time series using the innovative algorithm.
- ARMAForecast(IntTimeTimeSeries, ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Constructs a forecaster for a time series assuming ARMA model.
- ARMAForecast(IntTimeTimeSeries, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Constructs a forecaster for a time series assuming ARMA model.
- ARMAForecastMultiStep - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
Computes the h-step ahead prediction of a causal ARMA model, by the innovative algorithm.
- ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int, InnovationsAlgorithm, ARMAForecastOneStep) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARMA model.
- ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int, InnovationsAlgorithm) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARMA model.
- ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARMA model.
- ARMAForecastMultiStep(double[], ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARMA model.
- ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the one-step ahead prediction for an ARMA model.
- ARMAForecastOneStep - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
Computes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm.
- ARMAForecastOneStep(IntTimeTimeSeries, ARMAModel, InnovationsAlgorithm) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Makes the one-step ahead prediction for an ARMA model.
- ARMAForecastOneStep(IntTimeTimeSeries, ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Makes the one-step ahead prediction for an ARMA model.
- ARMAForecastOneStep(double[], ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Makes the one-step ahead prediction for an ARMA model.
- ARMAGARCHFit - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch
-
This implementation fits, for a data set, an ARMA-GARCH model by Quasi-Maximum Likelihood
Estimation.
- ARMAGARCHFit(double[], int, int, int, int, double, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
Constructs a model.
- ARMAGARCHFit(double[], int, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- ARMAGARCHModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch
-
An ARMA-GARCH model takes this form:
\[
X_t = \mu + \sum_{i=1}^p \phi_i X_{t-i} + \sum_{i=1}^q \theta_j \epsilon_{t-j} + \epsilon_t,
\\
\epsilon_t = \sqrt{h_t\eta_t},
\\
h_t = \alpha_0 + \sum_{i=1}^{r} (\alpha_i e_{t-i}^2) + \sum_{i=1}^{s} (\beta_i h_{t-i})
\]
- ARMAGARCHModel(ARMAModel, GARCHModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
-
Construct a univariate ARMA-GARCH model.
- ARMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
A univariate ARMA model, Xt, takes this form.
- ARMAModel(double, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model.
- ARMAModel(double, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model with unit variance.
- ARMAModel(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model with zero-intercept (mu).
- ARMAModel(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model with unit variance and zero-intercept
(mu).
- ARMAModel(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Copy constructor.
- armaxMean(Matrix, Matrix, Vector) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Compute the multivariate ARMAX conditional mean.
- armaxMean(double[], double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Compute the univariate ARMAX conditional mean.
- ARMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
- ARMAXModel(double, double[], double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model.
- ARMAXModel(double, double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model with unit variance.
- ARMAXModel(double[], double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model with zero-intercept (mu).
- ARMAXModel(double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model with unit variance and zero-intercept (mu).
- ARMAXModel(ARMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Copy constructor.
- ARModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This class represents an AR model.
- ARModel(double, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model.
- ARModel(double, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model with unit variance.
- ARModel(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model with zero-intercept (mu).
- ARModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model with unit variance and zero-intercept (mu).
- ARModel(ARModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Copy constructor.
- ArrayUtils - Class in com.numericalmethod.suanshu.misc
-
- ARResamplerFactory - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler
-
- ARResamplerFactory(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
-
- ARResamplerFactory() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
-
- ARTIFICIAL - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- ARTIFICIAL_COST - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- AS159 - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the
lists of row and column sums.
- AS159(int[], int[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
-
Constructs a random table generator according to the row and column totals.
- AS159(int[], int[], RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
-
Constructs a random table generator according to the row and column totals.
- AS159.RandomMatrix - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
a random matrix generated by AS159 and its probability
- asArray() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Cast this data structure as a double[].
- asec(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc secant of a value; the returned angle is in the range 0.0 through pi.
- asech(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc hyperbolic secant of a value.
- asin(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Inverse of sine.
- asinh(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc hyperbolic sine of a value.
- assertEqual(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test if
Number x equal to
bound.
- assertEqual(T, T, String, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test if two
Numbers
x1 and
x2 are equal.
- assertFalse(boolean, String, Object...) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Check if an argument condition is false.
- assertGreaterThan(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test if
Number x is greater than
bound.
- assertLessThan(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test if
Number x is less than
bound.
- assertNegative(T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
- assertNonNegative(T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test if
Number x is non-negative.
- assertNonPositive(T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test if
Number x is non-positive.
- assertNormalDouble(double, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Check if an argument is a normal
double value (that is,
NOT
Double.NaN nor infinity).
- assertNormalFloat(float, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Check if an argument is a normal
float value (that is,
NOT
Float.NaN nor infinity).
- assertNotGreaterThan(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test if
Number x is not greater than
bound.
- assertNotInfinity(double, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
- assertNotInfinity(float, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
- assertNotLessThan(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test if
Number x is not less than
bound.
- assertNotNaN(double, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
- assertNotNaN(float, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
- assertNotNull(Object, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Check if obj is not null.
- assertNull(Object, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Check if obj is null.
- assertPositive(T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
- assertRange(T, T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test whether the specified
Number occurs within the range [
low,
high]
(both inclusive).
- assertRangeLeftOpen(T, T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test whether the specified
Number occurs within the range (
low,
high]
(left exclusive, right inclusive).
- assertRangeOpen(T, T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test whether the specified
Number occurs within the range (
low,
high)
(both exclusive).
- assertRangeRightOpen(T, T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Test whether the specified
Number occurs within the range [
low,
high)
(left inclusive, right exclusive).
- assertTrue(boolean, String, Object...) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
-
Check if an argument condition is true.
- asymptoticCDF(double) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
This is the asymptotic distribution of the Kolmogorov distribution.
- asymptoticCDF(double, double) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
This is the asymptotic distribution of the one-sided Kolmogorov distribution.
- atan(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Inverse of tangent.
- atanh(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc hyperbolic tangent of a value.
- ATOMIC_MASS_MU - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The atomic mass constant \(m_u\) in kilograms (kg).
- AtThreshold - Class in com.numericalmethod.suanshu.misc.algorithm.stopcondition
-
Stops when the value reaches a given value with a given precision.
- AtThreshold(double, double) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AtThreshold
-
Stops when the value reaches a given value with a given precision.
- AugmentedDickeyFuller - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
The Augmented Dickey Fuller test tests whether a one-time differencing (d = 1) will make the time
series stationary.
- AugmentedDickeyFuller(double[], TrendType, int, ADFDistribution) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
Performs the Augmented Dickey-Fuller test to test for the existence of unit root.
- AugmentedDickeyFuller(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
Performs the Augmented Dickey-Fuller test to test for the existence of unit root.
- AutoARIMAFit - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
Selects the order and estimates the coefficients of an ARIMA model automatically by AIC or AICC.
- AutoARIMAFit(double[], int, int, int, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
-
Automatically selects and estimates the ARIMA model using custom parameters.
- AutoARIMAFit(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
-
Automatically selects and estimates the ARIMA model using default parameters.
- AutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that
EXt = 0.
- AutoCorrelation(ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
Compute the auto-correlation function for an ARMA model.
- AutoCorrelationFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate
-
This is the auto-correlation function of a univariate time series {xt}.
- AutoCorrelationFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCorrelationFunction
-
- AutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by
recursion.
- AutoCovariance(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
Computes the auto-covariance function for an ARMA model.
- AutoCovarianceFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate
-
This is the auto-covariance function of a univariate time series {xt}.
- AutoCovarianceFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCovarianceFunction
-
- autoEpsilon(double...) - Static method in class com.numericalmethod.suanshu.misc.PrecisionUtils
-
Guess a reasonable precision parameter.
- autoEpsilon(double[]...) - Static method in class com.numericalmethod.suanshu.misc.PrecisionUtils
-
Guess a reasonable precision parameter.
- autoEpsilon(MatrixTable) - Static method in class com.numericalmethod.suanshu.misc.PrecisionUtils
-
Guess a reasonable precision parameter.
- AutoParallelMatrixMathOperation - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation
-
This class uses
ParallelMatrixMathOperation when the first input matrix argument's size
is greater than the defined threshold; otherwise, it uses
SimpleMatrixMathOperation.
- AutoParallelMatrixMathOperation() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- AutoParallelMatrixMathOperation(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- AverageImplicitModelPCA - Class in com.numericalmethod.suanshu.stats.factor.implicitmodelpca
-
This decomposes the observations into a model of one explicit factor, the
average observation per subject, and implicit factors.
- AverageImplicitModelPCA(Matrix, int) - Constructor for class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.AverageImplicitModelPCA
-
Constructs an explicit-implicit model for a time series of vectored
observations
- AverageImplicitModelPCA(Matrix, double) - Constructor for class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.AverageImplicitModelPCA
-
Constructs an explicit-implicit model for a time series of vectored
observations
- avers(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the arc versine of a value; the returned angle is in the range zero through
pi.
- avg_duration1 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.CalibrationParam
-
- avg_duration2 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.CalibrationParam
-
- AVOGADRO_NA - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The Avogadro constant \(N_A\), \(L\) in units per mole (mol-1).
- B() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization
-
Gets B, which is the square upper part of U.t().multiply(A).multiply(V).
- B() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
-
- B() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
-
Gets B, which is the square upper part of U.t().multiply(A).multiply(V).
- b() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the non-homogeneous part, the right-hand side vector, of the linear system.
- B() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.Pow
-
Get the double precision matrix.
- b() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
Gets the height (y-axis) of the rectangular region.
- b() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
-
Get the size of the two-dimensional space along the y-axis, that is, the range of y,
(0 < y < b).
- b() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
-
Gets the size of the two-dimensional space along the y-axis, that is, the range of y,
(0 < y < b).
- b() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.QuadraticSyntheticDivision
-
Get b as in the remainder (b * (x + u) + a).
- B(int, double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
-
Compute bn.
- b() - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Get b.
- B() - Method in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA.Signal
-
- B() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Gets B as in eq.
- b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
Get the constraint values.
- b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Gets b.
- b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Gets the objective vector, b, in the compact form.
- b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets b.
- b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets b.
- b() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the values, b, of the greater-than-or-equal-to constraints A * x ≥ b.
- b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- B - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the values of the inequality constraints: b as in \(Ax \geq b\).
- b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- b() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- B() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets B, the implicit factor loading matrix.
- b(int) - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets the implicit factor loading for the n-th subject.
- B() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets B, the factor loading matrix.
- b(int) - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets the factor loading for the n-th subject.
- B() - Method in class com.numericalmethod.suanshu.stats.hmm.discrete.DiscreteHMM
-
Gets the conditional probabilities of the observation symbols: rows
correspond to state; columns corresponds symbols.
- b() - Method in interface com.numericalmethod.suanshu.stats.random.variancereduction.ControlVariates.Estimator
-
Gets the optimal b.
- B(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Get the Brownian motion value at the i-th time point.
- b1() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
-
Gets the GARCH coefficient.
- backSearch(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDeflationSearch
-
Finds H22 such that H22 is the largest unreduced
Hessenberg sub-matrix, and H33 is upper quasi-triangular.
- backSearch(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
-
- backSearch(Matrix, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
-
- backward(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
-
Perform a backward sweep.
- BackwardElimination - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
Constructs a GLM model for a set of observations using the backward elimination method.
- BackwardElimination(GLMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.BackwardElimination
-
Constructs a GLM model using the backward elimination method, with
EliminationByAIC as the default algorithm.
- BackwardElimination(GLMProblem, BackwardElimination.Step) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.BackwardElimination
-
Constructs a GLM model using the backward elimination method.
- BackwardElimination.Step - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
- BackwardSubstitution - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
-
Backward substitution solves a matrix equation in the form Ux = b
by an iterative process for an upper triangular matrix U.
- BackwardSubstitution() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
- BanachSpace<B,F extends Field<F> & Comparable<F>> - Interface in com.numericalmethod.suanshu.algebra.structure
-
A Banach space, B, is a complete normed vector space such that
every Cauchy sequence (with respect to the metric d(x, y) = |x - y|) in B has a limit in B.
- Bartlett - Class in com.numericalmethod.suanshu.stats.test.variance
-
Bartlett's test is used to test if k samples are from populations with equal variances, hence homoscedasticity.
- Bartlett(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.Bartlett
-
Perform the Bartlett test to test for equal variances across the groups.
- base() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.Pow
-
Get the radix or base of the coefficient.
- basis() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the kernel basis.
- Basis - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation
-
A basis is a set of linearly independent vectors spanning a vector space.
- Basis(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Basis
-
Construct a vector that corresponds to the i-th dimension in Rn.
- basisAndFreeVars() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the kernel basis and the associated free variables for each basis/column.
- BaumWelch - Class in com.numericalmethod.suanshu.stats.hmm.discrete
-
This implementation trains an HMM model by observations using the Baum–Welch
algorithm.
- BaumWelch(int[], DiscreteHMM, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.discrete.BaumWelch
-
Constructs an HMM model by training an initial model using the Baum–Welch
algorithm.
- BBNode - Interface in com.numericalmethod.suanshu.misc.algorithm.bb
-
A branch-and-bound algorithm maintains a tree of nodes to keep track of the search paths and the pruned paths.
- begin() - Method in class com.numericalmethod.suanshu.interval.Interval
-
Get the beginning of this interval.
- BEGINNING_OF_TIME - Static variable in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
This represents a time before all (representable) times.
- BEGINNING_OF_TIME_LONG - Static variable in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
This represents a time before all (representable) times, in long
representation.
- beq() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the values, beq, of the equality constraints Aeq * x ≥ beq.
- beq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- beq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the values of the equality constraints: beq as in \(A_{eq}x = b_{eq}\).
- beq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- BernoulliTrial - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
-
A Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes,
"success" and "failure", in which the probability of success, p, is the same every time
the experiment is conducted.
- BernoulliTrial(RandomNumberGenerator, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.BernoulliTrial
-
- Best1Bin - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
The Best-1-Bin rule is the same as the
Rand-1-Bin rule,
except that it always pick the best candidate in the population to be the base.
- Best1Bin(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Best1Bin
-
Construct an instance of Best1Bin.
- Best2Bin - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
The Best-1-Bin rule always picks the best chromosome as the base.
- Best2Bin(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin
-
Construct an instance of Best2Bin.
- Best2Bin.DeBest2BinCell - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
- beta - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
β = 2 / v'v.
- beta() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
-
Gets the value of the wave coefficient β
- beta() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
-
Get the value of the wave coefficient β
- beta() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
-
Gets β in the equation (also called thermal diffusivity in case of the
heat equation).
- beta() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
-
Gets the coefficient in the PDE (thermal diffusivity in case of the heat equation).
- Beta - Class in com.numericalmethod.suanshu.analysis.function.special.beta
-
The beta function defined as:
\[
B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0
\]
The R equivalent function is
beta.
- Beta() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.Beta
-
- beta - Variable in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- beta() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- beta() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the set of cointegrating factors, by columns.
- beta(int) - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the r-th cointegrating factor, counting from 1.
- beta - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
-
β: the shape parameter
- beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
Gets the GLM coefficients estimator, β^.
- beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
Gets the GLM coefficient estimator, β^.
- beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyLARS
-
- beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyQP
-
- beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
-
- beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.UnconstrainedLASSObyQP
-
- beta() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.LinearModel
-
Gets \(\hat{\beta}\) and statistics.
- beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
-
- beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
- beta() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Gets the GARCH coefficient.
- beta() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the GARCH coefficients.
- BetaDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The beta distribution is the posterior distribution of the parameter p of a binomial
distribution
after observing α - 1 independent events with probability p and
β - 1 with probability 1 - p,
if the prior distribution of p is uniform.
- BetaDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
Construct a Beta distribution.
- betaHat() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.GLMFitting
-
Gets the estimates of β, β^, as in
E(Y) = μ = g-1(Xβ)
- betaHat() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
- betaHat() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
- betaHat() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMBeta
-
Gets the coefficient estimates, β^.
- BetaMixtureDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Beta distribution to model the observations.
- BetaMixtureDistribution(BetaMixtureDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution
-
Constructs a Beta distribution for each state in the HMM model.
- BetaMixtureDistribution(BetaMixtureDistribution.Lambda[], int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution
-
Constructs a Beta distribution for each state in the HMM model.
- BetaMixtureDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the Beta distribution parameters
- BetaRegularized - Class in com.numericalmethod.suanshu.analysis.function.special.beta
-
The Regularized Incomplete Beta function is defined as:
\[
I_x(p,q) = \frac{B(x;\,p,q)}{B(p,q)} = \frac{1}{B(p,q)} \int_0^x t^{p-1}\,(1-t)^{q-1}\,dt, p > 0, q > 0
\]
The R equivalent function is
pbeta.
- BetaRegularized(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularized
-
Construct an instance of Ix(p,q) with the parameters p and q.
- BetaRegularizedInverse - Class in com.numericalmethod.suanshu.analysis.function.special.beta
-
The inverse of the Regularized Incomplete Beta function is defined at:
\[
x = I^{-1}_{(p,q)}(u), 0 \le u \le 1
\]
The R equivalent function is
qbeta.
- BetaRegularizedInverse(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularizedInverse
-
Construct an instance of \(I^{-1}_{(p,q)}(u)\) with parameters p and p.
- betas() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the sequence of betas.
- BFGSImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer.BFGSImpl
-
- BFGSMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
-
The Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method
to solve unconstrained nonlinear optimization problems.
- BFGSMinimizer(boolean, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
-
Construct a multivariate minimizer using the BFGS method.
- BFGSMinimizer.BFGSImpl - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
-
an implementation of the BFGS algorithm
- BFS<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
-
This class implements the breadth-first-search using iteration.
- BFS(Graph<V, ? extends Edge<V>>) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS
-
Constructs a BFS tree of a graph.
- BFS(Graph<W, ? extends Edge<V>>, V, int) - Static method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS
-
Runs the breadth-first-search on a graph from a designated root.
- BFS.Node<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
-
This is a node in a BFS-spanning tree.
- bias(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
Computes the amount of deviation from neutrality, hence bias.
- bias(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
Computes the amount of deviation from self financing, hence bias.
- bias(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPZeroValue
-
Computes the amount of deviation from zero value, hence bias.
- BIC() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMInformationCriteria
-
Gets the Bayesian information criterion.
- BiconjugateGradientSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Biconjugate Gradient method (BiCG) is useful for solving non-symmetric
n-by-n linear systems.
- BiconjugateGradientSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
Construct a Biconjugate Gradient (BiCG) solver.
- BiconjugateGradientSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
Construct a Biconjugate Gradient (BiCG) solver.
- BiconjugateGradientStabilizedSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Biconjugate Gradient Stabilized (BiCGSTAB) method is useful for solving
non-symmetric n-by-n linear systems.
- BiconjugateGradientStabilizedSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
- BiconjugateGradientStabilizedSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
- BicubicInterpolation - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
Bicubic interpolation is the two-dimensional equivalent of cubic Hermite spline interpolation.
- BicubicInterpolation() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
-
- BicubicInterpolation(BicubicInterpolation.PartialDerivatives) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
-
Create a new instance which uses the given derivatives to interpolate.
- BicubicInterpolation.PartialDerivatives - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
Specify the partial derivatives defined at points on a
BivariateGrid.
- BicubicSpline - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
Bicubic splines are the two-dimensional equivalent of cubic splines.
- BicubicSpline() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicSpline
-
- BiDiagonalization - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization
-
Given a tall (m x n) matrix A, where m ≥ n,
find orthogonal matrices U and V such that U' * A * V = B.
- BiDiagonalizationByGolubKahanLanczos - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization
-
This implementation uses Golub-Kahan-Lanczos algorithm with reorthogonalization.
- BiDiagonalizationByGolubKahanLanczos(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
-
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
- BiDiagonalizationByGolubKahanLanczos(Matrix, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
-
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
- BiDiagonalizationByGolubKahanLanczos(Matrix, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
-
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
- BiDiagonalizationByHouseholder - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization
-
Given a tall (m x n) matrix A, where m ≥ n,
we find orthogonal matrices U and V such that U' * A * V = B.
- BiDiagonalizationByHouseholder(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
-
Runs the Householder bi-diagonalization for a tall matrix.
- BidiagonalMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
-
A bi-diagonal matrix is either upper or lower diagonal.
- BidiagonalMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Constructs a bi-diagonal matrix from a 2D double[][] array.
- BidiagonalMatrix(int, BidiagonalMatrix.BidiagonalMatrixType) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Constructs a 0 bi-diagonal matrix of dimension dim * dim.
- BidiagonalMatrix(BidiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Copy constructor.
- BidiagonalMatrix.BidiagonalMatrixType - Enum in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
-
the available types of bi-diagonal matrices
- BidiagonalSVDbyMR3 - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3
-
Given a bidiagonal matrix A, computes the singular value decomposition (SVD) of A,
using "Algorithm of Multiple Relatively Robust Representations" (MRRR).
- BidiagonalSVDbyMR3(BidiagonalMatrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
Creates a singular value decomposition for a bidiagonal matrix A.
- BigDecimalUtils - Class in com.numericalmethod.suanshu.number.big
-
These are the utility functions to manipulate
BigDecimal.
- bigDecimalValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
- BigIntegerUtils - Class in com.numericalmethod.suanshu.number.big
-
These are the utility functions to manipulate
BigInteger.
- BilinearInterpolation - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
Bilinear interpolation is the 2-dimensional equivalent of linear interpolation.
- BilinearInterpolation() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BilinearInterpolation
-
- bin(MultinomialRVG) - Static method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Picks the first non-empty bin.
- BinomialDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments,
each of which yields success with probability p.
- BinomialDistribution(int, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
Construct a Binomial distribution.
- BinomialMixtureDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Binomial distribution to model the observations.
- BinomialMixtureDistribution(BinomialMixtureDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution
-
Constructs a Binomial distribution for each state in the HMM model.
- BinomialMixtureDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the Binomial distribution parameters
- BinomialRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
-
This random number generator samples from the binomial distribution.
- BinomialRNG(int, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.BinomialRNG
-
Construct a random number generator to sample from the binomial distribution.
- BinomialRNG(int, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.BinomialRNG
-
Construct a random number generator to sample from the binomial distribution.
- Bins<T> - Class in com.numericalmethod.suanshu.misc.algorithm
-
This class divides the items based on their keys into a number of bins.
- Bins(int) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.Bins
-
Constructs an empty bin of valued items.
- Bins(int, Map<Double, T>) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.Bins
-
Constructs a bin with valued items.
- bins - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
bin allocation; the j(kappa) defined between equations 33 and 34 in
paper 2016
- BisectionRoot - Class in com.numericalmethod.suanshu.analysis.root.univariate
-
The bisection method repeatedly bisects an interval and then selects a subinterval in which a
root must lie for further processing.
- BisectionRoot() - Constructor for class com.numericalmethod.suanshu.analysis.root.univariate.BisectionRoot
-
- BisectionRoot(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.root.univariate.BisectionRoot
-
Create an instance with the tolerance for convergence and the maximum number of iterations.
- BivariateArrayGrid - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
- BivariateArrayGrid(double[][], double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
-
Create a new grid with a given two-dimensional array of grid values, and values for grid line
positions along the x-axis and the y-axis.
- BivariateEVD - Interface in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
Bivariate Extreme Value (BEV) distribution is the joint distribution of component-wise maxima of
two-dimensional iid random vectors.
- BivariateEVDAsymmetricLogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
The bivariate asymmetric logistic model.
- BivariateEVDAsymmetricLogistic(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- BivariateEVDAsymmetricLogistic(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- BivariateEVDAsymmetricLogistic(double, double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- BivariateEVDAsymmetricLogistic(double, double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- BivariateEVDAsymmetricMixed - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
The asymmetric mixed model.
- BivariateEVDAsymmetricMixed(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- BivariateEVDAsymmetricMixed(double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- BivariateEVDAsymmetricMixed(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- BivariateEVDAsymmetricNegativeLogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
The bivariate asymmetric negative logistic model.
- BivariateEVDAsymmetricNegativeLogistic(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- BivariateEVDAsymmetricNegativeLogistic(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- BivariateEVDAsymmetricNegativeLogistic(double, double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- BivariateEVDAsymmetricNegativeLogistic(double, double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- BivariateEVDBilogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
The bilogistic model.
- BivariateEVDBilogistic(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- BivariateEVDBilogistic(double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- BivariateEVDBilogistic(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- BivariateEVDColesTawn - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
The Coles-Tawn model.
- BivariateEVDColesTawn(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- BivariateEVDColesTawn(double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- BivariateEVDColesTawn(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- BivariateEVDHuslerReiss - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
The Husler-Reiss model.
- BivariateEVDHuslerReiss(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- BivariateEVDHuslerReiss(double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- BivariateEVDHuslerReiss(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- BivariateEVDLogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
The bivariate logistic model.
- BivariateEVDLogistic(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- BivariateEVDLogistic(double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- BivariateEVDLogistic(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- BivariateEVDNegativeBilogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
The negative bilogistic model.
- BivariateEVDNegativeBilogistic(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- BivariateEVDNegativeBilogistic(double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- BivariateEVDNegativeBilogistic(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- BivariateEVDNegativeLogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
-
The bivariate negative logistic model.
- BivariateEVDNegativeLogistic(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- BivariateEVDNegativeLogistic(double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- BivariateEVDNegativeLogistic(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- BivariateGrid - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
A rectilinear (meaning that grid lines are not necessarily equally-spaced) bivariate
grid of double values.
- BivariateGridInterpolation - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
A bivariate interpolation, which requires the input to form a rectilinear grid.
- BivariateProbabilityDistribution - Interface in com.numericalmethod.suanshu.stats.distribution.multivariate
-
A bivariate or joint probability distribution for X_1, X_2 is a probability distribution
that gives the probability that each of X_1, X_2, ... falls in any particular range or
discrete set of values specified for that variable.
- BivariateRealFunction - Interface in com.numericalmethod.suanshu.analysis.function.rn2r1
-
A bivariate real function takes two real arguments and outputs one real value.
- BivariateRegularGrid - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
A regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes
(e.g.
- BivariateRegularGrid(double[][], double, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
Create a new grid where the dependent variable values are taken from the given
two-dimensional array and the values of the dependent variables are specified by their first
values and the difference between successive values.
- BlockSplitPointSearch - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Computes the splitting points with the given threshold.
- BlockSplitPointSearch(double, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.BlockSplitPointSearch
-
- BlockWinogradAlgorithm - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
This implementation accelerates matrix multiplication via a combination of the Strassen algorithm
and block matrix multiplication.
- BlockWinogradAlgorithm() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.BlockWinogradAlgorithm
-
- BMSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete
-
A Brownian motion is a stochastic process with the following properties.
- BMSDE(double, double) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.BMSDE
-
Construct a univariate Brownian motion.
- BMSDE() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.BMSDE
-
Construct a univariate standard Brownian motion.
- BoltzAnnealingFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction
-
Matlab: @annealingboltz - The step has length square root of temperature, with direction
uniformly at random.
- BoltzAnnealingFunction(int, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
-
Constructs a new instance where the RVG is created from a given RLG.
- BoltzAnnealingFunction(RandomVectorGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
-
Constructs a new instance that uses a given RVG.
- BOLTZMANN_K - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The Boltzmann constant \(k\) in joule per kelvin (J K-1).
- BoltzTemperatureFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction
-
\(T_k = T_0 / ln(k)\).
- BoltzTemperatureFunction(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.BoltzTemperatureFunction
-
Constructs a new instance with an initial temperature.
- BootstrapEstimator - Class in com.numericalmethod.suanshu.stats.random.sampler.resampler
-
This class estimates the statistic of a sample using a bootstrap method.
- BootstrapEstimator(Resampler, StatisticFactory, int) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.BootstrapEstimator
-
Constructs a bootstrap estimator.
- BootstrapEstimator(Resampler, StatisticFactory, int, boolean) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.BootstrapEstimator
-
Constructs a bootstrap estimator.
- BorderedHessian - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
A bordered Hessian matrix consists of the Hessian of a multivariate function f,
and the gradient of a multivariate function g.
- BorderedHessian(RealScalarFunction, RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.BorderedHessian
-
Construct the bordered Hessian matrix for multivariate functions f and g at
point x.
- BottomUp<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
-
This implementation traverses a directed acyclic graph starting from the leaves at the bottom,
and reaches the roots.
- BottomUp(DAGraph<V, ? extends Arc<V>>) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.BottomUp
-
Constructs a BottomUp traversal instance.
- bound(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Bounds a given value by a given range.
- Bound(int, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
Construct a bound constraint for a variable.
- bounds() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
-
Get a deep copy of the bounds.
- BoxConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
-
This represents the lower and upper bounds for a variable.
- BoxConstraints(int, BoxConstraints.Bound...) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
-
Construct a set of bound constraints.
- BoxConstraints(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
-
Construct a set of bound constraints.
- BoxConstraints.Bound - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
-
A bound constraint for a variable.
- BoxGeneralizedSimulatedAnnealingMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box
-
- BoxGeneralizedSimulatedAnnealingMinimizer(int, double, double, double, StopCondition, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
- BoxGeneralizedSimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
- BoxGeneralizedSimulatedAnnealingMinimizer(int, StopCondition) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
- BoxGSAAcceptanceProbabilityFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
-
This probability function boxes an unconstrained probability function so that when a proposed
state is outside the box, it has a probability of 0.
- BoxGSAAcceptanceProbabilityFunction(Vector, Vector, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.BoxGSAAcceptanceProbabilityFunction
-
Constructs a boxed acceptance probability function.
- BoxGSAAnnealingFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction
-
- BoxGSAAnnealingFunction(Vector, Vector, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.BoxGSAAnnealingFunction
-
Constructs a boxed annealing function.
- BoxMinimizer<P extends BoxOptimProblem,S extends MinimizationSolution<?>> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained
-
- BoxMuller - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.normal
-
The Box-Muller transform (by George Edward Pelham Box and Mervin Edgar Muller 1958)
is a pseudo-random number sampling method for generating pairs of independent standard
normally distributed
(zero expectation, unit variance) random numbers,
given a source of uniformly distributed random numbers.
- BoxMuller(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.BoxMuller
-
Construct a random number generator to sample from the standard Normal distribution.
- BoxMuller() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.BoxMuller
-
Construct a random number generator to sample from the standard Normal distribution.
- BoxOptimProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.problem
-
A box constrained optimization problem, for which a solution must be within fixed bounds.
- BoxOptimProblem(RealScalarFunction, BoxConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
-
Constructs an optimization problem with box constraints.
- BoxOptimProblem(RealScalarFunction, Vector, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
-
Constructs an optimization problem with box constraints.
- BoxOptimProblem(BoxOptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
-
Copy constructor.
- BoxPierce - Class in com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
-
- BoxPierce(double[], int, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
Perform the Box-Pierce test to check auto-correlation in a time series.
- BracketSearchMinimizer - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This class provides implementation support for those univariate optimization algorithms that are based on bracketing.
- BracketSearchMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer
-
Construct a univariate minimizer using a bracket search method.
- BracketSearchMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
- BranchAndBound - Class in com.numericalmethod.suanshu.misc.algorithm.bb
-
Branch-and-Bound (BB or B&B) is a general algorithm for finding optimal solutions of various optimization problems,
especially in discrete and combinatorial optimization.
- BranchAndBound(ActiveList, BBNode) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.bb.BranchAndBound
-
Solve a minimization problem using a branch-and-bound algorithm.
- BranchAndBound(BBNode) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.bb.BranchAndBound
-
Solve a minimization problem using a branch-and-bound algorithm using depth-first search.
- branching() - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.BBNode
-
Get the children of this node by using the branching operation.
- branching() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
Get the children of this node by using the branching operation.
- BrentCetaMaximizer - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer
-
- BrentCetaMaximizer(double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
Constructs a maximizer with a given ε (for the Brent's search
algorithm).
- BrentCetaMaximizer() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
- BrentMinimizer - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
Brent's algorithm is the preferred method for finding the minimum of a univariate function.
- BrentMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BrentMinimizer
-
Construct a univariate minimizer using Brent's algorithm.
- BrentMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This is the solution to a Brent's univariate optimization.
- BrentRoot - Class in com.numericalmethod.suanshu.analysis.root.univariate
-
Brent's root-finding algorithm combines super-linear convergence with reliability of bisection.
- BrentRoot(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.root.univariate.BrentRoot
-
Construct an instance of Brent's root finding algorithm.
- BreuschPagan - Class in com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
-
The Breusch-Pagan test tests for conditional heteroskedasticity.
- BreuschPagan(LMResiduals, boolean) - Constructor for class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.BreuschPagan
-
Perform the Breusch-Pagan test to test for heteroskedasticity in a linear regression model.
- BrownForsythe - Class in com.numericalmethod.suanshu.stats.test.variance
-
The Brown-Forsythe test is a statistical test for the equality of group variances based on performing an ANOVA on a transformation of the response variable.
- BrownForsythe(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
Perform the Brown-Forsythe test to test for equal variances across the groups.
- BruteForce<D,R> - Class in com.numericalmethod.suanshu.misc.algorithm
-
A brute force algorithm, or brute-force search or exhaustive search, also
known as generate and test, is a very general problem-solving technique that
consists of systematically enumerating all possible candidates for the
solution and checking whether each candidate satisfies the problem's
statement.
- BruteForce(Function<D, R>) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.BruteForce
-
Constructs a brute force search for a function.
- BruteForceIPMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce
-
This implementation solves an integral constrained minimization problem by
brute force search for all possible integer combinations.
- BruteForceIPMinimizer(SubProblemMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem, IterativeSolution<Vector>>>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
-
Constructs a brute force minimizer to solve integral constrained
minimization problems.
- BruteForceIPMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
-
Constructs a brute force minimizer to solve integral constrained
minimization problems.
- BruteForceIPMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce
-
This is the solution to an integral constrained minimization using the
brute-force search.
- BruteForceIPProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce
-
This implementation is an integral constrained minimization problem that has enumerable integral domains.
- BruteForceIPProblem(RealScalarFunction, EqualityConstraints, LessThanConstraints, BruteForceIPProblem.IntegerDomain[], double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
-
Construct an integral constrained minimization problem with explicit integral domains.
- BruteForceIPProblem(RealScalarFunction, BruteForceIPProblem.IntegerDomain[], double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
-
Construct an integral constrained minimization problem with explicit integral domains.
- BruteForceIPProblem.IntegerDomain - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce
-
This specifies the integral domain for an integral variable,
i.e., the integer values the variable can take.
- BruteForceMinimizer<R extends Comparable<R>> - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained
-
This implementation solves an unconstrained minimization problem by
brute force search for all given possible values.
- BruteForceMinimizer(boolean) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.BruteForceMinimizer
-
- BruteForceMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained
-
This is the solution to solving an optimization using the brute force
algorithm.
- Bt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration
-
This is a FiltrationFunction that returns \(B(t_i)\),
the Brownian motion value at the i-th time point.
- Bt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Bt
-
- Bt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Get the entire Brownian path.
- build(double) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.Kagi
-
Makes a KAGI construction for the given random process.
- build() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011Solver.Builder
-
- Builder(Dai2011HMM, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011Solver.Builder
-
- BurlischStoerExtrapolation - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation
-
Burlisch-Stoer extrapolation (or Gragg-Bulirsch-Stoer (GBS)) algorithm combines three powerful
ideas: Richardson extrapolation, the use of rational function extrapolation in Richardson-type
applications, and the modified midpoint method, to obtain numerical solutions to ordinary
differential equations (ODEs) with high accuracy and comparatively little computational effort.
- BurlischStoerExtrapolation(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation.BurlischStoerExtrapolation
-
Create an instance of the algorithm with the precision parameter and the maximum number of
iterations allowed.
- BurnInRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
-
A burn-in random number generator discards the first M samples.
- BurnInRNG(RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.BurnInRNG
-
Construct a burn-in RNG.
- BurnInRVG - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate
-
A burn-in random number generator discards the first M samples.
- BurnInRVG(RandomVectorGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.BurnInRVG
-
Construct a burn-in RVG.
- c() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets the value of c.
- c() - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Get c.
- C() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Gets C as in eq.
- C() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Gets C.
- C() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
-
Gets C.
- c(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets ci.
- c() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- c() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets c.
- c() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the objective function.
- c() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- c() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- c - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
n=c/p
- c() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- c(int) - Method in class com.numericalmethod.suanshu.stats.hmm.ForwardBackwardProcedure
-
- C0() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
-
Get the covariance matrix of x0.
- C0() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the variance of x0.
- c1() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the coefficient c1 in the mixed boundary condition
at the boundary x = 0.
- c1() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
-
Gets the coefficient c1 in the mixed boundary condition at the boundary
x = 0.
- C1 - Interface in com.numericalmethod.suanshu.analysis.differentiation.differentiability
-
A function, f, is said to be of class C1 if the derivative f' exists.
- c2() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the coefficient c2 in the mixed boundary condition
at the boundary x = a.
- c2() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
-
Gets the coefficient c2 in the mixed boundary condition at the boundary
x = a.
- C2 - Interface in com.numericalmethod.suanshu.analysis.differentiation.differentiability
-
A function, f, is said to be of class C2 if the first and second derivatives, f' and f'', exist.
- C2OptimProblem - Interface in com.numericalmethod.suanshu.optimization.problem
-
This is an optimization problem of a real valued function that is twice differentiable.
- C2OptimProblemImpl - Class in com.numericalmethod.suanshu.optimization.problem
-
This is an optimization problem of a real valued function: \(\max_x f(x)\).
- C2OptimProblemImpl(RealScalarFunction, RealVectorFunction, RntoMatrix) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
Construct an optimization problem with an objective function.
- C2OptimProblemImpl(RealScalarFunction, RealVectorFunction) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
Construct an optimization problem with an objective function.
- C2OptimProblemImpl(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
Construct an optimization problem with an objective function.
- C2OptimProblemImpl(C2OptimProblemImpl) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
Copy Ctor.
- CalibrationParam(double, double, double, double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.CalibrationParam
-
- CartesianProduct<T> - Class in com.numericalmethod.suanshu.misc.algorithm
-
The Cartesian product can be generalized to the n-ary Cartesian product over
n sets X1, ..., Xn.
- CartesianProduct(T[]...) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.CartesianProduct
-
Construct an
Iterable of all combinations of arrays, taking one
element from each array.
- CaseResamplingReplacement - Class in com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap
-
This is the classical bootstrap method described in the reference.
- CaseResamplingReplacement(double[]) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacement(double[], RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacement(double[], ConcurrentCachedRLG) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacementForObject<X> - Class in com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap
-
This is the classical bootstrap method described in the reference.
- CaseResamplingReplacementForObject(X[], Class<X>) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacementForObject(X[], Class<X>, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacementForObject(X[], Class<X>, ConcurrentCachedRLG) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
Constructs a bootstrap sample generator.
- CauchyPolynomial - Class in com.numericalmethod.suanshu.analysis.function.polynomial
-
The Cauchy's polynomial of a polynomial takes this form:
- CauchyPolynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.CauchyPolynomial
-
- cbind(Vector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of vectors by columns.
- cbind(SparseVector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of sparse vectors by columns and returns a CSR sparse matrix.
- cbind(List<Vector>) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines a list of vectors by columns.
- cbind(Matrix...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of matrices by columns.
- cbind(SparseMatrix...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of sparse matrices by columns.
- ccdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- ccdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
The complementary cumulative distribution function.
- cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.AbstractBivariateProbabilityDistribution
-
- cdf(double, double) - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.BivariateProbabilityDistribution
-
The joint distribution function \(F_{X_1,X_2}(x_1,x_2) = Pr(X_1 \le x_1, X_2 \le x_2)\).
- cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution
-
- cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution
-
- cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution
-
- cdf(Vector) - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the cumulative probability F(x) = Pr(X ≤ x).
- cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
Gets the cumulative probability F(x) = Pr(X ≤ x).
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- cdf(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Gets the cumulative probability F(x) = Pr(X ≤ x).
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
Gets the cumulative probability F(x) = Pr(X ≤ x).
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
The cumulative distribution function.
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
The cumulative distribution function.
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- centralMoment(int) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Get the value of the k-th central moment.
- CentralPath - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing
-
A central path is a solution to both the primal and dual problems of a semi-definite programming problem.
- CentralPath(Matrix, Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
-
Construct a central path.
- Ceta - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta
-
The function C(η) to be maximized (Eq.
- Ceta(Matrix, double, Ceta.PortfolioMomentsEstimator) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.Ceta
-
- Ceta.PortfolioMoments - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta
-
- Ceta.PortfolioMomentsEstimator - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta
-
- CetaMaximizer - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer
-
Defines an algorithm to search for the maximal C(η).
- CetaMaximizer.NegCetaFunction - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer
-
- CetaMaximizer.Solution - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer
-
- ChangeOfVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
Change of variable can easy the computation of some integrals, such as improper integrals.
- ChangeOfVariable(SubstitutionRule, Integrator) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
-
Construct an integrator that uses change of variable to do integration.
- CHARACTERISTIC_IMPEDANCE_Z0 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The characteristic impedance of vacuum \(Z_0\) in ohms (Ω).
- CharacteristicPolynomial - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
-
The characteristic polynomial of a square matrix is the function
- CharacteristicPolynomial(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
Construct the characteristic polynomial for a square matrix.
- ChebyshevRule - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule
-
- ChebyshevRule(int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
Create a Chebyshev rule of the given order.
- checkInputs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Checks whether this LMProblem instance is valid.
- checkInputs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticProblem
-
- Cheng1978 - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.beta
-
Cheng, 1978, is a new rejection method for generating beta variates.
- Cheng1978(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.Cheng1978
-
Construct a random number generator to sample from the beta distribution.
- Cheng1978(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.Cheng1978
-
Construct a random number generator to sample from the beta distribution.
- children(V) - Method in interface com.numericalmethod.suanshu.graph.DiGraph
-
Gets the set of all children of this vertex.
- children(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseDiGraph
-
- children(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- children(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- ChiSquareDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The Chi-square distribution is the distribution of
the sum of the squares of a set of statistically independent standard Gaussian random variables.
- ChiSquareDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
Construct a Chi-Square distribution.
- ChiSquareIndependenceTest - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
Pearson's chi-square test of independence assesses whether paired observations on two variables,
expressed in a contingency table, are independent of each other.
- ChiSquareIndependenceTest(Matrix, int, ChiSquareIndependenceTest.Type) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
-
Assess whether the two random variables in the contingency table are independent.
- ChiSquareIndependenceTest(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
-
Assess whether the two random variables in the contingency table are independent.
- ChiSquareIndependenceTest.Type - Enum in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
the available distributions used for the test
- Chol - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
Cholesky decomposition decomposes a real, symmetric (hence square), and
positive definite matrix A into A = L * Lt, where
L is a lower triangular matrix.
- Chol(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
-
Run the Cholesky decomposition on a real, symmetric (hence square), and
positive definite matrix.
- Chol(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
-
Run the Cholesky decomposition on a real, symmetric (hence square), and
positive definite matrix.
- Chol(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
-
Run the Cholesky decomposition on a real, symmetric (hence square), and
positive definite matrix.
- Cholesky - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix
A into A = L * Lt, where L is a lower triangular matrix.
- CholeskyBanachiewicz - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix
A into A = L * Lt, where L is a lower triangular matrix.
- CholeskyBanachiewicz(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewicz
-
Runs the Cholesky decomposition on a real, symmetric (hence square), and positive definite
matrix.
- CholeskyBanachiewiczParallelized - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
- CholeskyBanachiewiczParallelized(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized
-
- CholeskySparse - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix
A into A = L * Lt, where L is a lower triangular matrix.
- CholeskySparse(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskySparse
-
Runs the Cholesky decomposition on a real, symmetric (hence square), and positive definite
matrix.
- CholeskyWang2006 - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
Cholesky decomposition works only for a positive definite matrix.
- CholeskyWang2006(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyWang2006
-
Constructs the Cholesky decomposition of a matrix.
- Chromosome - Interface in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm
-
A chromosome is a representation of a solution to an optimization problem.
- clamped() - Static method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicSpline
-
Creates an instance with end conditions which fits clamped
splines, and the first derivative at both ends are zero.
- clamped(double, double) - Static method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicSpline
-
Creates an instance with end conditions which fits clamped
splines, meaning that the first derivative at both ends equal to the
given values.
- clear() - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.ActiveList
-
Removes all of the elements from this collection.
- clear() - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- Cluster(int, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.cluster.Clusters.Cluster
-
Create a cluster with the beginning and ending indices of the cluster.
- ClusterAnalyzer - Class in com.numericalmethod.suanshu.stats.evt.cluster
-
This class counts clusters of exceedances based on observations above a given threshold, and the
discontinuity of exceedances can be tolerated by an interval length r.
- ClusterAnalyzer(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.cluster.ClusterAnalyzer
-
Create an instance with the given threshold value and default interval length value of 1.
- ClusterAnalyzer(double, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.cluster.ClusterAnalyzer
-
Create an instance with the given threshold and clustering interval length.
- clusters() - Method in class com.numericalmethod.suanshu.graph.community.GirvanNewman
-
Gets all the clusters, each of which is connected.
- Clusters - Class in com.numericalmethod.suanshu.stats.evt.cluster
-
Store cluster information obtained by cluster analysis.
- Clusters(double[], List<Clusters.Cluster>, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.cluster.Clusters
-
- Clusters.Cluster - Class in com.numericalmethod.suanshu.stats.evt.cluster
-
Define the beginning and ending indices (inclusively) of a cluster.
- Coefficients(ConvectionDiffusionEquation1D, int, int, double[]) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D.Coefficients
-
Constructs the coefficient computation
- Coefficients(HeatEquation1D, int, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
-
Constructs the coefficient computation
- CointegrationMLE - Class in com.numericalmethod.suanshu.stats.cointegration
-
Two or more time series are cointegrated if they each share a common type of
stochastic drift, that is, to a limited degree they share a certain type of
behavior in terms of their long-term fluctuations, but they do not
necessarily move together and may be otherwise unrelated.
- CointegrationMLE(MultivariateSimpleTimeSeries, boolean, int, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Perform the Johansen MLE procedure on a multivariate time series.
- CointegrationMLE(MultivariateSimpleTimeSeries, boolean, int) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Perform the Johansen MLE procedure on a multivariate time series, using
the EIGEN test.
- CointegrationMLE(MultivariateSimpleTimeSeries, boolean) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Perform the Johansen MLE procedure on a multivariate time series,
using the EIGEN test, with the number of lags = 2.
- collection2DoubleArray(Collection<? extends Number>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a collection of numbers to a double array.
- collection2IntArray(Collection<Integer>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a collection of Integers to an int array.
- collection2LongArray(Collection<Long>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a collection of Longs to a long array.
- colMeans(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the column means.
- colMeanVector(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the column mean vector of a given matrix.
- color - Variable in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node
-
- color() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node
-
Gets the color of this node.
- colSums(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the column sums.
- colSumVector(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the column sum vector of a given matrix.
- ColumnBindMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
A fast "cbind" matrix from vectors.
- ColumnBindMatrix(Vector...) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- columnIndices() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
-
- columns(Matrix, int[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the columns of a matrix.
- columns(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the columns of a matrix.
- com.numericalmethod.suanshu.algebra.linear.matrix - package com.numericalmethod.suanshu.algebra.linear.matrix
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3 - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder
-
- com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite
-
- com.numericalmethod.suanshu.algebra.linear.matrix.generic - package com.numericalmethod.suanshu.algebra.linear.matrix.generic
-
- com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype - package com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype
-
- com.numericalmethod.suanshu.algebra.linear.vector - package com.numericalmethod.suanshu.algebra.linear.vector
-
- com.numericalmethod.suanshu.algebra.linear.vector.doubles - package com.numericalmethod.suanshu.algebra.linear.vector.doubles
-
- com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense - package com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense
-
- com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation - package com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation
-
- com.numericalmethod.suanshu.algebra.structure - package com.numericalmethod.suanshu.algebra.structure
-
- com.numericalmethod.suanshu.analysis.curvefit - package com.numericalmethod.suanshu.analysis.curvefit
-
- com.numericalmethod.suanshu.analysis.curvefit.interpolation - package com.numericalmethod.suanshu.analysis.curvefit.interpolation
-
- com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate - package com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
- com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate - package com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
-
- com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate - package com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
-
- com.numericalmethod.suanshu.analysis.differentialequation - package com.numericalmethod.suanshu.analysis.differentialequation
-
- com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem - package com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem
-
- com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver - package com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver
-
- com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation - package com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation
-
- com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton - package com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
- com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta - package com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
- com.numericalmethod.suanshu.analysis.differentialequation.pde - package com.numericalmethod.suanshu.analysis.differentialequation.pde
-
- com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference
-
- com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2 - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2
-
- com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1 - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1
-
- com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2 - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2
-
- com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
-
- com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
-
- com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2 - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2
-
- com.numericalmethod.suanshu.analysis.differentiation - package com.numericalmethod.suanshu.analysis.differentiation
-
- com.numericalmethod.suanshu.analysis.differentiation.differentiability - package com.numericalmethod.suanshu.analysis.differentiation.differentiability
-
- com.numericalmethod.suanshu.analysis.differentiation.multivariate - package com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
- com.numericalmethod.suanshu.analysis.differentiation.univariate - package com.numericalmethod.suanshu.analysis.differentiation.univariate
-
- com.numericalmethod.suanshu.analysis.function - package com.numericalmethod.suanshu.analysis.function
-
- com.numericalmethod.suanshu.analysis.function.matrix - package com.numericalmethod.suanshu.analysis.function.matrix
-
- com.numericalmethod.suanshu.analysis.function.polynomial - package com.numericalmethod.suanshu.analysis.function.polynomial
-
- com.numericalmethod.suanshu.analysis.function.polynomial.root - package com.numericalmethod.suanshu.analysis.function.polynomial.root
-
- com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub - package com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub
-
- com.numericalmethod.suanshu.analysis.function.rn2r1 - package com.numericalmethod.suanshu.analysis.function.rn2r1
-
- com.numericalmethod.suanshu.analysis.function.rn2r1.univariate - package com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
- com.numericalmethod.suanshu.analysis.function.rn2rm - package com.numericalmethod.suanshu.analysis.function.rn2rm
-
- com.numericalmethod.suanshu.analysis.function.special - package com.numericalmethod.suanshu.analysis.function.special
-
- com.numericalmethod.suanshu.analysis.function.special.beta - package com.numericalmethod.suanshu.analysis.function.special.beta
-
- com.numericalmethod.suanshu.analysis.function.special.gamma - package com.numericalmethod.suanshu.analysis.function.special.gamma
-
- com.numericalmethod.suanshu.analysis.function.special.gaussian - package com.numericalmethod.suanshu.analysis.function.special.gaussian
-
- com.numericalmethod.suanshu.analysis.function.tuple - package com.numericalmethod.suanshu.analysis.function.tuple
-
- com.numericalmethod.suanshu.analysis.integration.univariate - package com.numericalmethod.suanshu.analysis.integration.univariate
-
- com.numericalmethod.suanshu.analysis.integration.univariate.riemann - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
- com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian
-
- com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule
-
- com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes
-
- com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
- com.numericalmethod.suanshu.analysis.root.multivariate - package com.numericalmethod.suanshu.analysis.root.multivariate
-
- com.numericalmethod.suanshu.analysis.root.univariate - package com.numericalmethod.suanshu.analysis.root.univariate
-
- com.numericalmethod.suanshu.analysis.sequence - package com.numericalmethod.suanshu.analysis.sequence
-
- com.numericalmethod.suanshu.combinatorics - package com.numericalmethod.suanshu.combinatorics
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- com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles - package com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
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- com.numericalmethod.suanshu.finance.trading.infantino2010 - package com.numericalmethod.suanshu.finance.trading.infantino2010
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- com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration - package com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
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- com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.check - package com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.check
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- com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008 - package com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008
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- com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005 - package com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005
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- com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility - package com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility
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- com.numericalmethod.suanshu.finance.trading.meanreversion.volarb - package com.numericalmethod.suanshu.finance.trading.meanreversion.volarb
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005 - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010 - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.nmsaam - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.nmsaam
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints
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- com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar - package com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar
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- com.numericalmethod.suanshu.finance.trading.returns - package com.numericalmethod.suanshu.finance.trading.returns
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- com.numericalmethod.suanshu.finance.trading.returns.moments - package com.numericalmethod.suanshu.finance.trading.returns.moments
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- com.numericalmethod.suanshu.finance.trading.trend.dai2011 - package com.numericalmethod.suanshu.finance.trading.trend.dai2011
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- com.numericalmethod.suanshu.finance.trading.trend.kst1995 - package com.numericalmethod.suanshu.finance.trading.trend.kst1995
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- com.numericalmethod.suanshu.geometry - package com.numericalmethod.suanshu.geometry
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- com.numericalmethod.suanshu.geometry.polyline - package com.numericalmethod.suanshu.geometry.polyline
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- com.numericalmethod.suanshu.graph - package com.numericalmethod.suanshu.graph
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- com.numericalmethod.suanshu.graph.algorithm.shortestpath - package com.numericalmethod.suanshu.graph.algorithm.shortestpath
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- com.numericalmethod.suanshu.graph.algorithm.traversal - package com.numericalmethod.suanshu.graph.algorithm.traversal
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- com.numericalmethod.suanshu.graph.community - package com.numericalmethod.suanshu.graph.community
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- com.numericalmethod.suanshu.graph.type - package com.numericalmethod.suanshu.graph.type
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- com.numericalmethod.suanshu.interval - package com.numericalmethod.suanshu.interval
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- com.numericalmethod.suanshu.misc - package com.numericalmethod.suanshu.misc
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- com.numericalmethod.suanshu.misc.algorithm - package com.numericalmethod.suanshu.misc.algorithm
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- com.numericalmethod.suanshu.misc.algorithm.bb - package com.numericalmethod.suanshu.misc.algorithm.bb
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- com.numericalmethod.suanshu.misc.algorithm.iterative - package com.numericalmethod.suanshu.misc.algorithm.iterative
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- com.numericalmethod.suanshu.misc.algorithm.iterative.monitor - package com.numericalmethod.suanshu.misc.algorithm.iterative.monitor
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- com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance - package com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance
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- com.numericalmethod.suanshu.misc.algorithm.stopcondition - package com.numericalmethod.suanshu.misc.algorithm.stopcondition
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- com.numericalmethod.suanshu.misc.datastructure - package com.numericalmethod.suanshu.misc.datastructure
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- com.numericalmethod.suanshu.misc.datastructure.time - package com.numericalmethod.suanshu.misc.datastructure.time
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- com.numericalmethod.suanshu.misc.license - package com.numericalmethod.suanshu.misc.license
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- com.numericalmethod.suanshu.misc.parallel - package com.numericalmethod.suanshu.misc.parallel
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- com.numericalmethod.suanshu.number - package com.numericalmethod.suanshu.number
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- com.numericalmethod.suanshu.number.big - package com.numericalmethod.suanshu.number.big
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- com.numericalmethod.suanshu.number.complex - package com.numericalmethod.suanshu.number.complex
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- com.numericalmethod.suanshu.number.doublearray - package com.numericalmethod.suanshu.number.doublearray
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- com.numericalmethod.suanshu.optimization - package com.numericalmethod.suanshu.optimization
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- com.numericalmethod.suanshu.optimization.multivariate.constrained - package com.numericalmethod.suanshu.optimization.multivariate.constrained
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint - package com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general - package com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear - package com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.socp - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.socp
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box - package com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod - package com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset - package com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint - package com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.integer - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem
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- com.numericalmethod.suanshu.optimization.multivariate.constrained.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.problem
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- com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm
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- com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
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- com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained
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- com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local
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- com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid
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- com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
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- com.numericalmethod.suanshu.optimization.multivariate.initialization - package com.numericalmethod.suanshu.optimization.multivariate.initialization
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- com.numericalmethod.suanshu.optimization.multivariate.minmax - package com.numericalmethod.suanshu.optimization.multivariate.minmax
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
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- com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
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- com.numericalmethod.suanshu.optimization.problem - package com.numericalmethod.suanshu.optimization.problem
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- com.numericalmethod.suanshu.optimization.univariate - package com.numericalmethod.suanshu.optimization.univariate
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- com.numericalmethod.suanshu.optimization.univariate.bracketsearch - package com.numericalmethod.suanshu.optimization.univariate.bracketsearch
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- com.numericalmethod.suanshu.stats.cointegration - package com.numericalmethod.suanshu.stats.cointegration
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- com.numericalmethod.suanshu.stats.covariance.covarianceselection - package com.numericalmethod.suanshu.stats.covariance.covarianceselection
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- com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso - package com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso
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- com.numericalmethod.suanshu.stats.covariance.nlshrink - package com.numericalmethod.suanshu.stats.covariance.nlshrink
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- com.numericalmethod.suanshu.stats.covariance.nlshrink.quest - package com.numericalmethod.suanshu.stats.covariance.nlshrink.quest
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- com.numericalmethod.suanshu.stats.descriptive - package com.numericalmethod.suanshu.stats.descriptive
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- com.numericalmethod.suanshu.stats.descriptive.correlation - package com.numericalmethod.suanshu.stats.descriptive.correlation
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- com.numericalmethod.suanshu.stats.descriptive.covariance - package com.numericalmethod.suanshu.stats.descriptive.covariance
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- com.numericalmethod.suanshu.stats.descriptive.moment - package com.numericalmethod.suanshu.stats.descriptive.moment
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- com.numericalmethod.suanshu.stats.descriptive.moment.weighted - package com.numericalmethod.suanshu.stats.descriptive.moment.weighted
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- com.numericalmethod.suanshu.stats.descriptive.rank - package com.numericalmethod.suanshu.stats.descriptive.rank
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- com.numericalmethod.suanshu.stats.distribution.discrete - package com.numericalmethod.suanshu.stats.distribution.discrete
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- com.numericalmethod.suanshu.stats.distribution.multivariate - package com.numericalmethod.suanshu.stats.distribution.multivariate
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- com.numericalmethod.suanshu.stats.distribution.multivariate.exponentialfamily - package com.numericalmethod.suanshu.stats.distribution.multivariate.exponentialfamily
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- com.numericalmethod.suanshu.stats.distribution.univariate - package com.numericalmethod.suanshu.stats.distribution.univariate
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- com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily - package com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily
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- com.numericalmethod.suanshu.stats.dlm.multivariate - package com.numericalmethod.suanshu.stats.dlm.multivariate
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- com.numericalmethod.suanshu.stats.dlm.univariate - package com.numericalmethod.suanshu.stats.dlm.univariate
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- com.numericalmethod.suanshu.stats.evt.cluster - package com.numericalmethod.suanshu.stats.evt.cluster
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- com.numericalmethod.suanshu.stats.evt.evd.bivariate - package com.numericalmethod.suanshu.stats.evt.evd.bivariate
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- com.numericalmethod.suanshu.stats.evt.evd.univariate - package com.numericalmethod.suanshu.stats.evt.evd.univariate
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- com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting - package com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting
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- com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer - package com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
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- com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical - package com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical
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- com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot - package com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot
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- com.numericalmethod.suanshu.stats.evt.evd.univariate.rng - package com.numericalmethod.suanshu.stats.evt.evd.univariate.rng
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- com.numericalmethod.suanshu.stats.evt.exi - package com.numericalmethod.suanshu.stats.evt.exi
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- com.numericalmethod.suanshu.stats.evt.function - package com.numericalmethod.suanshu.stats.evt.function
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- com.numericalmethod.suanshu.stats.evt.markovchain - package com.numericalmethod.suanshu.stats.evt.markovchain
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- com.numericalmethod.suanshu.stats.evt.timeseries - package com.numericalmethod.suanshu.stats.evt.timeseries
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- com.numericalmethod.suanshu.stats.factor.factoranalysis - package com.numericalmethod.suanshu.stats.factor.factoranalysis
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- com.numericalmethod.suanshu.stats.factor.implicitmodelpca - package com.numericalmethod.suanshu.stats.factor.implicitmodelpca
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- com.numericalmethod.suanshu.stats.factor.pca - package com.numericalmethod.suanshu.stats.factor.pca
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- com.numericalmethod.suanshu.stats.hmm - package com.numericalmethod.suanshu.stats.hmm
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- com.numericalmethod.suanshu.stats.hmm.discrete - package com.numericalmethod.suanshu.stats.hmm.discrete
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- com.numericalmethod.suanshu.stats.hmm.mixture - package com.numericalmethod.suanshu.stats.hmm.mixture
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- com.numericalmethod.suanshu.stats.hmm.mixture.distribution - package com.numericalmethod.suanshu.stats.hmm.mixture.distribution
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- com.numericalmethod.suanshu.stats.markovchain - package com.numericalmethod.suanshu.stats.markovchain
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- com.numericalmethod.suanshu.stats.random - package com.numericalmethod.suanshu.stats.random
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- com.numericalmethod.suanshu.stats.random.rng - package com.numericalmethod.suanshu.stats.random.rng
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- com.numericalmethod.suanshu.stats.random.rng.concurrent.cache - package com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
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- com.numericalmethod.suanshu.stats.random.rng.concurrent.context - package com.numericalmethod.suanshu.stats.random.rng.concurrent.context
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- com.numericalmethod.suanshu.stats.random.rng.multivariate - package com.numericalmethod.suanshu.stats.random.rng.multivariate
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- com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid - package com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid
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- com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis - package com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis
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- com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction - package com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction
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- com.numericalmethod.suanshu.stats.random.rng.univariate - package com.numericalmethod.suanshu.stats.random.rng.univariate
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- com.numericalmethod.suanshu.stats.random.rng.univariate.beta - package com.numericalmethod.suanshu.stats.random.rng.univariate.beta
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- com.numericalmethod.suanshu.stats.random.rng.univariate.exp - package com.numericalmethod.suanshu.stats.random.rng.univariate.exp
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- com.numericalmethod.suanshu.stats.random.rng.univariate.gamma - package com.numericalmethod.suanshu.stats.random.rng.univariate.gamma
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- com.numericalmethod.suanshu.stats.random.rng.univariate.normal - package com.numericalmethod.suanshu.stats.random.rng.univariate.normal
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- com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated - package com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated
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- com.numericalmethod.suanshu.stats.random.rng.univariate.poisson - package com.numericalmethod.suanshu.stats.random.rng.univariate.poisson
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- com.numericalmethod.suanshu.stats.random.rng.univariate.uniform - package com.numericalmethod.suanshu.stats.random.rng.univariate.uniform
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- com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear - package com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear
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- com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister - package com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister
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- com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation - package com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation
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- com.numericalmethod.suanshu.stats.random.sampler.resampler - package com.numericalmethod.suanshu.stats.random.sampler.resampler
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- com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap - package com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap
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- com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block - package com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block
-
- com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate - package com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate
-
- com.numericalmethod.suanshu.stats.random.variancereduction - package com.numericalmethod.suanshu.stats.random.variancereduction
-
- com.numericalmethod.suanshu.stats.regression - package com.numericalmethod.suanshu.stats.regression
-
- com.numericalmethod.suanshu.stats.regression.linear - package com.numericalmethod.suanshu.stats.regression.linear
-
- com.numericalmethod.suanshu.stats.regression.linear.glm - package com.numericalmethod.suanshu.stats.regression.linear.glm
-
- com.numericalmethod.suanshu.stats.regression.linear.glm.distribution - package com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
- com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link - package com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
- com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection - package com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
- com.numericalmethod.suanshu.stats.regression.linear.glm.quasi - package com.numericalmethod.suanshu.stats.regression.linear.glm.quasi
-
- com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family - package com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
-
- com.numericalmethod.suanshu.stats.regression.linear.lasso - package com.numericalmethod.suanshu.stats.regression.linear.lasso
-
- com.numericalmethod.suanshu.stats.regression.linear.lasso.lars - package com.numericalmethod.suanshu.stats.regression.linear.lasso.lars
-
- com.numericalmethod.suanshu.stats.regression.linear.logistic - package com.numericalmethod.suanshu.stats.regression.linear.logistic
-
- com.numericalmethod.suanshu.stats.regression.linear.ols - package com.numericalmethod.suanshu.stats.regression.linear.ols
-
- com.numericalmethod.suanshu.stats.regression.linear.panel - package com.numericalmethod.suanshu.stats.regression.linear.panel
-
- com.numericalmethod.suanshu.stats.regression.linear.residualanalysis - package com.numericalmethod.suanshu.stats.regression.linear.residualanalysis
-
- com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
-
- com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
- com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
- com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete
-
- com.numericalmethod.suanshu.stats.stochasticprocess.timegrid - package com.numericalmethod.suanshu.stats.stochasticprocess.timegrid
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process
-
- com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou
-
- com.numericalmethod.suanshu.stats.test - package com.numericalmethod.suanshu.stats.test
-
- com.numericalmethod.suanshu.stats.test.distribution - package com.numericalmethod.suanshu.stats.test.distribution
-
- com.numericalmethod.suanshu.stats.test.distribution.kolmogorov - package com.numericalmethod.suanshu.stats.test.distribution.kolmogorov
-
- com.numericalmethod.suanshu.stats.test.distribution.normality - package com.numericalmethod.suanshu.stats.test.distribution.normality
-
- com.numericalmethod.suanshu.stats.test.distribution.pearson - package com.numericalmethod.suanshu.stats.test.distribution.pearson
-
- com.numericalmethod.suanshu.stats.test.mean - package com.numericalmethod.suanshu.stats.test.mean
-
- com.numericalmethod.suanshu.stats.test.rank - package com.numericalmethod.suanshu.stats.test.rank
-
- com.numericalmethod.suanshu.stats.test.rank.wilcoxon - package com.numericalmethod.suanshu.stats.test.rank.wilcoxon
-
- com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity - package com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
-
- com.numericalmethod.suanshu.stats.test.timeseries.adf - package com.numericalmethod.suanshu.stats.test.timeseries.adf
-
- com.numericalmethod.suanshu.stats.test.timeseries.adf.table - package com.numericalmethod.suanshu.stats.test.timeseries.adf.table
-
- com.numericalmethod.suanshu.stats.test.timeseries.portmanteau - package com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
-
- com.numericalmethod.suanshu.stats.test.variance - package com.numericalmethod.suanshu.stats.test.variance
-
- com.numericalmethod.suanshu.stats.timeseries.datastructure - package com.numericalmethod.suanshu.stats.timeseries.datastructure
-
- com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate - package com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate
-
- com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime - package com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime
-
- com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime - package com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime
-
- com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate - package com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate
-
- com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime - package com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime
-
- com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime - package com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime
-
- com.numericalmethod.suanshu.stats.timeseries.linear.multivariate - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate
-
- com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima
-
- com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
-
- com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch
-
- com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
- combination(int, int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Compute the combination function or binomial coefficient.
- combination(int, int) - Static method in class com.numericalmethod.suanshu.number.big.BigIntegerUtils
-
Compute the combination function or the binomial coefficient.
- CombinedCetaMaximizer - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer
-
Searches the maximum C(η) by an array of given maximizers, being
tried in sequence.
- CombinedCetaMaximizer(CetaMaximizer[]) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
-
Constructs a combined maximizer.
- CombinedCetaMaximizer() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
-
Constructs a combined maximizer.
- CombinedVectorByRef - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles
-
For efficiency, this wrapper concatenates two or more vectors by references
(without data copying).
- CombinedVectorByRef(Vector, Vector, Vector...) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.CombinedVectorByRef
-
- commission(double) - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011Solver.Builder
-
- commission(double, double) - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011Solver.Builder
-
- CommonRandomNumbers - Class in com.numericalmethod.suanshu.stats.random.variancereduction
-
The common random numbers is a variance reduction technique to apply when we are comparing two
random systems, e.g., \(E(f(X_1) - g(X_2))\).
- CommonRandomNumbers(UnivariateRealFunction, UnivariateRealFunction, RandomLongGenerator, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.CommonRandomNumbers
-
Estimates \(E(f(X_1) - g(X_2))\), where f and g are functions of uniform random
variables.
- CommonRandomNumbers(UnivariateRealFunction, UnivariateRealFunction, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.CommonRandomNumbers
-
Estimates \(E(f(X_1) - g(X_2))\), where f and g are functions of uniform random
variables.
- CommonRandomNumbers(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.CommonRandomNumbers
-
Estimate \(E(f(X_1) - g(X_2))\), where f and g are functions of uniform random
variables.
- compare(SparseMatrix.Entry, SparseMatrix.Entry) - Method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry.TopLeftFirstComparator
-
- compare(SparseVector.Entry, SparseVector.Entry) - Method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry.Comparator
-
- compare(Pair, Pair) - Method in class com.numericalmethod.suanshu.analysis.function.tuple.PairComparatorByAbscissaFirst
-
- compare(Pair, Pair) - Method in class com.numericalmethod.suanshu.analysis.function.tuple.PairComparatorByAbscissaOnly
-
- compare(BigDecimal, BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compare two BigDecimals up to a precision.
- compare(Number, double) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- compare(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Compares two doubles up to a precision.
- compare(Number, double) - Method in interface com.numericalmethod.suanshu.number.NumberUtils.Comparable
-
Compare this and that numbers up to a precision.
- compare(Number, Number, double) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Compare two numbers.
- compareTo(Pair) - Method in class com.numericalmethod.suanshu.analysis.function.tuple.Pair
-
- compareTo(GraphTraversal.Node<V>) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal.Node
-
- compareTo(SortableArray) - Method in class com.numericalmethod.suanshu.misc.datastructure.SortableArray
-
- compareTo(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- compareTo(BoxConstraints.Bound) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
- compareTo(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
- compareTo(PanelData<S>.Row) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData.Row
-
- Complex - Class in com.numericalmethod.suanshu.number.complex
-
A complex number is a number consisting of a real number part and an imaginary number part.
- Complex(double, double) - Constructor for class com.numericalmethod.suanshu.number.complex.Complex
-
Construct a complex number from the real and imaginary parts.
- Complex(double) - Constructor for class com.numericalmethod.suanshu.number.complex.Complex
-
Construct a complex number from a real number.
- ComplexMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype
-
- ComplexMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- ComplexMatrix(Complex[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- ComplexMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- CompositeDoubleArrayOperation - Class in com.numericalmethod.suanshu.number.doublearray
-
It is desirable to have multiple implementations and switch between them for, e.g., performance
reason.
- CompositeDoubleArrayOperation(CompositeDoubleArrayOperation.ImplementationChooser) - Constructor for class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
Construct a CompositeDoubleArrayOperation by supplying the multiplexing criterion and
the multiple DoubleArrayOperations.
- CompositeDoubleArrayOperation(int, DoubleArrayOperation, DoubleArrayOperation) - Constructor for class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
Construct a CompositeDoubleArrayOperation that chooses an implementation by array
length.
- CompositeDoubleArrayOperation.ImplementationChooser - Interface in com.numericalmethod.suanshu.number.doublearray
-
Specify which implementation to use.
- CompositeLinearCongruentialGenerator - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear
-
A composite generator combines a number of simple
LinearCongruentialGenerator, such as
Lehmer,
to form one longer period generator by first summing values and then taking modulus.
- CompositeLinearCongruentialGenerator(LinearCongruentialGenerator[]) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
Construct a linear congruential generator from some simpler and shorter modulus generators.
- compute(Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.BlockSplitPointSearch
-
Searches splitting points in the symmetric tridiagonal matrix.
- compute(Matrix) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion.MatrixNorm
-
- compute() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.AhatEstimation
-
- compute(Matrix) - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004
-
Estimates the covariance matrix for a given matrix Y (each column
in Y is a time-series), with the optimal shrinkage parameter
computed by the algorithm.
- compute(Matrix, double) - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004
-
Estimates the covariance matrix for a given matrix Y (each column
in Y is a time-series), with the given shrinkage parameter.
- compute(double[][], double[][], int) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EpsilonStatisticsCalculator
-
Compute the statistics.
- computeCointegratingBeta(Matrix) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.RobustCointegration
-
- computeGershgorinIntervals(Vector, Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenBoundUtils
-
Computes the Gershgorin bounds for all eigenvalues in a symmetric tridiagonal matrix
T.
- computeOptimalPositions(int) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
-
- computeOptimalPositions(int) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
-
Computes the solution to the problem described in Section 3.2 in reference.
- computeOptimalPositions(int) - Method in interface com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueSolver
-
Computes the solution to the problem described in Section 3.2 in reference.
- computePrice(double, double) - Method in interface com.numericalmethod.suanshu.finance.trading.returns.ReturnsCalculator
-
Computes the next price after a return.
- computePrice(double, double) - Method in enum com.numericalmethod.suanshu.finance.trading.returns.ReturnsCalculators
-
- computeReturn(double, double) - Method in interface com.numericalmethod.suanshu.finance.trading.returns.ReturnsCalculator
-
Computes the portfolio return.
- computeReturn(double, double) - Method in enum com.numericalmethod.suanshu.finance.trading.returns.ReturnsCalculators
-
- computeRobustPair(String, String, Vector, Vector) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.RobustCointegration
-
- computeShortTermCointegratingBeta(Matrix, double) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.RobustCointegration
-
- computeWeightedRSS(ACERFunction.ACERParameter, double[], double[], double[]) - Static method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit
-
Measure how fit the estimated log-ACER function to the empirical epsilons by weighted sum of
squared residuals (RSS).
- computeWeights(double[], double[]) - Static method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit
-
Compute weights from epsilon values and their corresponding confidence interval half-width.
- computeWeightsByPeriodLength(double[][]) - Static method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.ACERUtils
-
Compute the weights for periods, proportional to the lengths of the periods.
- concat(Vector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Concatenates an array of vectors into one vector.
- concat(Collection<Vector>) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Concatenates an array of vectors into one vector.
- concat(SparseVector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Concatenates an array of sparse vectors into one sparse vector.
- concat(double[]...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Concatenate an array of arrays into one array.
- concat(LinearConstraints...) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
Concatenate collections of linear constraints into one collection.
- CONCURRENT_RNORM - Static variable in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
- ConcurrentCachedGenerator<T> - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
-
A generic wrapper that makes an underlying item generator thread-safe by
caching generated items in a concurrently-accessible list.
- ConcurrentCachedGenerator(ConcurrentCachedGenerator.Generator<T>, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedGenerator
-
Creates a new instance which wraps the given item generator and uses a
cache of the specified size.
- ConcurrentCachedGenerator.Generator<T> - Interface in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
-
Defines a generic generator of type T.
- ConcurrentCachedRLG - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
-
This is a fast thread-safe wrapper for random long generators.
- ConcurrentCachedRLG(RandomLongGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRLG
-
Constructs a new instance which wraps the given random long generator and
uses a cache of the specified size.
- ConcurrentCachedRLG(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRLG
-
Construct a new instance which wraps the given random long generator and
uses a cache which has 1000 entries per available core.
- ConcurrentCachedRNG - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
-
This is a fast thread-safe wrapper for random number generators.
- ConcurrentCachedRNG(RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRNG
-
Constructs a new instance which wraps the given random number generator
and uses a cache of the specified size.
- ConcurrentCachedRNG(RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRNG
-
Construct a new instance which wraps the given random number generator
and
uses a cache which has 8 entries per available core.
- ConcurrentCachedRVG - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
-
This is a fast thread-safe wrapper for random vector generators.
- ConcurrentCachedRVG(RandomVectorGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRVG
-
Constructs a new instance which wraps the given random vector generator
and uses a cache of the specified size.
- ConcurrentCachedRVG(RandomVectorGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRVG
-
Constructs a new instance which wraps the given random vector generator
and uses a cache which has 8 entries per available core.
- ConcurrentStandardNormalRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.normal
-
- ConcurrentStandardNormalRNG(RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.ConcurrentStandardNormalRNG
-
- ConcurrentStandardNormalRNG() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.ConcurrentStandardNormalRNG
-
- conditionalCopula(double, double) - Method in interface com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVD
-
The conditional copula function conditioning on either margin.
- conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- conditionalForEach(boolean, Iterable<T>, IterationBody<T>) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Calls
forEach
only if
conditionToParallelize is
true.
- conditionalForLoop(boolean, int, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Runs a parallel for-loop only if conditionToParallelize is true.
- conditionalForLoop(boolean, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
- conditionalMean(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Compute the multivariate ARMA conditional mean, given all the lags.
- conditionalMean(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Compute the univariate ARMA conditional mean, given all the lags.
- conditionalMean(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Compute the univariate AR1 conditional mean, given the last lag.
- ConditionalSumOfSquares - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
The method Conditional Sum of Squares (CSS) fits an ARIMA model by minimizing
the conditional sum of squares.
- ConditionalSumOfSquares(double[], int, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Fit an ARIMA model for the observations using CSS.
- ConditionalSumOfSquares(double[], int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Fit an ARIMA model for the observations using CSS.
- conditionNumber(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Computes the condition number of a given matrix A.
- CONDUCTANCE_QUANTUM_G0 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The conductance quantum \(G_0\) in siemens (s).
- ConfidenceInterval - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting
-
This class stores information for a list of confidence intervals, with the same confidence level.
- ConfidenceInterval(double, Vector, Vector, Vector) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.ConfidenceInterval
-
Create an instance with the confidence interval information.
- confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Compute the \((1 - \alpha)100\%\) confidence intervals for each element of the fitted
parameter, given the required confidence level.
- confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Get the confidence interval.
- confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
Compute the confidence interval.
- CongruentMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
Given a matrix A and an invertible matrix P, we create the congruent matrix
B s.t.,
B = P'AP
- CongruentMatrix(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.CongruentMatrix
-
Constructs the congruent matrix B = P'AP.
- conjugate() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the conjugate of the complex number, namely, (a - bi).
- ConjugateGradientMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection
-
A conjugate direction optimization method is performed by using
sequential line search along directions that bear a strict mathematical relationship to one another.
- ConjugateGradientMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.ConjugateGradientMinimizer
-
Construct a multivariate minimizer using the Conjugate-Gradient method.
- ConjugateGradientNormalErrorSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
For an under-determined system of linear equations, Ax = b, or
when the coefficient matrix A is non-symmetric and nonsingular,
the normal equation matrix AAt is symmetric and
positive definite, and hence CG is applicable.
- ConjugateGradientNormalErrorSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
Construct a Conjugate Gradient Normal Error (CGNE) solver.
- ConjugateGradientNormalErrorSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
Construct a Conjugate Gradient Normal Error (CGNE) solver.
- ConjugateGradientNormalResidualSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
For an under-determined system of linear equations, Ax = b, or
when the coefficient matrix A is non-symmetric and nonsingular,
the normal equation matrix AAt is symmetric and
positive definite, and hence CG is applicable.
- ConjugateGradientNormalResidualSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
Construct a Conjugate Gradient Normal Residual method (CGNR) solver.
- ConjugateGradientNormalResidualSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
Construct a Conjugate Gradient Normal Residual method (CGNR) solver.
- ConjugateGradientSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Conjugate Gradient method (CG) is useful for solving a symmetric n-by-n
linear system.
- ConjugateGradientSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
Construct a Conjugate Gradient (CG) solver.
- ConjugateGradientSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
Construct a Conjugate Gradient (CG) solver.
- ConjugateGradientSquaredSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Conjugate Gradient Squared method (CGS) is useful for solving
a non-symmetric n-by-n linear system.
- ConjugateGradientSquaredSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
Construct a Conjugate Gradient Squared (CGS) solver.
- ConjugateGradientSquaredSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
Construct a Conjugate Gradient Squared (CGS) solver.
- ConstantDriftVector - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
The class represents a constant drift function.
- ConstantDriftVector(Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantDriftVector
-
Construct a constant drift function.
- Constants - Class in com.numericalmethod.suanshu.misc
-
This class lists the global parameters and constants in this SuanShu library.
- ConstantSeeder<T extends Seedable> - Class in com.numericalmethod.suanshu.stats.random.rng
-
A wrapper that seeds each given seedable random number generator with the given seed(s).
- ConstantSeeder(Iterable<T>, long...) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.ConstantSeeder
-
Constructs a wrapper with the underlying RNGs and the seeds for seeding each RNG.
- ConstantSigma1 - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
The class represents a constant diffusion coefficient function.
- ConstantSigma1(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
Construct a constant diffusion coefficient function.
- ConstantSigma2 - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
- ConstantSigma2(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
Construct a constant diffusion coefficient function.
- ConstrainedCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
-
- ConstrainedCellFactory - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
This defines a Differential Evolution operator that takes in account constraints.
- ConstrainedCellFactory(DEOptimCellFactory) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
Construct an instance of a ConstrainedCellFactory that define the constrained Differential Evolution operators.
- ConstrainedCellFactory.ConstrainedCell - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
A ConstrainedCell is a chromosome for a constrained optimization problem.
- ConstrainedLASSObyLARS - Class in com.numericalmethod.suanshu.stats.regression.linear.lasso
-
This class solves the constrained form of LASSO by modified least angle regression (LARS) and
linear interpolation:
\[
\min_w \left \{ \left \| Xw - y \right \|_2^2 \right \}\) subject to \( \left \| w \right \|_1
\leq t
\]
- ConstrainedLASSObyLARS(ConstrainedLASSOProblem, boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyLARS
-
Solves a constrained LASSO problem by modified least angle regression (LARS) and linear
interpolation.
- ConstrainedLASSObyLARS(ConstrainedLASSOProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyLARS
-
Solves a constrained LASSO problem by modified least angle regression (LARS) and linear
interpolation.
- ConstrainedLASSObyQP - Class in com.numericalmethod.suanshu.stats.regression.linear.lasso
-
This class solves the constrained form of LASSO (i.e.\(\min_w \left \{ \left \| Xw - y \right
\|_2^2 \right \}\)
subject to \( \left \| w \right \|_1 \leq t \)) by transforming it into a single quadratic
programming problem with (2 * m + 1) constraints, where m is the number of
columns of the design matrix.
- ConstrainedLASSObyQP(ConstrainedLASSOProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyQP
-
Solves a constrained LASSO problem by transforming it into a single quadratic programming
problem.
- ConstrainedLASSOProblem - Class in com.numericalmethod.suanshu.stats.regression.linear.lasso
-
A LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS
(residual sum of squared errors) problem with L1 regularization.
- ConstrainedLASSOProblem(Vector, Matrix, double) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSOProblem
-
Constructs a LASSO problem in the constrained form.
- ConstrainedLASSOProblem(ConstrainedLASSOProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSOProblem
-
Copy constructor.
- ConstrainedMinimizer<P extends ConstrainedOptimProblem,S extends MinimizationSolution<?>> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained
-
- ConstrainedOptimProblem - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.problem
-
A constrained optimization problem takes this form.
- ConstrainedOptimProblemImpl1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.problem
-
This implements a constrained optimization problem for a function f
subject to equality and less-than-or-equal-to constraints.
- ConstrainedOptimProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
Constructs a constrained optimization problem.
- ConstrainedOptimProblemImpl1(ConstrainedOptimProblemImpl1) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
Copy constructor.
- ConstrainedOptimSubProblem - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained
-
A constrained optimization sub-problem takes this form.
- constraints() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint.MinimumWeights
-
- constraints() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint.NoConstraints
-
- constraints() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint.NoShortSelling
-
- constraints() - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.Corvalan2005.WeightsConstraint
-
Gets the less-than constraints on weights.
- Constraints - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint
-
A set of constraints for a (real-valued) optimization problem is a set of functions.
- constraints - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
the constraint/cost functions
- ConstraintsUtils - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint
-
These are the utility functions for manipulating
Constraints.
- ConstraintsUtils() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.ConstraintsUtils
-
- ConstraintViolationException() - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.ConstraintViolationException
-
Constructs a ConstraintViolationException.
- ConstraintViolationException(String) - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.ConstraintViolationException
-
Constructs a ConstraintViolationException with an error
message.
- containActive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Check if the active set contains a certain index.
- containInactive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Check if the inactive set contains a certain index.
- contains(UndirectedEdge<V>) - Method in class com.numericalmethod.suanshu.graph.community.EdgeBetweeness
-
Checks if the graph contains an edge.
- contains(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
-
Check if this graph contains a vertex.
- contains(Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- containsAll(Collection<?>) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- containsEdge(Graph<V, ?>, HyperEdge<V>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Returns true if this graph's edge collection contains e
- containsVertex(Graph<V, ?>, V) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Returns true if this graph's vertex collection contains v
- ContextRNG<T> - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.context
-
This uniform number generator generates independent sequences of random numbers per context.
- ContextRNG() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ContextRNG
-
- ContinuedFraction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
A continued fraction representation of a number has this form:
\[
z = b_0 + \cfrac{a_1}{b_1 + \cfrac{a_2}{b_2 + \cfrac{a_3}{b_3 + \cfrac{a_4}{b_4 + \ddots\,}}}}
\]
ai and bi can be functions of x, which in turn makes z a function of x.
- ContinuedFraction(ContinuedFraction.Partials, double, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- ContinuedFraction(ContinuedFraction.Partials, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- ContinuedFraction(ContinuedFraction.Partials) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- ContinuedFraction.MaxIterationsExceededException - Exception in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
RuntimeException thrown when the continued fraction fails to converge for a given epsilon before a certain number of iterations.
- ContinuedFraction.Partials - Interface in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
This interface defines a continued fraction in terms of
the partial numerators an, and the partial denominators bn.
- ControlVariates - Class in com.numericalmethod.suanshu.stats.random.variancereduction
-
Control variates method is a variance reduction technique that exploits information about the
errors in estimates of known quantities to reduce the error of an estimate of an unknown
quantity.
- ControlVariates(UnivariateRealFunction, UnivariateRealFunction, double, double, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.ControlVariates
-
- ControlVariates.Estimator - Interface in com.numericalmethod.suanshu.stats.random.variancereduction
-
- ConvectionDiffusionEquation1D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
-
The convection–diffusion equation is a combination of the diffusion and
convection (advection) equations, and describes physical phenomena where
particles, energy, or other physical quantities are transferred inside a
physical system due to two processes: diffusion and convection.
- ConvectionDiffusionEquation1D(BivariateRealFunction, BivariateRealFunction, BivariateRealFunction, double, double, UnivariateRealFunction, double, UnivariateRealFunction, double, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Constructs a convection-diffusion equation problem.
- ConvergenceFailure - Exception in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
-
- ConvergenceFailure(ConvergenceFailure.Reason) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
-
Construct an exception with reason.
- ConvergenceFailure(ConvergenceFailure.Reason, String) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
-
Construct an exception with reason and error message.
- ConvergenceFailure.Reason - Enum in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
-
the reasons for the convergence failure
- convertToDerivativeFunction(RealVectorFunction, int) - Static method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Converts the given vector function to a first order derivative function.
- cookDistances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMDiagnostics
-
Cook distances.
- coordinates - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
the coordinates of this entry
- copy2D(double[][]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Copies a 2D array.
- cor(Matrix, double) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.check.CorrelationCheck
-
- cor(double) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
-
- correlation() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
-
Get the correlation, i.e., Pearson's correlation coefficient.
- CorrelationCheck - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.check
-
- CorrelationCheck(Matrix, double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.check.CorrelationCheck
-
- CorrelationCheck(Matrix, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.check.CorrelationCheck
-
- CorrelationMatrix - Class in com.numericalmethod.suanshu.stats.descriptive.correlation
-
The correlation matrix of n random variables X1, ...,
Xn is the n × n matrix whose i,j entry is
corr(Xi, Xj), the correlation between
X1 and Xn.
- CorrelationMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.correlation.CorrelationMatrix
-
Construct a correlation matrix from a covariance matrix.
- Corvalan2005 - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005
-
This paper tackles the corner solution problem of many portfolio optimizers, by
optimizing the portfolio diversification with some relaxation on the volatility σ
and the expected return R of a given optimized (but non-diversified) portfolio.
- Corvalan2005(double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.Corvalan2005
-
Constructs an instance of the Corvalan model.
- Corvalan2005(Minimizer<? super ConstrainedOptimProblem, IterativeSolution<Vector>>, DiversificationMeasure, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.Corvalan2005
-
Constructs an instance of the Corvalan model.
- Corvalan2005.WeightsConstraint - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005
-
Constraints on weights which are defined by a set of less-than constraints.
- cos(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the cosine of a vector, element-by-element.
- cos(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Cosine of a complex number.
- cosec(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the cosecant of an angle.
- cosh(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Hyperbolic cosine of a complex number.
- cost() - Method in class com.numericalmethod.suanshu.graph.type.SimpleArc
-
- cost() - Method in class com.numericalmethod.suanshu.graph.type.SimpleEdge
-
- cost() - Method in interface com.numericalmethod.suanshu.graph.WeightedEdge
-
Gets the cost or weight of this edge.
- COST - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- cot(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the cotangent of an angle.
- coth(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the hyperbolic cotangent of a hyperbolic angle.
- count(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCount
-
Counts the number of eigenvalues that are less than a given value x.
- count(double, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCountInRange
-
Counts the number of eigenvalues of T that are in the given interval.
- count(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SturmCount
-
Computes the Sturm count.
- count(double) - Method in class com.numericalmethod.suanshu.combinatorics.Counter
-
Get the count, i.e., the number of occurrences, of a particular number.
- countEntriesInEachColumn(List<SparseMatrix.Entry>, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Counts the number of entries in each column.
- countEntriesInEachRow(List<SparseMatrix.Entry>, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Counts the number of entries in each row.
- Counter - Class in com.numericalmethod.suanshu.combinatorics
-
A counter keeps track of the number of occurrences of numbers.
- Counter() - Constructor for class com.numericalmethod.suanshu.combinatorics.Counter
-
Construct a counter with no rounding.
- Counter(int) - Constructor for class com.numericalmethod.suanshu.combinatorics.Counter
-
Construct a counter.
- CountMonitor<S> - Class in com.numericalmethod.suanshu.misc.algorithm.iterative.monitor
-
This
IterationMonitor counts the number of iterates generated, hence
the number of iterations.
- CountMonitor() - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.CountMonitor
-
- CourantPenalty - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the squared error penalties.
- CourantPenalty(EqualityConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.CourantPenalty
-
Construct a CourantPenalty penalty function from a collection of equality constraints.
- CourantPenalty(EqualityConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.CourantPenalty
-
Construct a CourantPenalty penalty function from a collection of equality constraints.
- CourantPenalty(EqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.CourantPenalty
-
Construct a CourantPenalty penalty function from a collection of equality constraints.
- cov() - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.AntitheticVariates
-
- cov() - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.CommonRandomNumbers
-
Gets the covariance between f and g.
- cov() - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.ControlVariates
-
- covariance() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.CovarianceEstimation
-
- covariance() - Method in interface com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionSolver
-
Get the estimated Covariance matrix of the selection problem.
- covariance() - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
-
Gets the estimated covariance matrix.
- covariance() - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Get the estimated covariance matrix.
- Covariance - Class in com.numericalmethod.suanshu.stats.descriptive.covariance
-
Covariance is a measure of how much two variables change together.
- Covariance() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
-
Construct an empty Covariance calculator.
- Covariance(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
-
Construct a Covariance calculator, initialized with two samples.
- Covariance(Covariance) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
-
Copy constructor.
- covariance() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution
-
- covariance() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution
-
- covariance() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution
-
- covariance() - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the covariance matrix of this distribution.
- covariance() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution
-
- covariance() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.AbstractBivariateEVD
-
- covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMBeta
-
- covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMBeta
-
- covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMBeta
-
Gets the covariance matrix of the coefficient estimates, β^.
- covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticBeta
-
- covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSBeta
-
- covariance(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
-
Get the covariance matrix for prediction errors for \(\hat{x}_{n+1}\), made at time n.
- covariance(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
-
Get the covariance matrix for prediction errors for \(\hat{x}_{n+1}\), made at time n.
- covariance(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
-
Get the covariance matrix for prediction errors at time t for x^t+1.
- covariance() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Get the asymptotic covariance matrix of the estimators.
- covariance() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the asymptotic covariance matrix of the estimated parameters,
φ and θ.
- CovarianceEstimation - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008
-
Estimates the covariance matrix by maximum likelihood.
- CovarianceEstimation(Matrix, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.CovarianceEstimation
-
Solves the maximum likelihood problem for covariance selection.
- covarianceMatrix() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Get the covariance matrix, which is estimated as the inverse of negative Hessian matrix of
the log-likelihood function valued at the fitted parameter.
- CovarianceSelectionGLASSOFAST - Class in com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso
-
GLASSOFAST is the Graphical LASSO algorithm to solve the covariance selection
problem.
- CovarianceSelectionGLASSOFAST(CovarianceSelectionProblem) - Constructor for class com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
-
Solves the maximum likelihood problem for covariance selection.
- CovarianceSelectionLASSO - Class in com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso
-
The LASSO approach of covariance selection.
- CovarianceSelectionLASSO(CovarianceSelectionProblem, double) - Constructor for class com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Estimate the covariance matrix directly by using LASSO.
- CovarianceSelectionLASSO(CovarianceSelectionProblem) - Constructor for class com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Estimate the covariance matrix directly by using LASSO.
- CovarianceSelectionProblem - Class in com.numericalmethod.suanshu.stats.covariance.covarianceselection
-
This class defines the covariance selection problem outlined in d'Aspremont
(2008).
- CovarianceSelectionProblem(Matrix, double) - Constructor for class com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionProblem
-
Constructs a covariance selection problem.
- CovarianceSelectionProblem(MultivariateTimeSeries, double, boolean) - Constructor for class com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionProblem
-
Constructs a covariance selection problem from a multivariate time
series.
- CovarianceSelectionProblem(MultivariateTimeSeries, double) - Constructor for class com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionProblem
-
Constructs a covariance selection problem from a multivariate time
series.
- CovarianceSelectionProblem(CovarianceSelectionProblem) - Constructor for class com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionProblem
-
Copy constructor.
- CovarianceSelectionSolver - Interface in com.numericalmethod.suanshu.stats.covariance.covarianceselection
-
- covers(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the coversed sine or coversine of an angle.
- Cr - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
the crossover probability
- CramerVonMises2Samples - Class in com.numericalmethod.suanshu.stats.test.distribution
-
This algorithm calculates the two sample Cramer-Von Mises test statistic and p-value.
- CramerVonMises2Samples(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.CramerVonMises2Samples
-
Calculate the statistics and p-value of two sample Cramer-Von Mises test.
- CrankNicolsonConvectionDiffusionEquation1D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
-
This class uses the Crank-Nicolson scheme to obtain a numerical solution of a
one-dimensional convection-diffusion PDE.
- CrankNicolsonConvectionDiffusionEquation1D() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D
-
Constructs a Crank-Nicolson solver for a 1 dimensional
convection-diffusion PDE.
- CrankNicolsonConvectionDiffusionEquation1D.Coefficients - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
-
Gets the coefficients of a discretized 1D convection-diffusion equation
for each time step.
- CrankNicolsonHeatEquation1D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
-
The Crank-Nicolson method is an algorithm for obtaining a numerical solution
to parabolic PDE problems.
- CrankNicolsonHeatEquation1D() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D
-
- CrankNicolsonHeatEquation1D.Coefficients - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
-
Gets the coefficients of a discretized 1D heat equation for each time
step.
- crossover(Chromosome) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.Chromosome
-
Construct a Chromosome by crossing over a pair of chromosomes.
- crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
-
- crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
-
- crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
-
- crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
-
Crossover by taking the midpoint.
- csch(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the hyperbolic cosecant of a hyperbolic angle.
- CSDPMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing
-
Implements the CSDP algorithm for semidefinite programming problem with equality constraints.
- CSDPMinimizer(double, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
-
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming
problems.
- CSDPMinimizer(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
-
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming
problems.
- CSDPMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing
-
- CSRSparseMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
The Compressed Sparse Row (CSR) format for sparse matrix has this representation:
(value, col_ind, row_ptr).
- CSRSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Constructs a sparse matrix in CSR format.
- CSRSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Constructs a sparse matrix in CSR format.
- CSRSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Constructs a sparse matrix in CSR format by a list of non-zero entries.
- CSRSparseMatrix(int, int, List<SparseMatrix.Entry>, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Constructs a sparse matrix in CSR format by a list of non-zero entries.
- CSRSparseMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Constructs a sparse matrix from a matrix.
- CSRSparseMatrix(CSRSparseMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Copy constructor.
- CSV_SEPARATOR - Static variable in class com.numericalmethod.suanshu.number.DoubleUtils
-
The default separator for CSV file parsing.
- Ctor2x2(double, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Same as new GivensMatrix(2, 1, 2, c, s).
- CtorFromRho(int, int, int, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Constructs a Givens matrix from ρ.
- CtorToRotateColumns(int, int, int, double, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Constructs a Givens matrix such that [a b] * G = [* 0].
- CtorToRotateRows(int, int, int, double, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Constructs a Givens matrix such that G * [a b]t = [* 0]t.
- CtorToZeroOutEntry(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Constructs a Givens matrix such that G * A has 0 in the [i,j] entry.
- CtorToZeroOutEntryByTranspose(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Constructs a Givens matrix such that Gt * A has 0 in the [i,j]
entry.
- CubicHermite - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
-
Cubic Hermite spline interpolation is a piecewise spline interpolation, in which each polynomial
is in Hermite form which consists of two control points and two control tangents.
- CubicHermite() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicHermite
-
- CubicHermite(CubicHermite.Tangent) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicHermite
-
Construct an instance with the given method to compute tangents.
- CubicHermite.Tangent - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
-
The method for computing the control tangent at a given index.
- CubicHermite.Tangents - Enum in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
-
- CubicRoot - Class in com.numericalmethod.suanshu.analysis.function.polynomial.root
-
This is a cubic equation solver.
- CubicRoot() - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.root.CubicRoot
-
- CubicSpline - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
-
The cubic spline interpolation fits a cubic polynomial between each pair of
adjacent points such that adjacent cubics are continuous in their first and
second derivatives.
- CubicSpline() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicSpline
-
Creates an instance with default end conditions which fits
natural splines, meaning that the second derivative at both ends
are zero.
- cumsum(Vector[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the cumulative sums.
- cumsum(double[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Gets the cumulative sums of the elements in an array.
- cumsum(int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Gets the cumulative sums of the elements in an array.
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial
-
- cumulant(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMExponentialDistribution
-
The cumulant function of the exponential distribution.
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma
-
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian
-
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian
-
- cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson
-
- CumulativeNormalHastings - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
Hastings algorithm is faster but less accurate way to compute the cumulative standard Normal.
- CumulativeNormalHastings() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalHastings
-
- CumulativeNormalInverse - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The inverse of the cumulative standard Normal distribution function is defined as:
\[
N^{-1}(u)
/]
This implementation uses the Beasley-Springer-Moro algorithm.
- CumulativeNormalInverse() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalInverse
-
- CumulativeNormalMarsaglia - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
Marsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal.
- CumulativeNormalMarsaglia() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalMarsaglia
-
- cumulativeProportionVar() - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the cumulative proportion of overall variance explained by the
principal components
- CurveFitting - Interface in com.numericalmethod.suanshu.analysis.curvefit
-
Curve fitting is the process of constructing a curve, or mathematical function, that has the best
fit to a series of data points, possibly subject to constraints.
- cut(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer.MyCutter
-
- cut(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer.MyCutter
-
- cut(SimplexTable) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer.CutterFactory.Cutter
-
Cut a simplex table.
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
-
Get the diagonal matrix D as in Q * D * Q' = A.
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
The diagonal entries of the diagonal matrix D.
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot
-
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Gets the D matrix as in the real Schur canonical form Q'AQ = D.
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
-
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
-
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD
-
- D() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the D matrix as in SVD decomposition.
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Returns the matrix D as in A=UDV'.
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Get D the the diagonal matrix in the LDL decomposition.
- D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Gets the diagonal matrix D in the LDL decomposition.
- D() - Method in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA.Signal
-
- D() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Gets D as in eq.
- d() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets d.
- D() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
Computes D.
- d() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the order of integration.
- d() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the order of integration.
- DAgostino - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
-
D'Agostino's K2 test is a goodness-of-fit measure of departure from normality.
- DAgostino(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
Perform D'Agostino's test to test for the departure from normality.
- DAGraph<V,E extends Arc<V>> - Interface in com.numericalmethod.suanshu.graph
-
A directed acyclic graph (DAG), is a directed graph with no directed cycles.
- Dai2011HMM - Class in com.numericalmethod.suanshu.finance.trading.trend.dai2011
-
Creates a two-state Geometric Brownian Motion with a constant volatility.
- Dai2011HMM(double, double, double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Constructs a two-state Markov switching Geometric Brownian Motion.
- Dai2011HMM(Dai2011HMM.ModelParam) - Constructor for class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
- Dai2011HMM(Dai2011HMM) - Constructor for class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Copy constructor.
- Dai2011HMM.CalibrationParam - Class in com.numericalmethod.suanshu.finance.trading.trend.dai2011
-
- Dai2011HMM.ModelParam - Class in com.numericalmethod.suanshu.finance.trading.trend.dai2011
-
- Dai2011Solver - Class in com.numericalmethod.suanshu.finance.trading.trend.dai2011
-
Solves the stochastic control problem in the referenced paper to get the two
thresholds.
- Dai2011Solver.Boundaries - Class in com.numericalmethod.suanshu.finance.trading.trend.dai2011
-
- Dai2011Solver.Builder - Class in com.numericalmethod.suanshu.finance.trading.trend.dai2011
-
- dampedBFGSHessianUpdate(Matrix, Vector, Vector) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
-
Damped BFGS Hessian update.
- data() - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- DATE_FORMAT_STRING - Static variable in class com.numericalmethod.suanshu.misc.license.License
-
Date format for all kinds of dates in a license file.
- DateTimeGenericTimeSeries<V> - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure
-
This is a generic time series where time is indexed by DateTime and value can be any
type.
- DateTimeGenericTimeSeries(DateTime[], V[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.DateTimeGenericTimeSeries
-
Construct a time series.
- DateTimeGenericTimeSeries.Entry<V> - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure
-
This is the TimeSeries.Entry for a DateTime -indexed time series.
- DateTimeTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate
-
This is a time series has its double values indexed by DateTime.
- DateTimeTimeSeries(DateTime[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.DateTimeTimeSeries
-
Construct a time series from DateTime and double.
- DateTimeTimeSeries(ArrayList<DateTime>, ArrayList<Double>) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.DateTimeTimeSeries
-
Construct a time series from DateTime and double.
- dB(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get a Brownian motion increment.
- dB(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Get the Brownian increment at the i-th time point.
- dB(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomProcess
-
Get a Brownian motion increment.
- db(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.MilsteinSDE
-
\[
\frac{d\sigma}{dt}
\]
- DBeta - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of the
Beta function w.r.t
x, \({\partial \over \partial x} \mathrm{B}(x, y)\).
- DBeta() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBeta
-
- DBetaRegularized - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of the Regularized Incomplete Beta function,
BetaRegularized, w.r.t the upper limit,
x.
- DBetaRegularized(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBetaRegularized
-
Construct the derivative function of the Regularized Incomplete Beta function,
BetaRegularized.
- dBt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Get all the Brownian increments.
- ddy() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative
-
Gets y'' = F(x,y).
- DeBest2BinCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell
-
- deColumnMean(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the de-mean (column means) matrix of a given matrix.
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
Make a deep copy of the underlying matrix.
- deepCopy() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.Matrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
Return
this as this
Matrix is immutable.
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
Returns this as the reference is immutable.
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.CombinedVectorByRef
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- deepCopy() - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
- deepCopy() - Method in interface com.numericalmethod.suanshu.misc.DeepCopyable
-
The implementation returns an instance created from this by the copy
constructor of the class, or just this if the instance itself is
immutable.
- deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
- deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFtWt
-
- deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
- deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
- DeepCopyable - Interface in com.numericalmethod.suanshu.misc
-
This interface provides a way to do polymorphic copying.
- DEFAULT_CACHE_SIZE - Static variable in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
The default cache size = the number of available processors ×
1000.
- DEFAULT_CACHE_SIZE - Static variable in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
The default cache size = the number of available processors × 1000.
- DEFAULT_EPSILON - Static variable in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
- DEFAULT_GRID_SIZE - Static variable in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
- DEFAULT_INITIAL_TEMPERATURE - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
the default initial temperature
- DEFAULT_LICENSE_FILES - Static variable in class com.numericalmethod.suanshu.misc.license.License
-
Default license file names.
- DEFAULT_MATRIX_SIZE_THRESHOLD - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
The default matrix size threshold.
- DEFAULT_MAXIMUM_ITERATIONS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- DEFAULT_MERSENNE_EXPONENT - Static variable in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
-
- DEFAULT_METHOD - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD
-
The default algorithm for computing SVD.
- DEFAULT_MIN_RELATIVE_GAP - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Default value for the minimum relative gap threshold.
- DEFAULT_NLAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
the default number of lags
- DEFAULT_NUMBER_OF_LAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
-
the default number of lags
- DEFAULT_NUMBER_OF_LAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
the default number of lags
- DEFAULT_PENALTY_FUNCTION_FACTORY - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
the default penalty function factory
- DEFAULT_QA - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
the default acceptance parameter
- DEFAULT_QV - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
the default visiting parameter
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
The algorithm recomputes the residual as b - Axi once
per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
The algorithm recomputes the residual as b - Axi once per this number of
iterations
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
The algorithm recomputes the residual as b - Axi once per this number of iterations
- DEFAULT_SAFETY_FACTOR - Static variable in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
-
Default value for the safety factor γ.
- DEFAULT_SIGMA - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.AntoniouLu2007
-
the default value of the centering parameter
- DEFAULT_STABLE_ITERATION_COUNT - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- DEFAULT_THRESHOLD - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
-
The default tolerance parameter tol.
- DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.AbsoluteTolerance
-
default tolerance
- DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.RelativeTolerance
-
default tolerance
- DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- DefaultDeflationCriterion - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
-
The default deflation criterion is to use eq.
- DefaultDeflationCriterion() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
-
Constructs the default deflation criterion.
- DefaultDeflationCriterion(double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
-
Constructs the default deflation criterion.
- DefaultDeflationCriterion(DefaultDeflationCriterion.MatrixNorm) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
-
Constructs the default deflation criterion, with the algorithm for computing matrix norm for
the matrix argument in
isNegligible().
- DefaultDeflationCriterion(double, DefaultDeflationCriterion.MatrixNorm) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
-
Constructs the default deflation criterion, with the algorithm for computing matrix norm for
the matrix argument in
isNegligible().
- DefaultDeflationCriterion.MatrixNorm - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
-
Computes the norm of a given matrix.
- DefaultMatrixStorage - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype
-
There are multiple ways to implement the storage data structure depending on the matrix type for
optimization purpose.
- DefaultMatrixStorage(MatrixAccess) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Construct a DefaultMatrixStorage to wrap a storage for access.
- DefaultRoot() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.DefaultRoot
-
- DefaultSimplex - Class in com.numericalmethod.suanshu.optimization.multivariate.initialization
-
A simplex optimization algorithm, e.g., Nelder-Mead, requires an initial simplex to start the search.
- DefaultSimplex(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.initialization.DefaultSimplex
-
Construct a simplex builder.
- DefaultSimplex() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.initialization.DefaultSimplex
-
Construct a simplex builder.
- definingVector() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
-
- definingVector() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Get the Householder defining vector which is orthogonal to the Householder hyperplane.
- Deflation - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
-
A deflation found in a Hessenberg (or tridiagonal in symmetric case) matrix.
- DeflationCriterion - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
-
Determines whether a sub-diagonal entry is sufficiently small to be neglected.
- deflationCriterion - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
- degree() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Get the degree of this polynomial.
- deleteCol(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Deletes column i.
- deleteRow(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Deletes row i.
- delta - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- delta() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004.Result
-
Gets the shrinkage parameter δ.
- deltaX() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
Return the distance between two adjacent points along the x-axis.
- deltaX(int) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
-
Get the distance between two adjacent points along the axis with the given index.
- deltaY() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
Return the distance between two adjacent points along the y-axis.
- DenseData - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense
-
This implementation of the storage of a dense matrix stores the data of a 2D matrix as an 1D
array.
- DenseData(double[], int, int, DoubleArrayOperation) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Construct a storage, and specify the implementations of the element-wise operations.
- DenseData(double[], int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Construct a storage.
- DenseMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense
-
This class implements the standard, dense, double based matrix
representation.
- DenseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a 0 matrix of dimension nRows * nCols.
- DenseMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a matrix from a 2D double[][] array.
- DenseMatrix(double[], int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a matrix from a 1D double[].
- DenseMatrix(Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a column matrix from a vector.
- DenseMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Converts any matrix to the standard matrix representation.
- DenseMatrix(DenseMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Copy constructor performing a deep copy.
- DenseMatrix(DenseMatrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
This constructor is useful for subclass to pass in computed value.
- DenseMatrixMultiplication - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
Matrix operation that multiplies two matrices.
- DenseMatrixMultiplicationByBlock - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
- DenseMatrixMultiplicationByBlock() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock
-
- DenseMatrixMultiplicationByBlock(DenseMatrixMultiplicationByBlock.BlockAlgorithm) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock
-
- DenseMatrixMultiplicationByBlock.BlockAlgorithm - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
- DenseMatrixMultiplicationByIjk - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
Implements the naive IJK algorithm.
- DenseMatrixMultiplicationByIjk() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByIjk
-
- DenseVector - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense
-
This class implements the standard, dense, double based vector
representation.
- DenseVector(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector.
- DenseVector(int, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by repeating a value.
- DenseVector(double...) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a double[].
- DenseVector(Double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a Double[].
- DenseVector(List<Double>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a List<Double>.
- DenseVector(Collection<? extends Number>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a collection, with order defined by
its iterator.
- DenseVector(int[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a int[].
- DenseVector(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector from a column or row matrix.
- DenseVector(Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Casts any vector to a DenseVector.
- DenseVector(DenseVector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Copy constructor.
- Densifiable - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense
-
This interface specifies whether a matrix implementation can be efficiently converted to the standard dense matrix representation.
- density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.AbstractBivariateProbabilityDistribution
-
- density(double, double) - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.BivariateProbabilityDistribution
-
The joint distribution density \(f_{X_1,X_2}(x_1,x_2)\).
- density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution
-
- density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution
-
- density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution
-
- density(Vector) - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
-
The density function, which, if exists, is the derivative of F.
- density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
This is the probability mass function.
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
This is the probability mass function for the discrete sample.
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- density(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
The density function, which, if exists, is the derivative of F.
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
The density function, which, if exists, is the derivative of F.
- density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
The probability density function
\[
f(x; \mu,\sigma,\xi) =
\begin{cases}
\frac{1}{\sigma}\left(1+ \frac{\xi (x-\mu)}{\sigma}\right)^{\left(-\frac{1}{\xi} - 1\right)}
& \text{for} \; \xi \neq 0, \\
\frac{1}{\sigma}\exp \left(-\frac{x-\mu}{\sigma}\right) & \text{for} \; \xi = 0
\end{cases}
\]
for \(x \ge \mu\) when \(\xi \ge 0\), and \(\mu \le x \le \mu - \sigma /\xi\) when
\(\xi <0\).
- density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
The probability density function.
- density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
The probability density function.
- density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
-
- density(int, double) - Method in class com.numericalmethod.suanshu.stats.hmm.discrete.DiscreteHMM
-
Gets the (conditional) probability mass of making an observation in a
particular state.
- density(int, double) - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
Gets the (conditional) probability density/mass of making an observation
in a particular state.
- density(int, double) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHMM
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- density(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- DEOptim - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
Differential Evolution (DE) is a global optimization method.
- DEOptim(DEOptim.NewCellFactory, RandomLongGenerator, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
-
Construct a DEOptim to solve unconstrained minimization problems.
- DEOptim(double, double, RandomLongGenerator, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
-
Construct a DEOptim to solve unconstrained minimization problems.
- DEOptim(double, double, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
-
Construct a DEOptim to solve unconstrained minimization problems.
- DEOptim.NewCellFactory - Interface in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
This factory constructs a new DEOptimCellFactory for each minimization problem.
- DEOptim.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
This is the solution to a minimization problem using DEOptim.
- DeOptimCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
-
- DEOptimCellFactory - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
- DEOptimCellFactory(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Construct an instance of a DEOptimCellFactory.
- DEOptimCellFactory(DEOptimCellFactory) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Copy constructor.
- DEOptimCellFactory.DeOptimCell - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
A DeOptimCell is a chromosome for a real valued function (an optimization problem)
and a candidate solution.
- dependence(double) - Method in interface com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVD
-
The dependence function \(A\) for the parametric bivariate extreme value model.
- dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- depth() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS.Node
-
Gets the depth of this node.
- depth(V) - Method in interface com.numericalmethod.suanshu.graph.RootedTree
-
Gets the (unweighted) distance of a vertex from the root of the vertex.
- depth(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- depth(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- DeRand1BinCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
-
- DErf - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of the Error function,
Erf.
- DErf() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DErf
-
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkCloglog
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkFunction
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkIdentity
-
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkInverse
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkInverseSquared
-
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkLog
-
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkLogit
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkProbit
-
- derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkSqrt
-
Derivative of the link function, i.e., g'(x).
- DerivativeFunction - Interface in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem
-
Defines the derivative function F(x, y) for ODE problems.
- deRowMean(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the de-mean (row means) matrix of a given matrix.
- det() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix
-
The determinant of a Hilbert matrix is the reciprocal of an integer.
- det(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the determinant of a matrix.
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial
-
- deviance(double, double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMExponentialDistribution
-
Deviance D(y;μ^) measures the goodness-of-fit of a model, which is defined as
the difference between the maximum log likelihood achievable and that achieved by the model.
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma
-
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian
-
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian
-
- deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson
-
- deviance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMResiduals
-
Gets the (total) deviance.
- deviance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticResiduals
-
Gets the residual deviance.
- devianceResiduals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMResiduals
-
Gets the deviances residuals.
- devianceResiduals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticResiduals
-
Gets the residuals, ε.
- deviances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMResiduals
-
Gets the deviances of the observations.
- deviances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMResiduals
-
- df(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
-
Compute the finite difference for f at x with an increment
h for the n-th order using either forward, backward, or
central difference.
- df() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the degree of freedom.
- df() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Get the degree of freedom.
- df1() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
Get the first degree of freedom.
- df2() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
Get the second degree of freedom.
- Dfdx - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
The first derivative is a measure of how a function changes as its input changes.
- Dfdx(UnivariateRealFunction, Dfdx.Method) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
-
Construct the first order derivative function of a univariate function f.
- Dfdx(UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
-
Construct, using the central finite difference, the first order derivative function of a univariate function f.
- Dfdx.Method - Enum in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
the available methods to compute the numerical derivative
- DFFITS() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMDiagnostics
-
DFFITS, Welsch and Kuh Measure.
- DFPMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
-
The Davidon-Fletcher-Powell method is a quasi-Newton method
to solve unconstrained nonlinear optimization problems.
- DFPMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.DFPMinimizer
-
Construct a multivariate minimizer using the DFP method.
- DFS<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
-
This class implements the depth-first-search using iteration.
- DFS(Graph<? extends V, ? extends Edge<V>>) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS
-
Constructs a DFS tree of a graph.
- DFS(Graph<W, ? extends Edge<V>>, V, int) - Static method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS
-
Runs the depth-first-search on a graph from a designated root.
- DFS.Node<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
-
This is a node in a DFS-spanning tree.
- DFS.Node.Color - Enum in com.numericalmethod.suanshu.graph.algorithm.traversal
-
This is the coloring scheme of visits.
- DGamma - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of the Gamma function, \({d \mathrm{\Gamma}(x) \over dx}\).
- DGamma() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGamma
-
- DGaussian - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of a
Gaussian function, \({d \mathrm{\phi}(x) \over dx}\).
- DGaussian(Gaussian) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGaussian
-
Construct the derivative function of a Gaussian function.
- Dhat() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Gets the modified diagonal matrix which is positive definite.
- di2UnDiGraph(DiGraph<V, ? extends Arc<V>>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Converts a directed graph into an undirected graph by removing the direction of all arcs.
- diagnostics() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
Gets the diagnostic measures of an OLS regression.
- diagonal(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the diagonal of a matrix as a vector.
- diagonal(SparseMatrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the diagonal of a sparse matrix as a sparse vector.
- Diagonalization() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.Diagonalization
-
- DiagonalMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
-
A diagonal matrix has non-zero entries only on the main diagonal.
- DiagonalMatrix(double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Constructs a diagonal matrix from a double[].
- DiagonalMatrix(double[], int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- DiagonalMatrix(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Constructs a 0 diagonal matrix of dimension dim * dim.
- DiagonalMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- DiagonalMatrix(DiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Copy constructor.
- diagonalMatrix(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Gets the diagonal of a matrix.
- DiagonalSum - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
Add diagonal elements to a matrix, an efficient implementation.
- DiagonalSum(Matrix, double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- DiagonalSum(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- DiagonalSum(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- diff(double[], int, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Gets the lagged and iterated differences.
- diff(double[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Gets the first differences of an array.
- diff(double[][], int, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Gets the lagged and iterated differences of vectors.
- diff(double[][]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Gets the first differences of an array of vectors.
- diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct an instance of MultivariateGenericTimeTimeSeries by taking the first
difference d times.
- diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries by taking the first difference
d times.
- diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Construct an instance of GenericTimeTimeSeries by taking the first difference
d times.
- diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries by taking the first difference d
times.
- DifferencedIntTimeTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime
-
Differencing of a time series xt in discrete time t is the
transformation of the series to a new time series (1-B)xt where the new values
are the differences between consecutive values of xt.
- DifferencedIntTimeTimeSeries(IntTimeTimeSeries, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- Diffusion - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients
-
This class represents the diffusion term, σ, of a univariate SDE.
- diffusion() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.SDE
-
Get the diffusion coefficient: \(\sigma(t, X_t, Z_t, ...)\).
- DiffusionMatrix - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
The diffusion term, σ, of an SDE takes this form: σ(dt, Xt, Zt, ...).
- DiffusionSigma - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
This class implements the diffusion term in the form of a diffusion matrix.
- DiffusionSigma() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
-
- Digamma - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The digamma function is defined as the logarithmic derivative of the gamma function.
- Digamma() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.Digamma
-
- DiGraph<V,E extends Arc<V>> - Interface in com.numericalmethod.suanshu.graph
-
A directed graph or digraph is a graph, or set of nodes connected by edges, where the edges have
a direction associated with them.
- Dijkstra<V> - Class in com.numericalmethod.suanshu.graph.algorithm.shortestpath
-
Dijkstra's algorithm is a graph search algorithm that solves the single-source shortest path
problem for a graph with non-negative edge path costs, producing a shortest path tree.
- Dijkstra(DiGraph<V, ? extends WeightedArc<V>>, V) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.shortestpath.Dijkstra
-
- dim - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
the variable dimension
- dim() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the dimension of the process.
- dimension() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
-
- dimension() - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateGrid
-
Get the total number of dimensions of the grid.
- dimension() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
-
- dimension() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem.DerivativeFunction
-
Gets the dimension of y.
- dimension() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Gets the dimension of y.
- dimension() - Method in class com.numericalmethod.suanshu.geometry.LineSegment
-
Get the dimension of the coordinate space.
- dimension() - Method in class com.numericalmethod.suanshu.geometry.Point
-
Get the dimension of the coordinate space.
- dimension() - Method in interface com.numericalmethod.suanshu.geometry.polyline.PolygonalChain
-
Get the number of dimensions of this polygonal chain.
- dimension() - Method in class com.numericalmethod.suanshu.geometry.polyline.PolygonalChainByArray
-
- dimension() - Method in class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalArray
-
- dimension() - Method in interface com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalCollection
-
Returns the number of dimensions of the collection.
- dimension() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.Constraints
-
Get the number of variables.
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralConstraints
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
- dimension() - Method in class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
- dimension() - Method in interface com.numericalmethod.suanshu.optimization.problem.OptimProblem
-
Get the number of variables.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the dimension of the system, i.e., the dimension of the state vector.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
-
Gets the dimension of observation yt.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
-
Gets the dimension of state xt.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.ObservationEquation
-
Get the dimension of observation yt.
- dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Get the dimension of state xt.
- dimension() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
- dimension() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
- dimension() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix
-
Get the dimension of the process.
- dimension() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get the dimension of the process.
- dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- dimension() - Method in interface com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateTimeSeries
-
Get the dimension of the multivariate time series.
- dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the dimension of multivariate time series.
- dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the dimension of the multivariate time series.
- DimensionCheck - Class in com.numericalmethod.suanshu.misc.datastructure
-
These are the utility functions for checking table dimension.
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction
-
- dimensionOfDomain() - Method in interface com.numericalmethod.suanshu.analysis.function.Function
-
Get the number of variables the function has.
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractRealScalarFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2rm.AbstractRealVectorFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPZeroValue
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Gets the dimension of the original (unstacked) problem.
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.SumOfPenalties
-
- dimensionOfDomain() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.ZeroPenalty
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction
-
- dimensionOfRange() - Method in interface com.numericalmethod.suanshu.analysis.function.Function
-
Get the dimension of the range space of the function.
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractRealScalarFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.rn2rm.AbstractRealVectorFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPZeroValue
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
- dimensionOfRange() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.PenaltyFunction
-
- DirichletDistribution - Class in com.numericalmethod.suanshu.stats.distribution.multivariate
-
The Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted Dir(a),
is a family of continuous multivariate probability distributions parametrized by a vector
a of positive reals.
- DirichletDistribution(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution
-
Constructs an instance of Dirichlet distribution.
- DirichletDistribution(double[]) - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution
-
Constructs an instance of Dirichlet distribution.
- dis_m_LF_list - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
whole m_LF_zxi_list(including the modified Stieltjes transform of
endpoints)
- DiscreteHMM - Class in com.numericalmethod.suanshu.stats.hmm.discrete
-
This is the discrete hidden Markov model as defined by Rabiner.
- DiscreteHMM(Vector, Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.hmm.discrete.DiscreteHMM
-
Constructs a discrete hidden Markov model.
- DiscreteHMM(DiscreteHMM) - Constructor for class com.numericalmethod.suanshu.stats.hmm.discrete.DiscreteHMM
-
Copy constructor.
- DiscreteSDE - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete
-
This interface represents the discrete approximation of a univariate SDE.
- Discretization(double, double, double) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalGrid.Discretization
-
Constructs a discretization of an interval.
- discretization - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial
-
- dispersion(Vector, Vector, int) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMExponentialDistribution
-
Different distribution models have different ways to compute dispersion, Φ.
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma
-
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian
-
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian
-
- dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson
-
- dist - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMFamily
-
- distance(Point) - Method in class com.numericalmethod.suanshu.geometry.LineSegment
-
Calculate the shortest distance between a point and this line segment in Euclidean geometry.
- distance(Point) - Method in class com.numericalmethod.suanshu.geometry.Point
-
Compute the Euclidean distance between this point and the given point.
- distance(V) - Method in class com.numericalmethod.suanshu.graph.algorithm.shortestpath.Dijkstra
-
- distance(V) - Method in interface com.numericalmethod.suanshu.graph.algorithm.shortestpath.ShortestPath
-
Gets the shortest distance from the source to a vertex.
- distribution() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMFamily
-
- distribution() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.QuasiFamily
-
- DiversificationMeasure - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification
-
Defines the diversification of a portfolio.
- divide(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- divide(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- divide(DenseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- divide(Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
A vector is divided by another vector, element-by-element.
- divide(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- divide(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Divide this by that, entry-by-entry.
- divide(F) - Method in interface com.numericalmethod.suanshu.algebra.structure.Field
-
/ : F × F → F
That is the same as
this.multiply(that.inverse())
- divide(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Compute the quotient of this complex number divided by another complex number.
- divide(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
- divide(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
-
Divide one double array by another, entry-by-entry.
- divide(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- divide(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- divide(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- divide(Real, int) - Method in class com.numericalmethod.suanshu.number.Real
-
/ : R × R → R
Divide this number by another one.
- DividedDifferences - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
-
Divided differences is recursive division process for calculating the coefficients in the
interpolation polynomial in the Newton form.
- DividedDifferences(OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.DividedDifferences
-
Construct divided differences from a given collection of ordered pairs.
- DividedDifferences(SortedOrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.DividedDifferences
-
Construct divided differences from a given sorted collection of ordered pairs.
- dk - Variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
the line search direction at the k-th iteration
- DLM - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.
- DLM(double, double, ObservationEquation, StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Construct a univariate controlled dynamic linear model.
- DLM(DLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Copy constructor.
- DLMSeries - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is a simulator for a multivariate controlled dynamic linear model process.
- DLMSeries(int, DLM, double[], RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Simulate a univariate controlled dynamic linear model process.
- DLMSeries(int, DLM, double[]) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Simulate a univariate controlled dynamic linear model process.
- DLMSeries(int, DLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Simulate a univariate controlled dynamic linear model process.
- DLMSeries.Entry - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is the TimeSeries.Entry for a univariate DLM time series.
- DLMSim - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is a simulator for a univariate controlled dynamic linear model process.
- DLMSim(DLM, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSim
-
Simulate a univariate controlled dynamic linear model process.
- DLMSim.Innovation - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
a simulated innovation
- dof() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FAEstimator
-
Gets the degree of freedom in the factor analysis model.
- DOKSparseMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
The Dictionary Of Key (DOK) format for sparse matrix uses the coordinates of
non-zero entries in the matrix as keys.
- DOKSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Construct a sparse matrix in DOK format.
- DOKSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Construct a sparse matrix in DOK format.
- DOKSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Construct a sparse matrix in DOK format by a list of non-zero entries.
- DOKSparseMatrix(DOKSparseMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Copy constructor.
- domain - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
the integer values the variable can take
- Doolittle - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle
-
Doolittle algorithm is a LU decomposition algorithm which decomposes a square matrix
A into:
P is an n x n permutation matrix;
L is an n x n (unit) lower triangular matrix;
U is an n x n upper triangular matrix,
such that PA = LU.
- Doolittle(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
Runs Doolittle algorithm on a square matrix for LU decomposition.
- Doolittle(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
Runs Doolittle algorithm on a square matrix for LU decomposition.
- Doolittle(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
Runs Doolittle algorithm on a square matrix for LU decomposition.
- Doolittle(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
Runs Doolittle algorithm on a square matrix for LU decomposition.
- dotProduct(long[], long[]) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
\(x_1 \cdot x_2\)
This operation is called inner product when used in the context of vector space.
- dotProduct(double[], double[]) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
\(x_1 \cdot x_2\)
This operation is called inner product when used in the context of vector space.
- doubleArray2intArray(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a double array to an int array, rounding down if
necessary.
- doubleArray2List(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a double array to a list.
- DoubleArrayMath - Class in com.numericalmethod.suanshu.number.doublearray
-
These are the math functions that operate on double[].
- DoubleArrayOperation - Interface in com.numericalmethod.suanshu.number.doublearray
-
It is possible to provide different implementations for different platforms, hardware, etc.
- DoubleBruteForceMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained
-
This implementation solves an unconstrained minimization problem by
brute force search for all given possible values.
- DoubleBruteForceMinimizer(boolean) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.DoubleBruteForceMinimizer
-
- DoubleExponential - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation speeds up the convergence of the Trapezoidal Rule exponentially.
- DoubleExponential(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
Construct a DoubleExponential substitution rule by trying to automatically determine the substitution rule.
- DoubleExponential4HalfRealLine - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is good for the region \((0, +\infty)\).
- DoubleExponential4HalfRealLine(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential4HalfRealLine
-
Construct a DoubleExponential4HalfRealLine substitution rule.
- DoubleExponential4RealLine - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is good for the region \((-\infty, +\infty)\).
- DoubleExponential4RealLine(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential4RealLine
-
Construct a DoubleExponential4RealLine substitution rule.
- doubleMod(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Double modulo, analogous to x % y when x and y are ints.
- DoubleUtils - Class in com.numericalmethod.suanshu.number
-
These are the utility functions to manipulate double and int.
- DoubleUtils.ifelse - Interface in com.numericalmethod.suanshu.number
-
Return a value with the same shape as test which is filled with
elements selected from either yes or no depending on
whether the element of test is true or false.
- DoubleUtils.RoundingScheme - Enum in com.numericalmethod.suanshu.number
-
the available schemes to round a number
- DoubleUtils.which - Interface in com.numericalmethod.suanshu.number
-
Decide whether x satisfies the boolean test.
- doubleValue() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
Construct a
DenseMatrix equivalent of this Complex matrix (rounded if necessary).
- doubleValue() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
Construct a
DenseMatrix equivalent of this Real matrix (rounded if necessary).
- doubleValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- doubleValue() - Method in class com.numericalmethod.suanshu.number.Real
-
- doubleValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
- DPolynomial - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
This is the first order derivative function of a
Polynomial, which, again, is a polynomial.
- DPolynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DPolynomial
-
Construct the derivative function of a
Polynomial, which, again, is a polynomial.
- DQDS - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds
-
Computes all the eigenvalues of the symmetric positive definite tridiagonal matrix associated
with the qd-array Z to high relative accuracy.
- DQDS(int, Vector, Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds.DQDS
-
Computes all the eigenvalues of the symmetric positive definite tridiagonal matrix associated
with the q and e values to high relative accuracy.
- dReturns() - Method in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA.Signal
-
Gets the difference between predicted accumulated returns and
realized accumulated
returns at time T-H+1, the last historical fully realized
accumulated return.
- Drift - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients
-
This class represents the drift term, μ, of a univariate SDE.
- drift() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.SDE
-
Get the drift: \(\mu(t,X_t,Z_t,...)\).
- drift1 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.CalibrationParam
-
- drift2 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.CalibrationParam
-
- DriftVector - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
-
The drift term, μ, of an SDE takes this form: μ(dt, Xt, Zt, ...).
- drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct an instance of MultivariateGenericTimeTimeSeries by dropping the leading
nItems entries, those most backward in time entries.
- drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries by dropping the leading
nItems entries.
- drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Construct an instance of GenericTimeTimeSeries by dropping the leading nItems
entries.
- drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries by dropping the leading nItems
entries.
- dropFactor(int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection
-
Drops the indexed factor.
- dt - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.ModelParam
-
- dt - Variable in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
-
- dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the current time differential.
- dt(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Get the i-th time increment.
- dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Get all the time increments.
- dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Get the current time differential.
- dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUSim
-
Get the time interval \(\delta_t\) of this generator.
- du() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integral
-
Get an array of the measure values.
- du() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDB
-
- du() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDt
-
- dUdx(RealVectorFunction) - Static method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
-
Gets the derivative of the potential function, given the derivative of the log density.
- DuplicatedAbscissae - Exception in com.numericalmethod.suanshu.analysis.function.tuple
-
This exception is thrown when a function has two same x-abscissae, hence ill-defined.
- DuplicatedAbscissae() - Constructor for exception com.numericalmethod.suanshu.analysis.function.tuple.DuplicatedAbscissae
-
Construct an instance.
- DuplicatedAbscissae(String) - Constructor for exception com.numericalmethod.suanshu.analysis.function.tuple.DuplicatedAbscissae
-
Construct a DuplicatedAbscissae runtime exception with an error message.
- DVInv() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
Computes D / V(μ).
- dWt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the increment of the driving Brownian motion during the time differential.
- dWt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Get the increment of the driving Brownian motion during the time differential.
- dx(BivariateGrid, int, int) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicInterpolation.PartialDerivatives
-
Get the partial derivative \(\frac{\partial z}{\partial x}\), at the given position in
the grid.
- dx(BivariateGrid, int, int) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
- dx() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval
-
- dx() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.SubstitutionRule
-
the first order derivative of the transformation: x'(t) = dx(t)/dt
- dxdy(BivariateGrid, int, int) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicInterpolation.PartialDerivatives
-
Get the cross derivative \(\frac{\partial^2 z}{\partial x \partial y}\), at the given
position in the grid.
- dxdy(BivariateGrid, int, int) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
-
- dXt(MultivariateFt) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
- dXt(MultivariateFt) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE
-
This is the SDE specification of a stochastic process.
- dXt(MultivariateFt) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
-
This is the SDE specification of a stochastic process.
- dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.BMSDE
-
- dXt(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.DiscreteSDE
-
This is the SDE specification of a stochastic process.
- dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.EulerSDE
-
This is the SDE specification of a stochastic process.
- dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.MilsteinSDE
-
This is the SDE specification of a stochastic process.
- dy(BivariateGrid, int, int) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicInterpolation.PartialDerivatives
-
Get the partial derivative \(\frac{\partial z}{\partial y}\), at the given position in
the grid.
- dy(BivariateGrid, int, int) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
-
- dy() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Gets the first order derivative function.
- DynamicCreator - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation
-
Performs the Dynamic Creation algorithm (DC) to generate parameters for
MersenneTwister.
- DynamicCreator(MersenneExponent, long...) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.DynamicCreator
-
Constructs a new instance which aims to generate MT RNGs with the given period.
- DynamicCreatorException - Exception in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation
-
Indicates that a problem has occurred in the dynamic creation process, usually because suitable
parameters were not found.
- DynamicsState(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState
-
Constructs a new instance with the given position and momentum.
- E() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets E, the residual matrix.
- E() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets E, the residual matrix.
- e_t(int) - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets the residual of all subject at time t.
- e_t(int) - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets the residual of all subject at time t.
- Edge<V> - Interface in com.numericalmethod.suanshu.graph
-
An edge connects a pair of vertices.
- EdgeBetweeness<V> - Class in com.numericalmethod.suanshu.graph.community
-
The edge betweenness centrality is defined as the number of the shortest paths that go through an
edge in a graph or network.
- EdgeBetweeness(UnDiGraph<V, ? extends UndirectedEdge<V>>) - Constructor for class com.numericalmethod.suanshu.graph.community.EdgeBetweeness
-
Computes the edge-betweeness-es of all edges in an undirected graph.
- edges() - Method in class com.numericalmethod.suanshu.graph.community.EdgeBetweeness
-
Gets the set of all edges in the graph.
- edges() - Method in interface com.numericalmethod.suanshu.graph.Graph
-
Gets the set of all edges in this graph.
- edges(V) - Method in interface com.numericalmethod.suanshu.graph.Graph
-
Gets the set of all edges associated with a vertex in this graph.
- edges() - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
-
- edges(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
-
- edges() - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- edges(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- edges() - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- edges(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- Eigen - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
-
Given a square matrix A, an eigenvalue λ and its associated
eigenvector v are defined by Av = λv.
- Eigen(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Compute the eigenvalues and eigenvectors for a square matrix.
- Eigen(Matrix, Eigen.Method) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Compute the eigenvalues and eigenvectors for a square matrix.
- Eigen(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
- Eigen(Matrix, Eigen.Method, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Compute the eigenvalues and eigenvectors for a square matrix.
- eigen() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
Gets the eigenvalue decomposition of the correlation (or covariance)
matrix.
- Eigen.Method - Enum in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
-
the available methods to compute eigenvalues and eigenvectors
- eigenbasis() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get the eigenvectors.
- EigenBoundUtils - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Utility methods for computing bounds of eigenvalues.
- EigenCount - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Counts the number of eigenvalues in a symmetric tridiagonal matrix T that are less than a
given value x.
- EigenCount(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCount
-
Creates an instance for a symmetric tridiagonal matrix T.
- EigenCountInRange - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Finds the number of eigenvalues of the symmetric tridiagonal matrix T that are in a given
interval.
- EigenCountInRange(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCountInRange
-
Creates an instance for counting the number of eigenvalues of the symmetric tridiagonal
matrix T that are in a given interval.
- EigenDecomposition - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
-
Let A be a square, diagonalizable N × N matrix with N
linearly independent eigenvectors.
- EigenDecomposition(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
-
Runs the eigen decomposition on a square matrix.
- EigenDecomposition(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
-
Runs the eigen decomposition on a square matrix.
- EigenProperty - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
-
EigenProperty is a read-only structure that contains the information
about a particular eigenvalue,
such as its multiplicity and eigenvectors.
- eigenvalue() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get the eigenvalue.
- EigenvalueByDQDS - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds
-
Computes all the eigenvalues of a symmetric tridiagonal matrix.
- EigenvalueByDQDS(TridiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds.EigenvalueByDQDS
-
Computes all the eigenvalues of a symmetric tridiagonal matrix.
- eigenVector() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get an eigenvector.
- ELECTRIC_EPSILON0 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The electric permittivity or electrical constant \(\epsilon_0\) in farads per meter
(F m-1).
- ELECTRON_MASS_ME - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The electron rest mass \(m_e\) in kilograms (kg).
- ELECTRON_VOLT_EV - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The electron volt \(eV\) in joules (J).
- ELEMENTARY_CHARGE_E - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The elementary charge \(e\) in coulombs (C).
- ElementaryFunction - Class in com.numericalmethod.suanshu.number.complex
-
This class contains some elementary functions for complex number,
Complex.
- ElementaryFunction() - Constructor for class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
- ElementaryOperation - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
There are three elementary row operations which are equivalent to left multiplying an elementary
matrix.
- ElementaryOperation(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Construct a transformation matrix of elementary operations.
- ElementaryOperation(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Construct a transformation matrix of elementary operations.
- ElementaryOperation(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Transform A by elementary operations.
- elementDivide(Matrix, Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
- elementMultiply(Matrix, Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
- elementOperation(Matrix, Matrix, BivariateRealFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
- eliminate(GLMProblem, Matrix) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.BackwardElimination.Step
-
- eliminate(GLMProblem, Matrix) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.EliminationByAIC
-
- eliminate(GLMProblem, Matrix) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.EliminationByZValue
-
- EliminationByAIC - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
In each step, a factor is dropped if the resulting model has the least AIC, until no factor
removal can result in a model with AIC lower than the current AIC.
- EliminationByAIC() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.EliminationByAIC
-
- EliminationByZValue - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
In each step, the factor with the least z-value is dropped, until all z-values are greater than
the critical value (given by the significance level).
- EliminationByZValue(double) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.EliminationByZValue
-
Creates an instance with the given significance level [0, 1].
- Elliott2005DLM - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005
-
This class implements the Kalman filter model as in Elliott's paper.
- Elliott2005DLM(double, double, double, double, double, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Constructs an Elliott's Kalman filter model.
- Elliott2005DLM(double, double, double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Constructs an Elliott's Kalman filter model.
- Elliott2005DLM(Elliott2005DLM) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Copy constructor.
- ElliottOnlineFilter - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005
-
It is important to note that this algorithm does not guarantee that
A > 0
0 < B < 1
Therefore, we need to check the outputs.
- ElliottOnlineFilter(double[], Elliott2005DLM, int) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.ElliottOnlineFilter
-
- ElliottOnlineFilter(double[], double, double, double, double, int) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.ElliottOnlineFilter
-
- ElliottOnlineFilter.NoModelFitted - Exception in com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005
-
- EmpiricalACER - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical
-
This class contains empirical ACER \(\hat{\epsilon_k}(\eta_i)\) values and other related
statistics estimated from observations.
- EmpiricalACER(double[][], double[], double, double, double[][], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
- EmpiricalACEREstimation - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical
-
This class estimates empirical ACER values from the given observations.
- EmpiricalACEREstimation() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation
-
Create an instance with default values.
- EmpiricalACEREstimation(int, boolean, EpsilonStatisticsCalculator, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation
-
Create an instance for counting empirical ACERs.
- EmpiricalACERStatistics - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical
-
This class contains the computed statistics of the estimated ACERs.
- EmpiricalACERStatistics(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACERStatistics
-
Create an instance for storing the computed statistics for estimated epsilon for various
barrier levels.
- EmpiricalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
An empirical cumulative probability distribution function
is a cumulative probability distribution function that
assigns probability 1/n at each of the n numbers in a sample.
- EmpiricalDistribution(double[], Quantile.QuantileType) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
Construct an empirical distribution from a sample.
- EmpiricalDistribution(double[]) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- end() - Method in class com.numericalmethod.suanshu.interval.Interval
-
Get the end of this interval.
- ENDING_OF_TIME - Static variable in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
This represents a time after all (representable) times.
- ENDING_OF_TIME_LONG - Static variable in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
This represents a time after all (representable) times, in long
representation.
- energy(Vector, double...) - Method in class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AfterNoImprovement
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution
-
- entropy() - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the entropy of this distribution.
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- entropy() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Gets the entropy of this distribution.
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.AbstractBivariateEVD
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
-
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Deprecated.
- entropy() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Deprecated.
- Entry(MatrixCoordinate, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
Construct a sparse entry in a sparse matrix.
- Entry(int, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
- Entry(T, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
-
Construct an instance of TimeSeries.Entry.
- Entry(int, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateIntTimeTimeSeries.Entry
-
Construct an instance of Entry.
- Entry(double, Vector) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.MultivariateRealization.Entry
-
Construct an instance of TimeSeries.Entry.
- Entry(int, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.IntTimeTimeSeries.Entry
-
Construct an instance of TimeSeries.Entry.
- Entry(double, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.Realization.Entry
-
Construct an instance of TimeSeries.Entry.
- Entry(T, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
-
Construct an instance of Entry.
- EPSILON - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
the default epsilon used in this library
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- epsilon() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblem
-
Get the threshold to check whether a variable is an integer.
- epsilon() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
- epsilon() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
a precision parameter: when a number |x| ≤ ε, it is considered 0
- epsilon - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer
-
the convergence tolerance
- epsilon1 - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
- epsilon2 - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
- EpsilonStatisticsCalculator - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical
-
Compute statistics: mean, confidence interval of estimated ACER values \(\epsilon_k(\eta_i)\).
- EpsilonStatisticsCalculator(boolean, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EpsilonStatisticsCalculator
-
Create an instance with the weighting option and confidence interval.
- equal(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if two doubles are close enough, hence equal.
- equal(double[], double[], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if two double arrays are close enough, hence equal,
entry-by-entry.
- equal(double[][], double[][], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if two 2D arrays, double[][], are close enough, hence
equal, entry-by-entry.
- equal(int[], int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if two int arrays, int[], are equal,
entry-by-entry.
- equal(Number, Number, double) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Check the equality of two
Numbers, up to a precision.
- EqualityConstraints - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint
-
The domain of an optimization problem may be restricted by equality constraints.
- EqualityConstraints(Vector, SymmetricMatrix, SymmetricMatrix[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
Construct the equality constraints for a dual SDP problem,
\(\sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0}\).
- EqualityConstraints(Vector, Matrix[], Vector[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
Constructs the equality constraints for a dual SOCP problem, \(\max_y \mathbf{b'y}
\textrm{ s.t.,} \\ \mathbf{\hat{A}_i'y + s_i = \hat{c}_i} \\ s_i \in K_i, i = 1, 2, ...,
q\).
- EquallySpacedVariable(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid.EquallySpacedVariable
-
Create a new instance which specifies the position of the first element and the spacing
along a dimension as the given values.
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
- equals(SparseMatrix, SparseMatrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- equals(Object) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- equals(Object) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- equals(Object) - Method in class com.numericalmethod.suanshu.analysis.function.tuple.Pair
-
- equals(Graph<V, ?>, Graph<V, ?>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Check if two graphs are equal in terms of node values and edges.
- equals(Object) - Method in class com.numericalmethod.suanshu.interval.Interval
-
- equals(Object) - Method in class com.numericalmethod.suanshu.interval.Intervals
-
- equals(Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
- equals(Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- equals(Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.SortableArray
-
- equals(BigDecimal, BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Check if two BigDecimals are equal up to a precision.
- equals(Object) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- equals(Object) - Method in class com.numericalmethod.suanshu.number.Real
-
- equals(Object) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
-
- Erf - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The Error function is defined as:
\[
\operatorname{erf}(x) = \frac{2}{\sqrt{\pi}}\int_{0}^x e^{-t^2} dt
\]
- Erf() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.Erf
-
- Erfc - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
This complementary Error function is defined as:
\[
\operatorname{erfc}(x)
= 1-\operatorname{erf}(x)
= \frac{2}{\sqrt{\pi}} \int_x^{\infty} e^{-t^2}\,dt
\]
- Erfc() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.Erfc
-
- ErfInverse - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The inverse of the Error function is defined as:
\[
\operatorname{erf}^{-1}(x)
\]
- ErfInverse() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.ErfInverse
-
- ErgodicHybridMCMC - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid
-
A simple decorator which will randomly vary dt between each sample.
- ErgodicHybridMCMC(double, UnivariateRealFunction, AbstractHybridMCMC) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
-
Constructs a new instance where dt is given as a function.
- ErgodicHybridMCMC(double, double, RandomLongGenerator, AbstractHybridMCMC) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
-
Constructs a new instance where dt is uniformly drawn from a given range.
- error(T) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.minmax.MinMaxProblem
-
e(x, ω) is the error function, or the minmax objective, for a given ω.
- EST - Static variable in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
EST
- estimate(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleAR1Moments
-
- estimate(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleGARCHMoments1
-
- estimate(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleGARCHMoments2
-
- estimate(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.returns.moments.MomentsEstimatorLedoitWolf
-
- estimate(Matrix) - Method in interface com.numericalmethod.suanshu.finance.trading.returns.moments.ReturnsMoments.Estimator
-
Estimates the moments from a given returns matrix.
- estimate(Matrix) - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.LedoitWolf2016
-
- estimate(double[][]) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation
-
Estimate epsilon (or ACERs) from the given observations (each row is for one period).
- estimate(int) - Method in interface com.numericalmethod.suanshu.stats.random.MeanEstimator
-
Gets an estimator.
- estimate(int) - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.AntitheticVariates
-
- estimate(int) - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.CommonRandomNumbers
-
- estimate(int) - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.ControlVariates
-
- estimate(int) - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.ImportanceSampling
-
- EstimateByLogLikelihood - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting
-
Result from maximum likelihood fitting algorithm, which contains:
the log-likelihood function,
the fitted parameters for the target model,
the variance-covariance matrix,
the standard errors,
the confidence intervals.
- EstimateByLogLikelihood(Vector, RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
- estimated_lambda - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
estimated lambda
- estimatedPopulationEigenvalues(Vector) - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.TauEstimator
-
Estimates population eigenvalues from given sample eigenvalues.
- estimateForMultiPeriods(double[][]) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.ACERByCounting
-
Estimate for multiple periods.
- estimateForOnePeriod(double[]) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.ACERByCounting
-
Estimate for a single period.
- Estimator - Interface in com.numericalmethod.suanshu.stats.random
-
- eta() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
-
- EULER_MASCHERONI - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
the Euler-Mascheroni constant
- EulerMethod - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver
-
The Euler method is a first-order numerical procedure for solving ordinary differential equations
(ODEs) with a given initial value.
- EulerMethod(double) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.EulerMethod
-
Constructs an Euler's method instance, with the given step size.
- EulerMethod(int) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.EulerMethod
-
Constructs an Euler's method instance, with the given number of steps.
- EulerSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete
-
The Euler scheme is the first order approximation of an SDE.
- EulerSDE(SDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.EulerSDE
-
Discretize a continuous-time SDE using the Euler scheme.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.LinearInterpolator
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.NevilleTable
-
- evaluate(double, Vector) - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem.DerivativeFunction
-
Computes the derivative at the given point, x.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.MultivariateFiniteDifference
-
Evaluate numerically the partial derivative of f at point x.
- evaluate(Vector, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.MultivariateFiniteDifference
-
Evaluate numerically the partial derivative of f at point x with step size h.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Evaluate the function f at x, where x is from the domain.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
- evaluate(Vector, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Evaluate numerically the derivative of f at point x, f'(x), with step size h.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBeta
-
Evaluate \({\partial \over \partial x} \mathrm{B}(x, y)\).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBetaRegularized
-
Evaluate \({\partial \over \partial x} \mathrm{B_x}(p, q)\).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DErf
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGamma
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGaussian
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
-
Evaluate numerically the derivative of f at point x,
f'(x), with step size h.
- evaluate(D) - Method in interface com.numericalmethod.suanshu.analysis.function.Function
-
Evaluate the function f at x, where x is from the domain.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toConstantMatrix
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
Evaluate f(x) = A.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
Evaluate f(x1, x2) = A.
- evaluate(Number) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(Complex) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractBivariateRealFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractTrivariateRealFunction
-
- evaluate(double, double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
-
Evaluate y = f(x1,x2).
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.QuadraticFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.R1Projection
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarSubFunction
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarSubFunction
-
Evaluate the function f at x.
- evaluate(double, double, double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.TrivariateRealFunction
-
Evaluate y = f(x1,x2,x3).
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Evaluate y = f(x).
- evaluate(BigDecimal) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
-
Evaluate z.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
- evaluate(double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.UnivariateRealFunction
-
Evaluate y = f(x).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2rm.AbstractR1RnFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2rm.AbstractR1RnFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.rn2rm.RealVectorSubFunction
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.Beta
-
Evaluate B(x,y).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularized
-
Evaluate Ix(p,q).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularizedInverse
-
Evaluate \(I^{-1}_{(p,q)}(u)\).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.LogBeta
-
Compute log(B(x,y)).
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.MultinomialBetaFunction
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Digamma
-
- evaluate(double) - Method in interface com.numericalmethod.suanshu.analysis.function.special.gamma.Gamma
-
Evaluate \(\Gamma(z) = \int_0^\infty e^{-t} t^{z-1} dt\).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaGergoNemes
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczos
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczosQuick
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLowerIncomplete
-
Evaluate \(\gamma(s,x)\).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedP
-
Evaluate P(s,x).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedPInverse
-
Evaluate x = P-1(s,u).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedQ
-
Evaluate Q(s,x).
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaUpperIncomplete
-
Compute Γ(s,x).
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma
-
Evaluate the log of the Gamma function in the positive real domain.
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Trigamma
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalHastings
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalInverse
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalMarsaglia
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Erf
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Erfc
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.ErfInverse
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
- evaluate(double) - Method in interface com.numericalmethod.suanshu.analysis.function.special.gaussian.StandardCumulativeNormal
-
Evaluate \(F(x;\,\mu,\sigma^2)\).
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.analysis.function.special.Rastrigin
-
- evaluate(double) - Method in interface com.numericalmethod.suanshu.analysis.sequence.Summation.Term
-
Evaluate the term for an index.
- evaluate(double) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.Ceta
-
- evaluate(Matrix, double, double) - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMomentsEstimator
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.NegCetaFunction
-
- evaluate(Matrix, double, double) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.NPEBPortfolioMomentsEstimator
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
Note: x here is the trading size, not the position.
Evaluate the function f at x, where x is from the domain.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPZeroValue
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
Note: x here is the trading size, not the position.
Evaluate the function f at x, where x is from the domain.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
- evaluate(Constraints, Vector) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.ConstraintsUtils
-
Evaluates the constraints.
- evaluate(Matrix) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.Hp
-
Computes \(H_p(U) = \frac{1}{2}[PUP^{-1}]+P^{-*}U^*P^*\).
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Computes the final objective function value.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.CourantPenalty
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.FletcherPenalty
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.SumOfPenalties
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.ZeroPenalty
-
- evaluate(Realization[]) - Method in interface com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.F
-
F(B).
- evaluate() - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST
-
- evaluate(X) - Method in interface com.numericalmethod.suanshu.stats.distribution.discrete.ProbabilityMassFunction
-
Compute the probability mass for a discrete realization x.
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction
-
Compute the epsilon value.
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERInverseFunction
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERLogFunction
-
Compute the log-scale epsilon value at the given barrier level.
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.evt.function.ReturnLevel
-
Compute the return level from the given return period.
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.evt.function.ReturnPeriod
-
- evaluate(Vector, Vector) - Method in interface com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.MetropolisHastings.ProposalDensityFunction
-
Evaluates the density at the given points.
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
-
- evaluate(Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction.HybridMCMCProposalFunction
-
- evaluate(MultivariateFt) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantDriftVector
-
- evaluate(MultivariateFt) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
- evaluate(MultivariateFt) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix
-
Evaluate the diffusion matrix, σ(dt, Xt, Zt, ...), with respect to a filtration.
- evaluate(MultivariateFt) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
-
- evaluate(MultivariateFt) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ZeroDriftVector
-
- evaluate(MultivariateFt) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtAdaptedRealFunction
-
Evaluate this function, f, at time t.
- evaluate(MultivariateFt) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtAdaptedVectorFunction
-
Evaluate this function, f, at time t.
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Bt
-
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.FiltrationFunction
-
Compute the function value at the i-th time point, \(f(\mathfrak{F_{t_i}})\).
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.FiltrationFunction
-
- evaluate(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtAdaptedFunction
-
Evaluate this function, f, at time t.
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.XtAdaptedFunction
-
Evaluate this function, f, based on only the current value of the stochastic process.
- evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.XtAdaptedFunction
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
-
Get the i-th auto-correlation matrix.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
-
Get the i-th auto-covariance matrix.
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
Compute the auto-correlation for lag k.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCovariance
-
Compute the auto-covariance for lag k.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCovariance
-
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
- evaluate(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
Compute the partial auto-correlation for lag k.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
Get the i-th auto-correlation.
- evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
- evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
Gets the i-th auto-covariance.
- EvaluationException(String) - Constructor for exception com.numericalmethod.suanshu.analysis.function.Function.EvaluationException
-
Constructs an EvaluationException with the specified detail message.
- EvenlySpacedGrid - Class in com.numericalmethod.suanshu.stats.stochasticprocess.timegrid
-
This is an evenly spaced time grid.
- EvenlySpacedGrid(double, double, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.timegrid.EvenlySpacedGrid
-
Construct an evenly spaced time grid.
- ExceptionUtils - Class in com.numericalmethod.suanshu.misc
-
Exception-related utility functions.
- execute(Runnable) - Method in class com.numericalmethod.suanshu.misc.parallel.Mutex
-
The runnable is executed under synchronization of this
Mutex instance.
- executeAll(List<? extends Callable<T>>) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Executes a list of
Callable tasks, and returns a list of results
in the same sequential order as
tasks.
- executeAll(Callable<T>...) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Executes an arbitrary number of
Callable tasks, and returns a
list of results in the same order.
- executeAny(List<? extends Callable<T>>) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Executes a list of tasks in parallel, and returns the result from the
earliest successfully completed tasks (without throwing an exception).
- executeAny(Callable<T>...) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Executes a list of tasks in parallel, and returns the result from the
earliest successfully completed tasks (without throwing an exception).
- exp(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the exponential of a vector, element-by-element.
- exp(double) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute ex.
- exp(double, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute ex.
- exp(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute ex.
- exp(BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute ex.
- exp(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Exponential of a complex number.
- exp(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the exponentials of values.
- ExpectationAtEndTime - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random
-
This class computes the expectation (mean) and the variance of a stochastic process,
by Monte Carlo simulation, at the end of an interval: \(E(X_T)\).
- ExpectationAtEndTime(RandomRealizationGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Compute the expectation of a random process at the end time.
- ExpectationAtEndTime(RandomProcess, int, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Compute the expectation of a random process at the end time.
- ExpectationAtEndTime(SDE, double, double, int, double, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Compute the expectation of a stochastic SDE at the end time.
- expectedPnL() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.HConstruction
-
Gets the expected PnL when playing the H strategy in mean-reverting
model.
- ExplicitCentralDifference1D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1
-
This explicit central difference method is a numerical technique for solving
the one-dimensional wave equation by the following explicit
three-point central difference equation.
- ExplicitCentralDifference1D() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.ExplicitCentralDifference1D
-
- ExplicitCentralDifference2D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2
-
This explicit central difference method is a numerical technique for solving
the two-dimensional wave equation by the following explicit
three-point central difference equation.
- ExplicitCentralDifference2D() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.ExplicitCentralDifference2D
-
- ExplicitImplicitModelPCA - Class in com.numericalmethod.suanshu.stats.factor.implicitmodelpca
-
Given a time series of vectored observations, we decompose them
into a reduced dimension of linear sum of both explicit/specified and
implicit factors.
- ExplicitImplicitModelPCA(Matrix, Matrix, int) - Constructor for class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA
-
- ExplicitImplicitModelPCA(Matrix, Matrix, double) - Constructor for class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA
-
- ExplicitImplicitModelPCA.Result - Class in com.numericalmethod.suanshu.stats.factor.implicitmodelpca
-
- expm1(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the exponential-minus-one (ex - 1) of values.
- exponent() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
Get the exponent.
- Exponential - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is good for when the lower limit is finite, the upper limit is infinite, and the integrand falls off exponentially.
- Exponential(double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential
-
Construct an Exponential substitution rule.
- ExponentialDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The exponential distribution describes the times between events in a Poisson process,
a process in which events occur continuously and independently at a constant average rate.
- ExponentialDistribution() - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
Construct an instance of the standard exponential distribution, where the rate/lambda is 1.
- ExponentialDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
Construct an exponential distribution.
- ExponentialFamily - Class in com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily
-
The exponential family is an important class of probability distributions sharing this particular
form.
- ExponentialFamily(UnivariateRealFunction, RealVectorFunction, AbstractR1RnFunction, RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily.ExponentialFamily
-
Construct a factory to construct probability distribution in the exponential family of this
form.
- ExponentialMixtureDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Exponential distribution to model the observations.
- ExponentialMixtureDistribution(Double[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialMixtureDistribution
-
Constructs an Exponential distribution for each state in the HMM model.
- ExpTemperatureFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction
-
Logarithmic decay, where \(T_k = T_0 * 0.95^k\).
- ExpTemperatureFunction(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.ExpTemperatureFunction
-
Constructs a new instance with an initial temperature.
- exsec(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the exsecant of an angle.
- extractPeaks(double[]) - Static method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.ACERUtils
-
Extract peaks (values which are preceded and followed by values smaller than itself).
- extrema() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.HConstruction
-
- ExtremalGeneralizedEigenvalueByGreedySearch - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008
-
Solves
\[ \min_x \frac{x'Ax}{x'Bx} \\ \textup{s.t.,} \mathbf{Card}(x)
\leqslant k, \left \| x \right \| = 1 \]
- ExtremalGeneralizedEigenvalueByGreedySearch(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
-
Constructs the problem described in Section 3.2, d'Aspremont (2008),
changed to a minimization problem.
- ExtremalGeneralizedEigenvalueByGreedySearch(Matrix, Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
-
Constructs the problem described in Section 3.2, d'Aspremont (2008).
- ExtremalGeneralizedEigenvalueBySDP - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008
-
Solves the problem described in Section 3.2, d'Aspremont (2008).
- ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
-
Constructs the problem described in Section 3.2, d'Aspremont (2008),
changed to a minimization problem.
- ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix, boolean) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
-
Constructs the problem described in Section 3.2, d'Aspremont (2008).
- ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix, int, double, double, double, boolean) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
-
Constructs the problem described in Section 3.2, d'Aspremont (2008).
- ExtremalGeneralizedEigenvalueSolver - Interface in com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008
-
- ExtremalIndexByClusterSizeReciprocal - Class in com.numericalmethod.suanshu.stats.evt.exi
-
This class estimates the extremal index by the reciprocal of the average cluster size.
- ExtremalIndexByClusterSizeReciprocal(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.exi.ExtremalIndexByClusterSizeReciprocal
-
- ExtremalIndexByClusterSizeReciprocal(double, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.exi.ExtremalIndexByClusterSizeReciprocal
-
Create an instance with the given threshold and clustering interval length.
- ExtremalIndexByFerroSeegers - Class in com.numericalmethod.suanshu.stats.evt.exi
-
This class estimates the extremal index from observations by the algorithm proposed by Ferro and
Seegers.
- ExtremalIndexByFerroSeegers(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.exi.ExtremalIndexByFerroSeegers
-
Create an instance with the given threshold.
- ExtremalIndexEstimation - Interface in com.numericalmethod.suanshu.stats.evt.exi
-
The extremal index \(\theta \in [0,1]\) characterizes the degree of local dependence in the
extremes of a stationary time series.
- ExtremeValueMC - Class in com.numericalmethod.suanshu.stats.evt.markovchain
-
Simulation of first order extreme value Markov chains such that each pair of consecutive values
has the dependence structure of given bivariate extreme value distributions.
- ExtremeValueMC(BivariateEVD, ExtremeValueMC.MarginalDistributionType) - Constructor for class com.numericalmethod.suanshu.stats.evt.markovchain.ExtremeValueMC
-
Create an instance with a given bivariate distribution that defines the dependence structure
between two consecutive simulated values, and uses
UniformRNG for random number
generation.
- ExtremeValueMC(BivariateEVD, ExtremeValueMC.MarginalDistributionType, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.evt.markovchain.ExtremeValueMC
-
Create an instance with a given bivariate distribution that defines the dependence structure
between two consecutive simulated values, and a uniform random number generator.
- ExtremeValueMC.MarginalDistributionType - Enum in com.numericalmethod.suanshu.stats.evt.markovchain
-
Types of marginal distribution of each simulated value.
- Ey(Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
- Ey(Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
- Ey(Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyLARS
-
- Ey(Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyQP
-
- Ey(Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
-
- Ey(Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.UnconstrainedLASSObyQP
-
- Ey(Vector) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.LinearModel
-
Computes the expectation \(E(y(x))\) given an input.
- Ey(Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
-
Calculates the probability of occurrence (y = 1).
- Ey(Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
- Ey(Vector, Vector, boolean) - Static method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
- F() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem.ODE
-
Get the differential, \(y^{(n)} = F\).
- f(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
The forcing term.
- f(double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
-
Gets the value of the initial condition of u at x.
- f(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
-
Get the initial condition of u at the given point (x,y).
- f(double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the initial condition of u at a given position x.
- f(double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
-
Gets the initial condition of u at the given position x.
- f(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
-
Gets the initial condition of u at the given point (x,y).
- f - Variable in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
the original, unrestricted function
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- f - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
-
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
- F - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
the scaling factor
- f() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
Get the objective function.
- f - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- f() - Method in class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
- f() - Method in interface com.numericalmethod.suanshu.optimization.problem.OptimProblem
-
Get the objective function.
- f - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- F - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
unique dis_G_list
- F() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004.Result
-
Gets the sample constant correlation matrix F.
- F(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
-
Gets F(t), the coefficient matrix of xt.
- F(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.ObservationEquation
-
Get F(t), the coefficient of xt.
- F() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets F, the implicit factor value matrix.
- f(int) - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets the implicit factor values at time t.
- F() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets F, the factor value matrix.
- f(int) - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets the factor values at time t.
- f() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData.Transformation
-
Gets the transformation.
- f - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integral
-
the integrand
- F - Class in com.numericalmethod.suanshu.stats.test.variance
-
The F-test tests whether two normal populations have the same variance.
- F(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.F
-
Perform the F-test to test for equal variance of two normal populations.
- F(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.F
-
Perform the F-test to test for equal variance of two normal populations.
- F_idx - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
indices for dis_G_list which is unique
- F_Sum_BtDt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration
-
This represents a function of this integral
\[
I = \int_{0}^{1} B(t)dt
\]
- F_Sum_BtDt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.F_Sum_BtDt
-
- F_Sum_tBtDt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration
-
This represents a function of this integral
\[
\int_{0}^{1} (t - 0.5) * B(t) dt
\]
- F_Sum_tBtDt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.F_Sum_tBtDt
-
- FactorAnalysis - Class in com.numericalmethod.suanshu.stats.factor.factoranalysis
-
Factor analysis is a statistical method used to describe variability among
observed variables in terms of a potentially lower number of unobserved
variables called factors.
- FactorAnalysis(Matrix, int, FactorAnalysis.ScoringRule, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Performs factor analysis on the data set with a user defined scoring rule
and a user defined covariance (or correlation) matrix.
- FactorAnalysis(Matrix, int, FactorAnalysis.ScoringRule) - Constructor for class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Performs factor analysis on the data set with a user defined scoring
rule.
- FactorAnalysis(Matrix, int) - Constructor for class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Performs factor analysis on the data set, using Bartlett's weighted
least-squares scores, and sample correlation matrix.
- FactorAnalysis.ScoringRule - Enum in com.numericalmethod.suanshu.stats.factor.factoranalysis
-
These are the different ways to compute the factor analysis scores.
- factorial(int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
n!
- factorial(int) - Static method in class com.numericalmethod.suanshu.number.big.BigIntegerUtils
-
Compute the n factorial.
- factory - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- factoryCtor - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- FAEstimator - Class in com.numericalmethod.suanshu.stats.factor.factoranalysis
-
These are the estimators (estimated psi, loading matrix, scores, degrees of
freedom, test statistics, p-value, etc.) from the factor analysis MLE
optimization.
- family() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMProblem
-
Gets the quasi-family specification.
- FARADAY_F - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The Faraday constant \(F\) in coulombs per mole (C mol-1).
- FastAnnealingFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction
-
Matlab default: @annealingfast - The step has length temperature, with direction uniformly at
random.
- FastAnnealingFunction(int, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.FastAnnealingFunction
-
Constructs a new instance where the RVG is created from a given RLG.
- FastAnnealingFunction(RandomVectorGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.FastAnnealingFunction
-
Constructs a new instance that uses a given RVG.
- FastKroneckerProduct - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
This is a fast and memory-saving implementation of computing the Kronecker product.
- FastKroneckerProduct(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
Construct a Kronecker product for read-only.
- FastTemperatureFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction
-
Linear decay, where \(T_k = T_0 / k\).
- FastTemperatureFunction(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.FastTemperatureFunction
-
Constructs a new instance with an initial temperature.
- FDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The F distribution is the distribution of the ratio of two independent chi-squared variates.
- FDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
Construct an F distribution.
- fdx(UnivariateRealFunction) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
-
Get the integrand in the "transformed" integral, g(t) = f(x(t)) * x'(t).
- FerrisMangasarianWrightPhase1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
The phase 1 procedure finds a feasible table from an infeasible one by pivoting the simplex table of a related problem.
- FerrisMangasarianWrightPhase1(SimplexTable) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightPhase1
-
Construct the phase 1 algorithm for an infeasible table corresponding to a non-standard linear programming problem, e.g., b ≥ 0.
- FerrisMangasarianWrightPhase2 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
This implementation solves a canonical linear programming problem that does not need preprocessing its simplex table.
- FerrisMangasarianWrightPhase2(SimplexPivoting) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
Construct an LP solver to solve canonical LP problems using the Phase 2 algorithm in Ferris, Mangasarian & Wright.
- FerrisMangasarianWrightPhase2() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
Construct an LP solver to solve canonical LP problems using the Phase 2 algorithm in Ferris, Mangasarian & Wright.
- FerrisMangasarianWrightScheme2 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
The scheme 2 procedure removes equalities and free variables.
- FerrisMangasarianWrightScheme2(SimplexTable) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightScheme2
-
Construct the scheme 2 algorithm for a table with equalities and free variables.
- Fibonacci - Class in com.numericalmethod.suanshu.analysis.sequence
-
A Fibonacci sequence starts with 0 and 1 as the first two numbers.
- Fibonacci(int) - Constructor for class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
-
Construct a Fibonacci sequence.
- FibonaccMinimizer - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
The Fibonacci search is a dichotomous search where a bracketing interval is sub-divided by the Fibonacci ratio.
- FibonaccMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.FibonaccMinimizer
-
Construct a univariate minimizer using the Fibonacci method.
- FibonaccMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This is the solution to a Fibonacci's univariate optimization.
- Field<F> - Interface in com.numericalmethod.suanshu.algebra.structure
-
As an algebraic structure, every field is a ring, but not every ring is a field.
- Field.InverseNonExistent - Exception in com.numericalmethod.suanshu.algebra.structure
-
This is the exception thrown when the inverse of a field element does not exist.
- fillInStackTrace() - Method in error com.numericalmethod.suanshu.misc.license.LicenseError
-
- Filter - Interface in com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
-
A filter, for signal processing, takes (real) input signal and transforms it to (real) output signal.
- filtering(MultivariateIntTimeTimeSeries, MultivariateIntTimeTimeSeries) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Filter the observations.
- filtering(MultivariateIntTimeTimeSeries) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Filter the observations without control variable.
- filtering(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Filter the observations.
- filtering(double[]) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Filter the observations without control variable.
- filterPrices(Matrix) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.RobustCointegration
-
Filters out invalid prices.
- filterUnchangedPrices(Matrix) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.RobustCointegration
-
Filters out prices that are unchanged between consecutive times.
- Filtration - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration
-
This class represents the filtration information known at the end of time.
- Filtration(UnivariateTimeSeries<Double, ? extends UnivariateTimeSeries.Entry<Double>>) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Construct a Filtration from a Brownian path.
- FiltrationFunction - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration
-
A filtration function, parameterized by a fixed filtration, is a function of time,
\(f(\mathfrak{F_{t_i}})\).
- FiltrationFunction() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.FiltrationFunction
-
- findClusters(Matrix, double) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModelUtils
-
- findPosition(VertexTree<T>) - Static method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
Finds the position of a node from its tree root recursively.
- FINE_STRUCTURE_ALPHA - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The fine-structure constant \(\alpha\) (dimensionless).
- finishTime - Variable in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node
-
- finishTime() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node
-
Gets the finish time, the time to finish visiting its sub-tree, of this node.
- FiniteDifference - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
A finite difference (divided by a small increment) is an approximation of the
derivative of a function.
- FiniteDifference(UnivariateRealFunction, int, FiniteDifference.Type) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
-
Construct an approximate derivative function for f using finite
difference.
- FiniteDifference.Type - Enum in com.numericalmethod.suanshu.analysis.differentiation.univariate
-
the available types of finite difference
- FirstGeneration - Interface in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
-
This interface allows customization of how the first pool of chromosomes is generated.
- FirstOrderMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
-
This implements the steepest descent line search using the first order expansion of the Taylor's series.
- FirstOrderMinimizer(FirstOrderMinimizer.Method, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
-
Construct a multivariate minimizer using the First-Order method.
- FirstOrderMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
-
Construct a multivariate minimizer using the First-Order method.
- FirstOrderMinimizer.Method - Enum in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
-
the available methods to do line search
- FisherExactDistribution - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
-
Fisher's exact test distribution is, as its name states, exact, and can therefore be used
regardless of the sample characteristics.
- FisherExactDistribution(int[], int[], int, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.FisherExactDistribution
-
Construct the distribution for Fisher's exact test.
- FisherExactDistribution(int[], int[], int) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.FisherExactDistribution
-
Construct the distribution for Fisher's exact test.
- fit(OrderedPairs) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.CurveFitting
-
Fit a real valued function from a discrete set of data points.
- fit(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicHermite
-
- fit(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicSpline
-
- fit(OrderedPairs) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.Interpolation
-
Fit a real valued function from a discrete set of data points.
- fit(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.LinearInterpolation
-
- fit(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.NewtonPolynomial
-
- fit(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.curvefit.LeastSquares
-
- fit - Variable in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- fit(double[], double[], double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.LinearFit
-
Fit the ACER function with OLS.
- fit(EmpiricalACER, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit
-
Fit ACER function with empirical ACER estimates.
- fit(double[], double[], double[], double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit
-
Fit the ACER function with the input values of barrier levels, epsilons, confidence
intervals, and the mean of the peaks.
- fit(double[]) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.GEVFittingByMaximumLikelihood
-
Find the best-fit GEV parameters (location, scale, shape) by minimizing the negative
log-likelihood.
- fit(double[]) - Method in interface com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.MaximumLikelihoodFitting
-
Fit the model with the given observations.
- fit(double[]) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot.PeaksOverThreshold
-
Fits the observations to a generalized Pareto distribution (GPD).
- fit(double[]) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot.PeaksOverThresholdOnClusters
-
- fit(GLMProblem, Vector) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.GLMFitting
-
Fits a Generalized Linear Model.
- fit(GLMProblem, Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
- fit(GLMProblem, Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
- fitModel(double[]) - Method in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995MLE
-
Fits a KST model from returns.
- fitness() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.Chromosome
-
This is the fitness to determine how good this chromosome is.
- fitness() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
- fitted() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the fitted values, y^.
- fittedValues() - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.ResamplerModel
-
- fittedValues(OLSResiduals[]) - Static method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.ResamplerModel
-
- fittedValues() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleAR1Fit
-
- fittedValues() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleGARCHFit
-
- fitWithWeights(double[], double[], double[], double, double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit
-
Fit the ACER function with the input values of barrier levels, epsilons, confidence
intervals, and the mean of the peaks.
- fitWithWeightsAndInitial(double[], double[], double[], ACERFunction.ACERParameter, double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit
-
- FixedEffectsModel - Class in com.numericalmethod.suanshu.stats.regression.linear.panel
-
Fits the panel data to this linear model:
\[
y_{it} = \alpha_{i}+X_{it}\mathbf{\beta}+u_{it}
\]
where \(y_{it}\) is the dependent variable observed for individual \(i\) at
time \(t\), \(X_{it}\) is the time-variant \(1\times K\) regressor matrix,
\(\alpha_{i}\) is the unobservable time-invariant individual effect and
\(u_{it}\) is the error term.
- FixedEffectsModel(PanelData, String, String[], PanelData.Transformation[]) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.panel.FixedEffectsModel
-
Constructs a "within" fixed effects model from a panel of data.
- FixedEffectsModel(PanelData, String, String[]) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.panel.FixedEffectsModel
-
Constructs a "within" fixed effects model from a panel of data.
- fixing - Variable in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
the restrictions or fixed values
- fixing() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.ConstrainedOptimSubProblem
-
Gets the restrictions or fixed values;
- FletcherLineSearch - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch
-
This is Fletcher's inexact line search method.
- FletcherLineSearch(double, double, double, double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch.FletcherLineSearch
-
Construct a line search minimizer using the Fletcher method.
- FletcherLineSearch() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch.FletcherLineSearch
-
Construct a line search minimizer using the Fletcher method with the
default control parameters.
- FletcherPenalty - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the squared costs penalties.
- FletcherPenalty(LessThanConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.FletcherPenalty
-
Construct a Fletcher penalty function from a collection of inequality constraints.
- FletcherPenalty(LessThanConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.FletcherPenalty
-
Construct a Fletcher penalty function from a collection of inequality constraints.
- FletcherPenalty(LessThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.FletcherPenalty
-
Construct a Fletcher penalty function from a collection of inequality constraints.
- FletcherReevesMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection
-
The Fletcher-Reeves method is a variant of the Conjugate-Gradient method.
- FletcherReevesMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.FletcherReevesMinimizer
-
Construct a multivariate minimizer using the Fletcher-Reeves method.
- FlexibleTable - Class in com.numericalmethod.suanshu.misc.datastructure
-
This is a 2D table that can shrink or grow by row or by column.
- FlexibleTable(Object[], Object[], double[][]) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Constructs a flexible table that can shrink or grow.
- FlexibleTable(Object[], Object[]) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Constructs a table by row and column labels, initializing the content to 0.
- FlexibleTable(int, int) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Constructs a table using default labeling.
- FlexibleTable(FlexibleTable) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Copy constructor.
- FloatingLicenseServer - Class in com.numericalmethod.suanshu.misc.license
-
- floatValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- floatValue() - Method in class com.numericalmethod.suanshu.number.Real
-
- floatValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
- Fmin(double, int) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Compute the F critical value.
- fmin - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- fmin - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the best minimum found so far
- fminLast - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- fnext - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the next guess of the minimum
- foreach(Matrix, UnivariateRealFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a new matrix in which each entry is the result of applying a function to the
corresponding entry of a matrix.
- foreach(Vector, UnivariateRealFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Constructs a new vector in which each entry is the result of applying a
function to the corresponding entry of a vector.
- foreach(SparseVector, UnivariateRealFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Constructs a new vector in which each entry is the result of applying a
function to the corresponding entry of a sparse vector.
- forEach(Iterable<T>, IterationBody<T>) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Runs a "foreach" loop in parallel.
- foreach(double[], UnivariateRealFunction) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Apply a univariate function f to each element in an array.
- foreachColumn(Matrix, RealVectorFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a new matrix in which each column is the result of applying a real vector function
on each column vector of an input matrix.
- foreachColumn(Matrix, RealScalarFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Constructs a vector in which each entry is the result of applying a
RealScalarFunction to each column of an input matrix.
- foreachRow(Matrix, RealVectorFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a new matrix in which each row is the result of applying a real vector function on
each row vector of an input matrix.
- foreachRow(Matrix, RealScalarFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Constructs a vector in which each entry is the result of applying a
RealScalarFunction to each row of an input matrix.
- foreachVector(Vector[], RealScalarFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
- foreachVector(Collection<Vector>, RealScalarFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
- foreachVector(Vector[], RealVectorFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Applies a real vector function on each input vector.
- foreachVector(Collection<Vector>, RealVectorFunction) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Applies a real vector function on each input vector.
- Forecast(int, double, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
- Forest<V,E extends HyperEdge<V>> - Interface in com.numericalmethod.suanshu.graph
-
A forest is a disjoint union of trees.
- forLagOrder(int) - Method in interface com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.AutoCorrelationForObject
-
- forLagOrder(int) - Method in interface com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.AutoCovarianceForObject
-
- forLoop(int, int, LoopBody) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
- forLoop(int, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Runs a for-loop in parallel.
- forward(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
-
Perform a forward sweep.
- ForwardBackwardProcedure - Class in com.numericalmethod.suanshu.stats.hmm
-
The forward-backward procedure is an inference algorithm for hidden Markov
models which computes the posterior marginals of all hidden state variables
given a sequence of observations.
- ForwardBackwardProcedure(HiddenMarkovModel, double[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.ForwardBackwardProcedure
-
Constructs the forward and backward probability matrix calculator for an
HMM model.
- ForwardBackwardProcedure(HiddenMarkovModel, int[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.ForwardBackwardProcedure
-
Constructs the forward and backward probability matrix calculator for an
HMM model.
- ForwardSelection - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
Constructs a GLM model for a set of observations using the forward selection method.
- ForwardSelection(GLMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.ForwardSelection
-
Constructs a GLM model using the forward selection method, with
SelectionByAIC
as the default algorithm.
- ForwardSelection(GLMProblem, ForwardSelection.Step) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.ForwardSelection
-
Constructs a GLM model using the forward selection method.
- ForwardSelection.Step - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
- ForwardSubstitution - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
-
Forward substitution solves a matrix equation in the form Lx = b
by an iterative process for a lower triangular matrix L.
- ForwardSubstitution() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
- foundPositiveDefiniteHessian - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- fractionOfYear(LocalDate, LocalDate, double) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Computes a fraction, in years, based on the number of days between two
given dates.
- FrechetDistribution - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate
-
The Fréchet distribution is a special case (Type II) of the generalized extreme value
distribution, with \(\xi>0\).
- FrechetDistribution() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.FrechetDistribution
-
Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\),
shape \(\alpha=1\).
- FrechetDistribution(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.FrechetDistribution
-
Create an instance with the given parameter values.
- Frobenius(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the Frobenius norm, i.e., the sqrt of the sum of squares of all elements of a matrix.
- fromContext(HouseholderContext, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace.Householder
-
- fromPolar(double, double) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
-
Factory method to construct a complex number from the polar form: (r, θ).
- Fstat() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: F statistics
- Ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.FiltrationFunction
-
Compute all function values at all time points.
- ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integral
-
Get an array of the function values.
- ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDB
-
- ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDt
-
- Ft - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
This represents the concept 'Filtration', the information available at time t.
- Ft() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Construct an empty filtration (no information).
- Ft(Ft) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Copy constructor.
- FtAdaptedFunction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
This represents an Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.
- FtAdaptedRealFunction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This represents an Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.
- FtAdaptedVectorFunction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This represents a vector-valued Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.
- FtWt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
This is a filtration implementation that includes the path-dependent information,
Wt.
- FtWt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
Construct an empty filtration (no information).
- FtWt(FtWt) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
Copy constructor.
- fullMinimizer() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer.IterativeSolution
-
Gets the minimizer to the original problem.
- Function<D,R> - Interface in com.numericalmethod.suanshu.analysis.function
-
The mathematical concept of a function expresses the idea that one quantity (the argument of the function, also known as the input) completely determines another quantity (the value, or output).
- Function.EvaluationException - Exception in com.numericalmethod.suanshu.analysis.function
-
- FunctionOps - Class in com.numericalmethod.suanshu.analysis.function
-
These are some commonly used mathematical functions.
- fx() - Method in exception com.numericalmethod.suanshu.analysis.root.univariate.NoRootFoundException
-
Get f(x).
- g(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
The boundary value function.
- g(double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
-
Gets the value of the initial condition of the time derivative of u at x.
- g(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
-
Get the initial condition of the time derivative of u at the given point (x,y).
- g(double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
-
Gets the boundary condition at the given boundary point (x,y) at the given time point
t.
- g() - Method in interface com.numericalmethod.suanshu.analysis.differentiation.differentiability.C1
-
Get the gradient function, g, of a real valued function f.
- g - Variable in class com.numericalmethod.suanshu.graph.algorithm.traversal.TraversalFromRoots
-
- g() - Method in class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
-
- G(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
-
Gets G(t), the coefficient matrix of xt - 1.
- G(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Get G(t), the coefficient of xt - 1.
- g1(double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
The value of the linear combination of \(u\) and \(\frac{\partial
u}{\partial x}\) at the boundary \(x = 0\) at a given time \(t\).
- g2(double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
The value of the linear combination of \(u\) and \(\frac{\partial
u}{\partial x}\) at the boundary \(x = a\) at the given time \(t\).
- g2(double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
-
Gets the value of the linear combination of \(u\) and \(\frac{\partial u}{\partial x}\) at
the
boundary \(x = a\) at the given time \(t\).
- Gamma - Interface in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Gamma function is an extension of the factorial function to real and complex numbers, with its argument shifted down by 1.
- gamma - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- Gamma() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets Γ, the explicit factor loading matrix.
- gamma(HiddenMarkovModel, int[], Matrix[]) - Static method in class com.numericalmethod.suanshu.stats.hmm.discrete.BaumWelch
-
Gets the (T-1 * N) γ matrix, where the (t, i)-th
entry is γt(i).
- gamma() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the estimated sequence of gamma (arc length).
- gamma(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the AR coefficient of the i-th lagged differences.
- gamma() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the AR coefficients of the lagged differences; null if p = 1
- GammaDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
This gamma distribution, when k is an integer, is the distribution of
the sum of k independent exponentially distributed random variables,
each of which has a mean of θ (which is equivalent to a rate parameter of
θ-1).
- GammaDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
Construct a Gamma distribution.
- GammaGergoNemes - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Gergo Nemes' algorithm is very simple and quick to compute the Gamma function, if accuracy is not critical.
- GammaGergoNemes() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaGergoNemes
-
- GammaLanczos - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
Lanczos approximation provides a way to compute the Gamma function such that the accuracy can be made arbitrarily precise.
- GammaLanczos() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczos
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLanczos(double, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczos
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLanczosQuick - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
Lanczos approximation, computations are done in double.
- GammaLanczosQuick() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczosQuick
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLanczosQuick(double, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLanczosQuick
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLowerIncomplete - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Lower Incomplete Gamma function is defined as:
\[
\gamma(s,x) = \int_0^x t^{s-1}\,e^{-t}\,{\rm d}t = P(s,x)\Gamma(s)
\]
P(s,x) is the Regularized Incomplete Gamma P function.
- GammaLowerIncomplete() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaLowerIncomplete
-
- GammaMixtureDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Gamma distribution to model the observations.
- GammaMixtureDistribution(GammaMixtureDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaMixtureDistribution
-
Constructs a Gamma distribution for each state in the HMM model.
- GammaMixtureDistribution(GammaMixtureDistribution.Lambda[], int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaMixtureDistribution
-
Constructs a Gamma distribution for each state in the HMM model.
- GammaMixtureDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the Gamma distribution parameters
- GammaRegularizedP - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Regularized Incomplete Gamma P function is defined as:
\[
P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0
\]
The R equivalent function is
pgamma.
- GammaRegularizedP() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedP
-
- GammaRegularizedPInverse - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The inverse of the Regularized Incomplete Gamma P function is defined as:
\[
x = P^{-1}(s,u), 0 \geq u \geq 1
\]
When
s > 1, we use the asymptotic inversion method.
When
s <= 1, we use an approximation of
P(s,x) together with a higher-order Newton like method.
In both cases, the estimated value is then improved using Halley's method, c.f.,
HalleyRoot.
- GammaRegularizedPInverse() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedPInverse
-
- GammaRegularizedQ - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Regularized Incomplete Gamma Q function is defined as:
\[
Q(s,x)=\frac{\Gamma(s,x)}{\Gamma(s)}=1-P(s,x), s \geq 0, x \geq 0
\]
The algorithm used for computing the regularized incomplete Gamma Q function depends on the values of s and x.
- GammaRegularizedQ() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaRegularizedQ
-
- GammaUpperIncomplete - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Upper Incomplete Gamma function is defined as:
\[
\Gamma(s,x) = \int_x^{\infty} t^{s-1}\,e^{-t}\,{\rm d}t = Q(s,x) \times \Gamma(s)
\]
The integrand has the same form as the Gamma function, but the lower limit of the integration is a variable.
- GammaUpperIncomplete() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.GammaUpperIncomplete
-
- GARCH11Model - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
An GARCH11 model takes this form.
- GARCH11Model(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
-
Construct a GARCH(1,1) model.
- GARCHFit - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
This implementation fits, for a data set, a Generalized Autoregressive Conditional
Heteroscedasticity (GARCH) model
by maximizing the likelihood function using the gradient information.
- GARCHFit(double[], int, int, double, int, GARCHFit.GRADIENT) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit
-
Fit a GARCH(p, q) model to a time series.
- GARCHFit(double[], int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit
-
Fit a GARCH(p, q) model to a time series.
- GARCHFit.GRADIENT - Enum in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
the available methods to compute the gradient to guild the optimization search
- GARCHModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
The GARCH(p, q) model takes this form.
- GARCHModel(double, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Construct a GARCH model.
- GARCHModel(GARCHModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Copy constructor.
- GARCHResamplerFactory - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler
-
- GARCHResamplerFactory(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
-
- GARCHResamplerFactory() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
-
- GARCHResamplerFactory2 - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler
-
- GARCHResamplerFactory2(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- GARCHResamplerFactory2() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- GARCHSim - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
-
This class simulates the GARCH models of this form.
- GARCHSim(GARCHModel, double[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Simulate an GARCH model.
- GARCHSim(GARCHModel, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Simulate an GARCH model.
- GARCHSim(GARCHModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Simulate an GARCH model.
- GaussChebyshevQuadrature - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian
-
Gauss-Chebyshev Quadrature uses the following weighting function:
\[
w(x) = \frac{1}{\sqrt{1 - x^2}}
\]
to evaluate integrals in the interval (-1, 1).
- GaussChebyshevQuadrature(int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.GaussChebyshevQuadrature
-
Create an integrator of order n.
- GaussHermiteQuadrature - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian
-
Gauss-Hermite quadrature exploits the fact that quadrature approximations are open integration
formulas (that is, the values of the endpoints are not required) to evaluate of integrals in the
range \((-\infty, \infty )\).
- GaussHermiteQuadrature(int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.GaussHermiteQuadrature
-
Create an integrator of order n.
- Gaussian - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The Gaussian function is defined as:
\[
f(x) = a e^{- { \frac{(x-b)^2 }{ 2 c^2} } }
\]
- Gaussian(double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Construct an instance of the Gaussian function.
- Gaussian() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
-
Construct an instance of the standard Gaussian function: \(f(x) = e^{-{\frac{(x)^2}{2}}}\)
- GaussianElimination - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination
-
The Gaussian elimination performs elementary row operations to reduce a matrix to the row echelon form.
- GaussianElimination(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Run the Gaussian elimination algorithm.
- GaussianElimination(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Run the Gaussian elimination algorithm with partial pivoting.
- GaussianElimination4SquareMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination
-
- GaussianElimination4SquareMatrix(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
Run the Gaussian elimination algorithm on a square matrix.
- GaussianElimination4SquareMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
Run the Gaussian elimination algorithm on a square matrix.
- GaussianProposalFunction - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction
-
A proposal generator where each perturbation is a random vector, where each element is drawn
from a standard Normal distribution, multiplied by a scale matrix.
- GaussianProposalFunction(Matrix, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
-
Constructs a Gaussian proposal function.
- GaussianProposalFunction(double[], RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
-
Constructs a Gaussian proposal function.
- GaussianProposalFunction(double, int, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
-
Constructs a Gaussian proposal function.
- GaussianQuadrature - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian
-
A quadrature rule is a method of numerical integration in which we approximate the integral of a
function by a weighted sum of sample points.
- GaussianQuadrature(GaussianQuadratureRule) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature
-
Create a Gaussian quadrature integrator with the given quadrature rule.
- GaussianQuadratureRule - Interface in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule
-
This interface defines a Gaussian quadrature rule used in Gaussian quadrature.
- GaussJordanElimination - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination
-
Gauss-Jordan elimination performs elementary row operations to reduce a matrix to the reduced row echelon form.
- GaussJordanElimination(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Run the Gauss-Jordan elimination algorithm.
- GaussJordanElimination(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Run the Gauss-Jordan elimination algorithm.
- GaussLaguerreQuadrature - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian
-
Gauss-Laguerre quadrature exploits the fact that quadrature approximations are open integration
formulas (i.e.
- GaussLaguerreQuadrature(int, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.GaussLaguerreQuadrature
-
Create an integrator of order n.
- GaussLegendreQuadrature - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian
-
Gauss-Legendre quadrature considers the simplest case of uniform weighting: \(w(x) = 1\).
- GaussLegendreQuadrature(int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.GaussLegendreQuadrature
-
Create an integrator of order n.
- GaussNewtonImpl(C2OptimProblem, RntoMatrix) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl
-
- GaussNewtonMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
-
The Gauss-Newton method is a steepest descent method to minimize a real vector function in the form:
/[
f(x) = [f_1(x), f_2(x), ..., f_m(x)]'
/]
The objective function is
/[
F(x) = f' %*% f
]/
- GaussNewtonMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
-
Construct a multivariate minimizer using the Gauss-Newton method.
- GaussNewtonMinimizer.MySteepestDescent - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
-
- GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
-
an implementation of the Gauss-Newton algorithm.
- GaussSeidelSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
Similar to the
Jacobi method, the Gauss-Seidel method (GS)
solves each equation in sequential order.
- GaussSeidelSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
Construct a Gauss-Seidel (GS) solver.
- GBMProcess - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process
-
A Geometric Brownian motion (GBM) (occasionally, exponential Brownian motion) is
a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion.
- GBMProcess(double, double) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.GBMProcess
-
Construct a Geometric Brownian motion.
- gcd(int, int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Calculates the greatest common divisor of integer a and integer b.
- GeneralConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general
-
The real-valued constraints define the domain (feasible regions) for a real-valued objective
function in a constrained optimization problem.
- GeneralConstraints(Collection<RealScalarFunction>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralConstraints
-
Construct an instance of constraints from a collection of real-valued functions.
- GeneralConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralConstraints
-
Construct an instance of constraints from an array of real-valued functions.
- GeneralEqualityConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general
-
This is the collection of equality constraints for an optimization problem.
- GeneralEqualityConstraints(Collection<RealScalarFunction>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralEqualityConstraints
-
Constructs an instance of equality constraints from a collection of
real-valued functions.
- GeneralEqualityConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralEqualityConstraints
-
Constructs an instance of equality constraints from an array of
real-valued functions.
- GeneralGreaterThanConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general
-
This is the collection of greater-than-or-equal-to constraints for an optimization problem.
- GeneralGreaterThanConstraints(Collection<RealScalarFunction>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralGreaterThanConstraints
-
Construct an instance of greater-than-or-equal-to inequality constraints from a collection of
real-valued functions.
- GeneralGreaterThanConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralGreaterThanConstraints
-
Construct an instance of greater-than-or-equal-to inequality constraints from an array of
real-valued functions.
- GeneralizedConjugateResidualSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Generalized Conjugate Residual method (GCR) is useful for solving
a non-symmetric n-by-n linear system.
- GeneralizedConjugateResidualSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
Construct a GCR solver with restarts.
- GeneralizedConjugateResidualSolver(int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
Construct a GCR solver with restarts.
- GeneralizedConjugateResidualSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
Construct a full GCR solver.
- GeneralizedEVD - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate
-
Generalized extreme value (GEV) distribution is a family of continuous probability distributions
developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families
also known as type I, II and III extreme value distributions.
- GeneralizedEVD() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
Create an instance of generalized extreme value distribution with the default parameter
values: location \(\mu=0\), scale \(\sigma=1\), shape \(\xi=0\).
- GeneralizedEVD(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
Create an instance of generalized extreme value distribution with the given parameters.
- GeneralizedLinearModel - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
The Generalized Linear Model (GLM) is a flexible generalization of the Ordinary Least Squares
regression.
- GeneralizedLinearModel(GLMProblem, GLMFitting) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
Constructs a GeneralizedLinearModel instance.
- GeneralizedLinearModel(GLMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
Solves a generalized linear problem using the Iterative Re-weighted Least Squares algorithm.
- GeneralizedLinearModelQuasiFamily - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi
-
GLM for the quasi-families.
- GeneralizedLinearModelQuasiFamily(QuasiGLMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
Constructs a GeneralizedLinearModelQuasiFamily instance.
- GeneralizedMinimalResidualSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Generalized Minimal Residual method (GMRES) is useful for solving a non-symmetric n-by-n
linear system.
- GeneralizedMinimalResidualSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
Construct a GMRES solver with restarts.
- GeneralizedMinimalResidualSolver(int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
Construct a GMRES solver with restarts.
- GeneralizedMinimalResidualSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
Construct a full GMRES solver.
- GeneralizedParetoDistribution - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate
-
Generalized Pareto distribution (GPD) is used for modeling exceedances over (or shortfalls below)
a threshold.
- GeneralizedParetoDistribution() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\),
shape \(\xi=0\).
- GeneralizedParetoDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
Create an instance with zero location, and the given scale and shape parameters.
- GeneralizedParetoDistribution(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
Create an instance with the given parameter values.
- GeneralizedSimulatedAnnealingMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing
-
- GeneralizedSimulatedAnnealingMinimizer(int, double, double, double, StopCondition, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the Generalized Simulated Annealing minimizer.
- GeneralizedSimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the Generalized Simulated Annealing minimizer with the
recommended visiting and acceptance parameter.
- GeneralizedSimulatedAnnealingMinimizer(int, StopCondition) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the Generalized Simulated Annealing minimizer.
- GeneralLessThanConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general
-
This is the collection of less-than or equal-to constraints for an optimization problem.
- GeneralLessThanConstraints(Collection<RealScalarFunction>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralLessThanConstraints
-
Construct an instance of less-than or equal-to inequality constraints from a collection of real-valued functions.
- GeneralLessThanConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralLessThanConstraints
-
Construct an instance of less-than or equal-to inequality constraints from an array of real-valued functions.
- generateAndReflectColumns(int, int, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Reflects a range of sub-columns with a Householder generated by the first column in the
range.
- generateAndReflectRows(int, int, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Reflects a range of sub-rows with a Householder generated by the first row in the range.
- generateLeftHouseholder(int, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Generates a left Householder from a sub-column of the underlying matrix, in order to zero out
entries (except the first entry) of the sub-column.
- generateRightHouseholder(int, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Generates a right Householder from a sub-row of the underlying matrix, in order to zero out
entries (except the first entry) of the sub-row.
- generator - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
The vector which is used to generate the Householder vector.
- GenericFieldMatrix<F extends Field<F>> - Class in com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype
-
This is a generic matrix over a
Field.
- GenericFieldMatrix(int, int, F) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
Construct a matrix over a field.
- GenericFieldMatrix(F[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
Construct a matrix over a field.
- GenericMatrix<T extends GenericMatrix<T,F>,F extends Field<F>> - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.generic
-
This class defines a matrix over a field.
- GenericMatrixAccess<F extends Field<F>> - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.generic
-
This interface defines the methods for accessing entries in a matrix over a field.
- GenericTimeTimeSeries<T extends Comparable<? super T>> - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate
-
This is a univariate time series indexed by some notion of time.
- GenericTimeTimeSeries(T[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Construct a univariate time series from timestamps and values.
- GeneticAlgorithm - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm
-
A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution.
- GeneticAlgorithm(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Construct an instance of this implementation of genetic algorithm.
- geometricMultiplicity() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get the dimension of the vector space spanned by the eigenvectors.
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- get(int, int) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixAccess
-
Get the matrix entry at [i,j].
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- get(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets the value of an entry at (i,j) in the transformed matrix.
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- get(int, int) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.generic.GenericMatrixAccess
-
Get the matrix entry at [i,j].
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- get(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- get(int) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.CombinedVectorByRef
-
- get(int) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- get(int) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- get(int) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- get(int) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Get the value at position i.
- get(int, int) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.NevilleTable
-
Get the value of a table entry.
- get(int) - Method in class com.numericalmethod.suanshu.analysis.function.tuple.SortedOrderedPairs
-
Get the ordered pair at index i.
- get(int) - Method in class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
-
- get(int) - Method in interface com.numericalmethod.suanshu.analysis.sequence.Sequence
-
Get the i-th entry in the sequence, counting from 1.
- get(T) - Method in class com.numericalmethod.suanshu.combinatorics.Ties
-
Get the number of occurrences of an object.
- get(int) - Method in class com.numericalmethod.suanshu.interval.Intervals
-
Get the i-th interval.
- get(int, int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
- get(double, int) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Gets a particular table entry at [i,j].
- get(double, String) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Gets a particular table entry at [i, "header"].
- get(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable.Row
-
Gets the value in the row by column index.
- get(String) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable.Row
-
Gets the value in the row by column name.
- get(int...) - Method in class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalArray
-
- get(int...) - Method in interface com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalCollection
-
Returns the element at the specified position in this collection.
- get(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.SortableArray
-
- get() - Method in class com.numericalmethod.suanshu.misc.parallel.Reference
-
- get(String) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData.Row
-
- get(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Get the i-th value.
- get(int) - Method in interface com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateIntTimeTimeSeries
-
Get the value at time t (random access).
- get(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- get(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Get the i-th value.
- get(T) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Get the value at time t.
- get(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- get(int) - Method in interface com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.IntTimeTimeSeries
-
Get the value at time t.
- get(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- get(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction
-
Get the auto-correlation of Xi and Xj.
- get(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.MultivariateAutoCovarianceFunction
-
Get the auto-covariance matrix for Xi and Xj.
- get(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCorrelationFunction
-
Get the auto-correlation of xi and xj.
- get(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCovarianceFunction
-
Get the auto-covariance of xi and xj.
- get0s(int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets n 0 vectors.
- getAaa(int) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getACERs() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the estimated epsilon values for different barrier levels per period.
- getActive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Get the i-th active index.
- getActiveConstraints(Vector, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
Get the active constraint.
- getActiveIndices() - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Get all active indices.
- getActiveRows(Vector, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
Get the active constraint indices.
- getAll() - Method in class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
-
- getAll() - Method in interface com.numericalmethod.suanshu.analysis.sequence.Sequence
-
Get a copy of the whole (finite) sequence in double[].
- getAllParts(Vector, Map<Integer, Double>) - Static method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
Combines the variable and fixed values to form an input to the original
function.
- getAllSubjects() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the name of all subjects.
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FAEstimator
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarling
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.CramerVonMises2Samples
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get the description of the alternative hypothesis.
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.KruskalWallis
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Bartlett
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
- getAlternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Levene
-
- getAplus() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
-
- getARMA() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
-
Get the ARMA part of this ARIMA model, essentially ignoring the differencing.
- getARMAForecastOneStep() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Gets the auxiliary ARMA one-step ahead forecaster.
- getARMAGARCHModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- getARMAModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the fitted ARMA model.
- getARMAModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
-
Get the ARMA part of this model.
- getARMAX() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the ARMAX part of this ARIMAX model, essentially ignoring the differencing.
- getAuxiliaryOLSRegression(Vector, LMResiduals) - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
Get the auxiliary regression.
- getAuxiliaryOLSRegression(Vector, LMResiduals) - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White
-
- getAuxiliaryRegression() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.BreuschPagan
-
- getAuxiliaryRegression() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Glejser
-
- getAuxiliaryRegression() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.HarveyGodfrey
-
- getAuxiliaryRegression() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
Define the transformation of residuals.
- getAuxiliaryRegression() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White
-
- getAverageClusterSize() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.Clusters
-
Get the average cluster size.
- getBarrierLevels() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the barrier levels used for estimation.
- getBase() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Best1Bin
-
- getBase() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Pick a base chromosome from the population.
- getBasis(int) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Basis
-
Get the full set of the standard basis vectors.
- getBasis(int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Basis
-
Get a subset of the standard basis vectors.
- getBasis() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Get the orthogonal basis.
- getBCol(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Get the table entry at [i, B].
- getBeginIndex() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.Clusters.Cluster
-
Get the index of the first element of this cluster.
- getBest(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Get the i-th best chromosome.
- getBinKeyValues(RealScalarFunction) - Method in class com.numericalmethod.suanshu.misc.algorithm.Bins
-
Applies a function to the key of each bin.
- getBinObjectValues(Function<List<T>, Double>) - Method in class com.numericalmethod.suanshu.misc.algorithm.Bins
-
Applies a function to the items of each bin.
- getBins() - Method in class com.numericalmethod.suanshu.misc.algorithm.Bins
-
Divides the items into n bins.
- getBuyThreshold() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011Solver.Boundaries
-
Gets the buy threshold.
- getCharacteristicPolynomial() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
Get the characteristic polynomial.
- getChild(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Produce a child chromosome.
- getChild(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
-
- getChildren(DiGraph<V, ? extends Arc<V>>, V) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Get the set of vertices that have an incoming arc coming from a vertex.
- getClusterCount() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.Clusters
-
Get the number of clusters.
- getClusterMaxima() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.Clusters
-
Get an array of cluster maxima.
- getClusters(Matrix, double) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModelUtils
-
- getClusters(double[]) - Method in class com.numericalmethod.suanshu.stats.evt.cluster.ClusterAnalyzer
-
Count clusters from the given observations.
- getClusters() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.Clusters
-
Get the list of clusters.
- getCoefficient(int) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Get an-i, the coefficient of xn-i.
- getCoefficients() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Get a copy of the polynomial coefficients.
- getCoefficients() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
- getCoefficients() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
-
Get the coefficients \(c_i\) associated with each evaluation point \(x_i\).
- getCoefficients() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
-
- getCoefficients() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
- getCoefficients() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
- getColLabel(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Gets the label for column i.
- getColLabel(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- getColumn(int) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.Matrix
-
Get the specified column in the matrix as a vector.
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Get a column.
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- getColumn(int, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Gets a sub-column of the j-th column, from beginRow row to
endRow row, inclusively.
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- getColumn(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
- getComplement() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Get the basis of the orthogonal complement.
- getComplexRoots(List<? extends Number>) - Static method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRoot
-
Get a copy of only the
Complex but not real roots of a polynomial.
- getComponent(List<double[]>, int) - Static method in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
Gets the i-th component from a list of double[].
- getConcurrency() - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Returns the number of threads used for parallel execution.
- getConfidenceInterval() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
-
Get the estimated confidence intervals of the fitted ACER function.
- getConfidenceInterval() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACERStatistics
-
Get the width of half of the confidence interval, that is, the interval is mean +/-
width.
- getConfidenceLevel() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.ConfidenceInterval
-
Get the confidence level.
- getConfidenceWidths() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the confidence interval half-widths for the estimates of the barrier levels.
- getConstrainedOptimSubProblem(ConstrainedOptimProblem, Map<Integer, Double>) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer
-
- getConstraints() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.Constraints
-
Get the list of constraint functions.
- getConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralConstraints
-
Get the constraints.
- getConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
- getConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
- getConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
- getContext(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
Generates the context information from a generating vector x.
- getCoordinate(Vector[], int) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the vector entries from a particular coordinate.
- getCoordinate(Collection<Vector>, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the vector entries from a particular coordinate.
- getCoordinates() - Method in class com.numericalmethod.suanshu.geometry.Point
-
Get the coordinates of the point.
- getCostRow(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Get the table entry at [COST, j].
- getCount() - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.CountMonitor
-
Get the number of iterations.
- getCovarianceMatrix() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004.Result
-
Gets the "shrunk" covariance matrix.
- getCovariances(Matrix) - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.PortfolioOptimizationAlgorithm.CovarianceEstimator
-
- getCovariances(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleCovarianceEstimator
-
- getCutter(ILPProblem) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer.CutterFactory
-
Construct a new Cutter for a MILP problem.
- getDate(int, int, int, DateTimeZone) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Construct a DateTime instance with year, month, day, and time
zone.
- getDateTime(String) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Construct a DateTime instance from a string which ends in
TimeZone specification.
- getDateTime(String, DateFormat, DateTimeZone) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Construct a DateTime instance from a string with no TimeZone
specified.
- getDemeanedModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Get the demeaned version of the time series model.
- getDemeanedModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Get the demeaned version of the time series model.
- getDifference(int) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.DividedDifferences
-
Get the divided difference of the given order.
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer.ZangwillImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer.NewtonRaphsonImpl
-
- getDirection(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
Get the next search direction.
- getDisjointGraphs(UnDiGraph<V, E>, GraphUtils.GraphFactory<G>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Separate an undirected graph into disjointed connected graphs.
- getDisjointGraphs(UnDiGraph<V, E>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
- getDistribution(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.exponentialfamily.MultivariateExponentialFamily
-
Construct a probability distribution in the exponential family.
- getDistribution(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily.ExponentialFamily
-
Construct a probability distribution in the exponential family.
- getDistribution() - Method in class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
-
Get the univariate extreme value distribution for generating innovations.
- getDistribution() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHMM
-
Gets the distribution in the hidden Markov model.
- getDistribution(int) - Method in enum com.numericalmethod.suanshu.stats.test.timeseries.adf.TrendType
-
Get an ADF distribution per sample size.
- getDiversifiedWeights(Corvalan2005.WeightsConstraint, Vector, Matrix, Vector) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.Corvalan2005
-
Finds the optimal weights for a diversified portfolio.
- getDiversifiedWeights(Corvalan2005.WeightsConstraint, Vector, Matrix, Vector, EqualityConstraints, LessThanConstraints) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.Corvalan2005
-
Finds the optimal weights for a diversified portfolio.
- getEdge(V, V, X) - Method in interface com.numericalmethod.suanshu.graph.GraphUtils.EdgeFactory
-
Creates an edge between two nodes.
- getEdgeBetweeness(UnDiGraph<V, ? extends UndirectedEdge<V>>) - Method in interface com.numericalmethod.suanshu.graph.community.GirvanNewman.EdgeBetweenessCtor
-
Construct an EdgeBetweeness from an undirected graph.
- getEdges(Graph<V, ?>, V, V) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Gets the set of edges that connect the two vertices.
- getEigenvalue(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Get the i-th eigenvalue.
- getEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
- getEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds.DQDS
-
Gets the eigenvalues of the matrix, sorted in descending order.
- getEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds.EigenvalueByDQDS
-
Gets all the eigenvalues of the symmetric tridiagonal matrix, sorted in descending order.
- getEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
- getEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Gets all the eigenvalues in descending order.
- getEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
-
- getEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix
-
- getEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
Get all the eigenvalues.
- getEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Get all the eigenvalues.
- getEigenvalues() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Spectrum
-
Get all the eigenvalues.
- getEigenvalues() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the set of real eigenvalues.
- getEigenVector(Vector, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Get an eigenvector from an initial guess.
- getEigenVector() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Get an eigenvector.
- getEigenvector() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getEigenvectorMatrix() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Gets the eigenvector matrix, each column is an eigenvector.
- getEigenvectorMatrix() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
-
- getEigenvectors() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Gets all the eigenvectors which corresponds to the list of eigenvalues.
- getEigenvectors() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
-
- getEigenvectors() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix
-
Gets the eigenvectors.
- getEigenVectors() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
- getEigenvectors() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Gets the eigenvectors of A, which are the columns of Q.
- getEndIndex() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.Clusters.Cluster
-
Get the index of the last element of this cluster.
- getEndPoint1() - Method in class com.numericalmethod.suanshu.geometry.LineSegment
-
Get the first endpoint.
- getEndPoint2() - Method in class com.numericalmethod.suanshu.geometry.LineSegment
-
Get the second endpoint.
- getEntryList() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getEntryList() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getEntryList() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getEntryList() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
-
- getEqualityConstraints() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblem
-
Gets the equality constraints, hi(x) = 0
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
- getEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
- getEstimates() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
-
Get the empirical estimates.
- getEstimators(int) - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Gets the estimators (estimated psi, loading matrix, degree of freedom,
test statistics, p-value, etc) obtained from the factor analysis, given
the maximum number of iterations.
- getEstimators(Vector, int) - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Gets the estimators (estimated psi, loading matrix, degree of freedom,
test statistics, p-value, etc) obtained from the factor analysis, given
the initial psi and the maximum number of iterations.
- getEstimators() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting
-
Calculate the LARS fitting estimators.
- getEvaluationPoints() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
- getEvaluationPoints() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
-
Get the evaluation points for the quadrature rule (\(x_i\)).
- getEvaluationPoints() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
-
- getEvaluationPoints() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
- getEvaluationPoints() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
- getEventCountPerPeriod() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the mean peak rate, or the average number of peaks per period.
- getExceedenceCount() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.Clusters
-
Get the number of observations that is greater than a given threshold.
- getExceptions() - Method in exception com.numericalmethod.suanshu.misc.parallel.MultipleExecutionException
-
Get all exceptions encountered during execution.
- getExpectedContingencyTable(int[], int[]) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
-
Assume the null hypothesis of independence, we compute the expected frequency of each
category.
- getExtHeaders() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the extended headers, including the subject and time headers.
- getExtHeadersString() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the extended headers, including the subject and time headers.
- getFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMProblem
-
Get the exponential family distribution of the mean.
- getFeasibleInitialPoint(LinearEqualityConstraints) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
-
Given a collection of linear greater-than-or-equal-to constraints as well as a collection of
equality constraints,
find a feasible initial point that satisfy the constraints.
- getFeasibleInitialPoint() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
-
Given a collection of linear greater-than-or-equal-to constraints,
find a feasible initial point that satisfy the constraints.
- getFeasibleInitialPoint(LinearEqualityConstraints) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearLessThanConstraints
-
Given a collection of linear less-than-or-equal-to constraints as well as a collection of equality constraints,
find a feasible initial point that satisfy the constraints.
- getFeasibleInitialPoint() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearLessThanConstraints
-
Given a collection of linear less-than-or-equal-to constraints,
find a feasible initial point that satisfy the constraints.
- getFirstGeneration() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Initialize the first population.
- getFirstGeneration() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.FirstGeneration
-
Generate the initial pool of chromosomes.
- getFirstGeneration() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.PerturbationAroundPoint
-
- getFirstGeneration() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.UniformMeshOverRegion
-
- getFirstGeneration() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
The initial population is generated by putting a uniform mesh/grid/net over the entire
region.
- getFirstInstantAfter(DateTime, DateTimeZone, LocalTime) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Get the first instant of time after a given start time.
- getFirstNonIntegralIndices(double[]) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
Get the index of the first integral variable whose value is not an integer, violating the integral constraints.
- getFirstNonZeroIndex() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getFitResult() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
-
Get the fitted parameter.
- getFittedOU(double[], double) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUFitting
-
Get the fitted OU process.
- getFittedOU(double[]) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
-
Fit an OU process by using MLE.
- getFittedOU(double[], double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
-
- getFittedOU(double[]) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
-
Fit an OU process by using least squares regression.
- getFittedOU(double[], double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
-
- getFittedParameters() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Get the fitted parameters.
- getFittedState(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the posterior expected state.
- getFittedState(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the posterior expected state.
- getFittedStates() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the posterior expected states.
- getFittedStates() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the posterior expected states.
- getFittedStateVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the posterior expected state variance.
- getFittedStateVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the posterior expected state variance.
- getFlags() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection
-
Gets the factor flags.
- getFractional(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Get the fractional part of a number.
- getFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get an empty filtration of the process.
- getFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.FiltrationFunction
-
Get the filtration.
- getFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.SDE
-
Get an empty filtration of the process.
- getGARCHModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
-
Get the GARCH part of this model.
- getGraph() - Method in interface com.numericalmethod.suanshu.graph.GraphUtils.GraphFactory
-
Creates an empty graph.
- getGreaterThanConstraint(Vector, int) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
Construct a greater-than constraint for the branching greater-than subproblem.
- getGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
-
Get the greater-than-or-equal-to constraints of the linear programming problem.
- getGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the set of linear greater-than-or-equal-to constraints.
- getHeaders() - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Gets the column names.
- getHMM() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
- getHomogeneousSoln() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.Solution
-
Get the basis of the homogeneous solution for the linear system,
Ax = b.
- getHouseholderMatrices() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
Gets the householder reflections used in the reflection.
- getInactive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Get the i-th inactive index.
- getInactiveIndices() - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Get all inactive indices.
- getIncrement(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl
-
- getIncrement(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer.ZangwillImpl
-
- getIncrement(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
- getIncrement(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
Get the increment fraction, αk.
- getIndex(String) - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.PortfolioOptimizationAlgorithm.SymbolLookup
-
Gets the index (starts from 1) of the product with a given symbol.
- getIndexFromColLabel(Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Translates a column label to a column index.
- getIndexFromRowLabel(Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Translates a row label to a row index.
- getIndices() - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Gets a copy of the row indices.
- getInitialGuess() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the initial guess of the solution for the problem.
- getInitialHessian(Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
-
Get the initial Hessian matrix.
- getInitialHessian(Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- getInitialHessian(Vector, Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPASVariation
-
Get the initial Hessian matrix.
- getInitialHessian(Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPASVariation1
-
- getInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.initialization.DefaultSimplex
-
Build a simplex of N+1 vertices from an initial point, where N is the dimension of the initial points.
- getInitials(Vector...) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.initialization.InitialsFactory
-
Generate a set of initial points for optimization from the fewer than required points.
- getInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.initialization.UniformDistributionOverBox1
-
- getInitials() - Method in class com.numericalmethod.suanshu.optimization.multivariate.initialization.UniformDistributionOverBox1
-
Generate a set of initial points for optimization.
- getInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.initialization.UniformDistributionOverBox2
-
- getInitials() - Method in class com.numericalmethod.suanshu.optimization.multivariate.initialization.UniformDistributionOverBox2
-
Generate a set of initial points for optimization.
- getIntegerIndices() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblem
-
Get the indices of the integral variables.
- getIntegerIndices() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
- getIntegerIndices() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- getIntegralConstraint(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
-
Get the integral domain of a particular integral variable.
- getIntervalLength() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.ClusterAnalyzer
-
Get the clustering interval length.
- getIntervals(DateTime, DateTime, Period) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Creates a list of intervals between start and end every
so often.
- getInverseNorm() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getIterates() - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.IteratesMonitor
-
Get a list of all iterative states.
- getKalmanGain(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the Kalman gain.
- getKalmanGain(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the Kalman gain.
- getLambda(Vector) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
-
Computes the value of the objective function in eq.
- getLastDayOfMonth(DateTime) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Get the last day of the month which contain a time.
- getLastMillisecondOfDay(DateTime) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Get the last millisecond the day which contain a time.
- getLastNonZeroIndex() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getLeftHouseholders() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets all the accumulated left Householders.
- getLeftPreconditioner() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the left preconditioner.
- getLessThanConstraint(Vector, int) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
Construct a less-than constraint for the branching less-than subproblem.
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
-
- getLessThanConstraints() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblem
-
Gets the less-than-or-equal-to constraints, gi(x) ≤
0
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
- getLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
- getLHS(double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D.Coefficients
-
Gets the left hand side coefficient matrix of the Crank-Nicolson
scheme.
- getLHS() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
-
- getLHS(int) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
- getLicenseKey() - Static method in class com.numericalmethod.suanshu.misc.license.License
-
Gets the license key string of the current license.
- getLicenseLocation() - Static method in class com.numericalmethod.suanshu.misc.license.License
-
Gets the location of the current loaded license.
- getLinearModel(Object) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.FixedEffectsModel
-
- getLinearModel(Object) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.panel.PanelRegression
-
Gets the linear model for a particular subject/individual.
- getLinearSpan(double...) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
- getLmask() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getLocation() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
Get the location parameter.
- getLogLikelihoodFunction() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Get the log-likelihood function.
- getLower() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.ConfidenceInterval
-
Get the lower bounds of the confidence intervals.
- getLowerLevel(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- getLowerParameter() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- getLowerRight() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.Deflation
-
Gets the lower right corner of the deflation.
- getMAModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
Get the MA model of the inverse representation.
- getMarginal1() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- getMarginal2() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- getMaskB() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getMaskC() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getMaxIteration() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the specified maximum number of iterations.
- getMaxIterations() - Method in exception com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.MaxIterationsExceededException
-
Get the maximum number of iterations.
- getMaxIterations() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.IterativeIntegrator
-
Get the maximum number of iterations for this iterative procedure.
- getMaxIterations() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
- getMaxIterations() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
- getMean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACERStatistics
-
Get the mean of the empirical estimates for each barrier level.
- getMean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.ConfidenceInterval
-
Get the mean values.
- getMeanForecast(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
Calculates the k-step ahead forecast of X in ARMA model.
- getMeanResidual() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- getMeanReturns(double[][]) - Static method in class com.numericalmethod.suanshu.finance.trading.returns.Returns
-
Computes a vector of mean returns of the input returns (one column for
one asset).
- getMeanReturns(Matrix) - Static method in class com.numericalmethod.suanshu.finance.trading.returns.Returns
-
Computes a vector of mean returns of the input returns (one column for
one asset).
- getMeans(Matrix) - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.PortfolioOptimizationAlgorithm.MeanEstimator
-
- getMeans(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleMeanEstimator
-
- getMeans() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the (weighted) mean of the estimates for the generated barrier levels.
- getMessage() - Method in error com.numericalmethod.suanshu.misc.license.LicenseError
-
- getMessage() - Method in exception com.numericalmethod.suanshu.misc.parallel.MultipleExecutionException
-
Gather the stack traces for the thrown exceptions.
- getMinEigenValue(Matrix, double) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
-
Gets the minimum of all the eigenvalues of a matrix.
- getMinEigenValue(Matrix, double) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
-
Gets the minimum of all the eigenvalues of a matrix.
- getMinEigenValue(Matrix, double) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
-
Gets the minimum of all the eigen values of a matrix.
- getMm() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getModel() - Method in class com.numericalmethod.suanshu.stats.evt.timeseries.MARMASim
-
Get the MARMA model.
- getModel() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection
-
Gets the constructed model.
- getModel() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Get the fitted ARMA model.
- getModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the fitted ARIMA model.
- getModel() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit
-
Get the fitted GARCH model.
- getMStepParams(double[], Vector[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureDistribution
-
Maximize, for each state, the log-likelihood of the distribution with
respect to the observations and current estimators.
- getMStepParams(double[], Vector[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonMixtureDistribution
-
- getNegCount() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getNeighbors(Graph<V, ?>, V) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Gets the set of vertices which are connected to v via any edges in this graph.
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
- getNewFt() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE
-
Get an empty filtration of the process.
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
-
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.BMSDE
-
- getNewFt() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.DiscreteSDE
-
Get an empty filtration of the process.
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.EulerSDE
-
- getNewFt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.MilsteinSDE
-
- getNewPool(int) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Allocate space for a population pool.
- getNextGeneration(List<Chromosome>, List<Chromosome>) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Populate the next generation using the parent and children chromosome pools.
- getNextGeneration(List<Chromosome>, List<Chromosome>) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
-
- getNn() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getNonIntegralIndices(double[]) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblem
-
Check which elements in x do not satisfy the integral constraints.
- getNonIntegralIndices(double[]) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
- getNonIntegralIndices(double[]) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- getNormalization() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Get the normalized version of this polynomial so the leading coefficient is 1.
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FAEstimator
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarling
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.CramerVonMises2Samples
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get a description of the null hypothesis.
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.KruskalWallis
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Bartlett
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
- getNullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Levene
-
- getNumberOfPeriodsBetween(DateTime, DateTime, Period) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Return the number of periods between two times, rounding up.
- getObsDimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
-
Get the dimension of the observations.
- getObsDimension() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the dimension of observations.
- getObservationModel() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
-
Get the observation model.
- getObservationModel() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the observation model.
- getObservations() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
-
Get the observations.
- getObservations() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Get the observations.
- getObservations(HmmInnovation[], int) - Static method in class com.numericalmethod.suanshu.stats.markovchain.MCUtils
-
Get all observations that occur in a particular state.
- getObservations(int) - Method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.table.ADFDistributionTable
-
Get the observations to compute an empirical distribution.
- getOffsetVectors(Vector, Vector, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Given the reference vector v0, the delta dv, and
the range [a, b], the offset vectors are:
v0 + a * dv, v0 + (a + 1) * dv, ..., v0 + b * dv.
- getOmega() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.minmax.MinMaxProblem
-
Get the list of omegas, the domain.
- getOne() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Pick a chromosome for mutation/crossover.
- getOne() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Pick a random chromosome from the population.
- getOperation(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation.ImplementationChooser
-
Get an implementation based on the inputs.
- getOptimalBlockLength(double[], PattonPolitisWhite2009ForObject.Type) - Static method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Computes the optimal of block length.
- getOptimalBlockLength(Object[], PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject, PattonPolitisWhite2009ForObject.Type) - Static method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Computes the optimal of block length.
- getOptimalLag(double[]) - Static method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Finds the smallest lag \(\hat{m}\) such that the autocorrelation for lags
\((\hat{m} +
k),~k=1,\dots,K_N\) are all insignificant regarding to the critical
value.
- getOptimalLag(Object[], PattonPolitisWhite2009ForObject.AutoCorrelationForObject) - Static method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Finds the smallest lag \(\hat{m}\) such that the autocorrelation for lags
\((\hat{m} +
k),~k=1,\dots,K_N\) are all insignificant regarding to the critical
value.
- getOptimalPositions(int, int) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
-
- getOptimalValue() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
-
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.Lai2010OptimizationAlgorithm
-
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.nmsaam.SAAM
-
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup) - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.PortfolioOptimizationAlgorithm
-
Computes the optimal weights for the products using returns.
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.TopNOptimizationAlgorithm
-
- getOrderedNodes() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS
-
- getOrderedNodes(Collection<V>) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BottomUp
-
Gets the list of visited nodes, in the order of being visited.
- getOrderedNodes() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BottomUp
-
- getOrderedNodes() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS
-
- getOrderedNodes() - Method in interface com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal
-
Gets the list of visited nodes, in the order of being visited.
- getOrderedNodes() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.TraversalFromRoots
-
Gets the collection of visited nodes to build a spanning tree.
- getOrthogonalVector() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Projection
-
Get the orthogonal vector which is equal to v minus the projection of v on {wi}.
- getPanelValuesByHeaders(List<PanelData<S>.Row>, String[], PanelData.Transformation[]) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the (transformed) values from a panel data.
- getPanelValuesByHeaders(List<PanelData<S>.Row>, String[]) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the values from a panel data.
- getPanelValuesByHeaders(List<PanelData<S>.Row>) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the values from a panel data.
- getPanelValuesByTime(List<PanelData<S>.Row>) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the (transformed) values from a panel data.
- getPanelValuesByTime(List<PanelData<S>.Row>, String[], PanelData.Transformation[]) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the (transformed) values from a panel data.
- getParameter() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit.Result
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaMixtureDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- getParams() - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureDistribution
-
Get the parameters, for each state, of the distribution.
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution
-
- getParams() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonMixtureDistribution
-
- getParents(DiGraph<V, ? extends Arc<V>>, V) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Get the set of vertices that have an outgoing arc pointing to a vertex.
- getParticularSolution(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.Solution
-
Get a particular solution for the linear system.
- getPeakMean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the average value of peaks.
- getPeaks() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the peaks found in the observations.
- getPenaltyFunction(ConstrainedOptimProblem) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer.PenaltyFunctionFactory
-
Get an instance of the penalty function.
- getPeriodicInstants(DateTime, Period, int) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Make a list of periodic time instants.
- getPivot(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
- getPivot(SimplexTable) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
-
Get the next pivot.
- getPolynomial(int) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.HermitePolynomials
-
- getPolynomial(int) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LaguerrePolynomials
-
- getPolynomial(int) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LegendrePolynomials
-
- getPolynomial(int) - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.OrthogonalPolynomialFamily
-
Return an instance of the polynomial class of a given order.
- getPopulation() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Get the current generation.
- getPortfolioConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Gets the portfolio constraints represented in the objective function.
- getPortfolioConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Gets the portfolio constraints.
- getPossiblePairs(List<String>, List<String>) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModelUtils
-
- getPossiblePairs(int) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModelUtils
-
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
-
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature
-
- getPrecision() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Integrator
-
Get the convergence threshold.
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Romberg
-
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
- getPrecision() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
- getPredictedObservation(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior observation prediction.
- getPredictedObservation(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior observation prediction.
- getPredictedObservations() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior observation predictions.
- getPredictedObservations() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior observation predictions.
- getPredictedObservationVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior observation prediction variance.
- getPredictedObservationVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior observation prediction variance.
- getPredictedState(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior expected state.
- getPredictedState(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior expected state.
- getPredictedStates() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior expected states.
- getPredictedStates() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior expected states.
- getPredictedStateVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior expected state variance.
- getPredictedStateVariance(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get the prior expected state variance.
- getPriceMatrix(Vector, Vector) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModelUtils
-
- getPricesFromReturns(double, double[], ReturnsCalculator) - Static method in class com.numericalmethod.suanshu.finance.trading.returns.Returns
-
Gets the price series from a return series.
- getProblem() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the linear regression problem.
- getProblemSize() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Get the number of variables in the problem or the cost/objective function.
- getProcess() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUSim
-
Get the underlying OU process of this generator.
- getProjectionLength(int) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Projection
-
Get the length of v projected on each dimension {wi}.
- getProjectionVector(int) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Projection
-
Get the i-th projected vector of v on {wi}.
- getProperty(Number) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
- getProperty(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
- getRandom() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Get the estimated seasonal effect of the time series.
- getRealEigenvalues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Get all real eigenvalues.
- getRealRoots(List<? extends Number>) - Static method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRoot
-
Get a copy of only the real roots of a polynomial.
- getReason() - Method in exception com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
-
Get the reason for the convergence failure.
- getReciprocal() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Gets the reciprocal state switching GBM.
- getReducedLinearEqualityConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearEqualityConstraints
-
- getRegime(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010Regime
-
Gets the current regime.
- getResamplerModel(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
-
- getResamplerModel(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
-
- getResamplerModel(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
-
- getResidual() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getResultantTableau() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- getResultantTableau() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPSimplexMinimizer
-
Get the solution simplex table as a result of solving a linear programming problem.
- getResultantTableau() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- getResults(List<D>) - Method in class com.numericalmethod.suanshu.misc.algorithm.BruteForce
-
Runs in parallel the brute force algorithm on the function for a given
domain.
- getResults() - Method in exception com.numericalmethod.suanshu.misc.parallel.MultipleExecutionException
-
Get the results obtained so far.
- getResultsSerially(List<D>) - Method in class com.numericalmethod.suanshu.misc.algorithm.BruteForce
-
Deprecated.
- getReturnLevel() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
-
Get the return level function for the estimated ACER function.
- getReturns(double[], double[], ReturnsCalculator) - Static method in class com.numericalmethod.suanshu.finance.trading.returns.Returns
-
- getReturnsFromPrices(double[], ReturnsCalculator) - Static method in class com.numericalmethod.suanshu.finance.trading.returns.Returns
-
Computes returns series from prices.
- getRHS(Vector, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D.Coefficients
-
Computes the right hand side vector of the Crank-Nicolson scheme.
- getRHS(Vector, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
-
- getRHS(int) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
- getRightHouseholders() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets all the accumulated right Householders.
- getRightPreconditioner() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the right preconditioner.
- getRNG(T) - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ContextRNG
-
- getRoot(Matrix) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.DefaultRoot
-
- getRoot(Matrix) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.Diagonalization
-
- getRoot(Matrix) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.MatrixRoot
-
Gets the root of a matrix
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- getRow(int) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.Matrix
-
Get the specified row in the matrix as a vector.
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Get a row.
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- getRow(int, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Gets a sub-row of the i-th row, from beginCol column to
endCol column, inclusively.
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- getRow(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- getRow(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
- getRow(S, String) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets one particular row indexed by a pair of subject and time.
- getRowLabel(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Gets the label for row i.
- getRowLabel(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- getRowOnOrAfter(double) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Gets the row corresponding to a row index.
- getRowOnOrBefore(double) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Gets the row corresponding to a row index.
- getRows() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets all of the panel data.
- getRowsForSubject(S) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Get all the rows pertaining to a particular subject.
- getRowsOnOrAfter(double) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Gets the rows having the row index value equal to or just bigger than i.
- getRowsOnOrBefore(double) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Gets the rows having the row index value equal to or just smaller than i.
- getRQCorrection() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getRr() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getRSS() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit.Result
-
- getScale() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
Get the scale parameter.
- getSeasonal() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Get the stationary random component of the time series after the trend and seasonal
components are removed.
- getSellThreshold() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011Solver.Boundaries
-
Gets the sell threshold.
- getShape() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
Get the shape parameter.
- getSharedInstance() - Static method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Gets the globally shared executor.
- getShift0() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getShift1() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getShiftB() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getShiftC() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getShrunkCovarianceMatrix() - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.LedoitWolf2016.Result
-
Gets the nonlinear shrinkage covariance matrix.
- getSignal(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
Override this method to put in whatever constraints in the minimization problem.
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
-
Construct an instance of a SimpleCell.
- getSingularValues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds.SingularValueByDQDS
-
Gets all the singular values of the given bidiagonal matrix A, sorted in descending
order.
- getSingularValues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
-
- getSingularValues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
- getSingularValues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
-
- getSingularValues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD
-
- getSingularValues() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the normalized, hence positive, singular values.
- getSingularValues() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Returns the singular values (same as the eigenvalues) of A.
- getSolutionToOriginalProblem(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
-
Backs out the solution for the original (constrained) problem, if the modified
(unconstrained) problem can be solved.
- getSpanningCoefficients(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Find a linear combination of the basis that best approximates a vector in the least square
sense.
- getStandardError() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Gets the the auxiliary coefficients, Θ and V, in using the innovative algorithm.
- getStateCounts(int[]) - Static method in class com.numericalmethod.suanshu.stats.markovchain.MCUtils
-
Count the numbers of occurrences of states.
- getStateDimension() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
-
Get the dimension of states.
- getStateDimension() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the dimension of states.
- getStateModel() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
-
Get the state model.
- getStateModel() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the state model.
- getStates() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
-
Get the states.
- getStates() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
Get the states.
- getStationaryProbabilities(Matrix) - Static method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Gets the stationary state probabilities of a Markov chain that is irreducible, aperiodic and
strongly connected (positive recurrent).
- getStatistic() - Method in interface com.numericalmethod.suanshu.stats.descriptive.StatisticFactory
-
- getStats(CointegrationMLE) - Method in class com.numericalmethod.suanshu.stats.cointegration.JohansenTest
-
Get the set of likelihood ratio test statistics for testing H(r)
in H(r+1).
- getStepLength(double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
-
- getStepLength(double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.FastAnnealingFunction
-
- getStepLength(double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction
-
- getSubProblem(ConstrainedOptimSubProblem) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer
-
- getSymbol(int) - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.PortfolioOptimizationAlgorithm.SymbolLookup
-
Gets the symbol of the product at a given index (starts from 1).
- getTable() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.NevilleTable
-
Get a copy of the Neville table.
- getTailedMatrix(Matrix, double) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModelUtils
-
- getTailedMatrix(Matrix, double) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.RobustCointegration
-
- getThreshold() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.ClusterAnalyzer
-
Get the threshold for exceedance.
- getTime() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries.Entry
-
- getTime() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries.Entry
-
- getTime() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
-
- getTime() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
-
- getTime() - Method in interface com.numericalmethod.suanshu.stats.timeseries.datastructure.TimeSeries.Entry
-
Get the timestamp.
- getTime() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
-
- getTolerance() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
- getTopN(Vector, int, double) - Static method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.TopNOptimizationAlgorithm
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in interface com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel1
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel2
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel3
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel4
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel5
-
- getTransformedMatrix() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets the final matrix transformed by all the Householder transformations.
- getTransitionCounts(int[]) - Static method in class com.numericalmethod.suanshu.stats.markovchain.MCUtils
-
Count the numbers of times the state goes from one state to another.
- getTrend() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Get the estimated trend of the time series.
- getTwistIndex() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getType() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Gets the bi-diagonal matrix type.
- getUmask() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getUpper() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.ConfidenceInterval
-
Get the upper bounds of the confidence intervals.
- getUpperLeft() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.Deflation
-
Gets the upper left corner of the deflation.
- getUpperLevel(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- getUpperParameter() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- getValue(String) - Static method in class com.numericalmethod.suanshu.misc.license.License
-
- getValue() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries.Entry
-
- getValue() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries.Entry
-
- getValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
-
- getValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
-
- getValue() - Method in interface com.numericalmethod.suanshu.stats.timeseries.datastructure.TimeSeries.Entry
-
Get the entry value.
- getValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
-
- getValueArray() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getValueArray() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getValueArray() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getValueArray() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix
-
Exports the non-zero values in the matrix as arrays of row/column indices
and values.
- getValueHeaders() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the headers, excluding the subject and time headers.
- getValueHeadersString() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Gets the headers, excluding the subject and time headers.
- getVarForecast(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
Calculates the k-step ahead of conditional variance h in GARCH model.
- getVariablePart(double[], Map<Integer, Double>) - Static method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
Given an input to the original function, this extracts the variable parts
(excluding the fixed values).
- getVariables(SOCPGeneralConstraints) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
- getVariables() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Gets the variables involved in the portfolio constraints implied by the objective function.
- getVariables() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Get the list of all variables and their beginning indices starting from 0.
- getVARMA() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAModel
-
Get the ARMA part of this ARIMA model, essentially ignoring the differencing.
- getVARMAX() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the ARMAX part of this ARIMAX model, essentially ignoring the differencing.
- getVarResidual() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- Getvec - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec
-
Computes the (scaled) r-th column of the inverse of the sub-matrix block of the
tridiagonal matrix T = LDLT - λ I.
- Getvec(LDDecomposition, double, int, double, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
Computes an FP vector of a singleton.
- getVectorReturnsFromPrices(double[][], ReturnsCalculator) - Static method in class com.numericalmethod.suanshu.finance.trading.returns.Returns
-
Computes returns for a 2D array of prices (one column for one asset),
with the given
ReturnsCalculator.
- getVersion() - Static method in class com.numericalmethod.suanshu.misc.license.License
-
Gets the version number.
- getViterbiStates(double[]) - Method in class com.numericalmethod.suanshu.stats.hmm.Viterbi
-
Gets the most likely sequence of states using Viterbi algorithm (global
decoding), given the observations and the underlying hidden Markov model.
- getVr() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
-
- getWeightForObservation(double, double) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.LeastSquares.Weighting
-
Specify the weight given to a particular observation.
- getWeighting(double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
- getWeighting(double) - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
-
Get the weighting \(w(x_i)\) associated with a point \(x_i\).
- getWeighting(double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
-
- getWeighting(double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
- getWeighting(double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
- getWeights() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit.Result
-
- getWhole(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Get the integral part of a number (discarding the fractional part).
- getWmask() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getWw() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getX2() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
Get the Chi-squared distribution.
- getX2() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White
-
- GEVFittingByMaximumLikelihood - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting
-
Estimate the
GeneralizedEVD parameter from the observations by
maximum likelihood approach.
- GEVFittingByMaximumLikelihood() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.GEVFittingByMaximumLikelihood
-
- GirvanNewman<V,E extends UndirectedEdge<V>,G extends UnDiGraph<V,E>> - Class in com.numericalmethod.suanshu.graph.community
-
The Girvan–Newman algorithm detects communities in complex systems.
- GirvanNewman(UnDiGraph<V, E>, GirvanNewman.EdgeBetweenessCtor<V>, GraphUtils.GraphFactory<G>) - Constructor for class com.numericalmethod.suanshu.graph.community.GirvanNewman
-
Construct an instance of the Girvan-Newman algorithm.
- GirvanNewman.EdgeBetweenessCtor<V> - Interface in com.numericalmethod.suanshu.graph.community
-
This allows customization of the computation of edge-betweeness.
- GirvanNewmanUnDiGraph<V,E extends UndirectedEdge<V>> - Class in com.numericalmethod.suanshu.graph.community
-
- GirvanNewmanUnDiGraph(UnDiGraph<V, E>) - Constructor for class com.numericalmethod.suanshu.graph.community.GirvanNewmanUnDiGraph
-
Construct an instance of the Girvan-Newman algorithm.
- GivensMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype
-
Givens rotation is a rotation in the plane spanned by two coordinates axes.
- GivensMatrix(int, int, int, double, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Constructs a Givens matrix in the form
\[
G(i,j,c,s) = \begin{bmatrix}
1 & ...
- GivensMatrix(GivensMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Copy constructor.
- gk - Variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
the gradient at the k-th iteration
- Glejser - Class in com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
-
The Glejser test tests for conditional heteroskedasticity.
- Glejser(LMResiduals) - Constructor for class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Glejser
-
Perform the Glejser test to test for heteroskedasticity in a linear regression model.
- GLMBeta - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
This is the estimate of beta, β^, in a Generalized Linear Model.
- GLMBeta(GLMFitting, GLMResiduals) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMBeta
-
Construct an instance of Beta.
- GLMBinomial - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
This is the Binomial distribution of the error distribution in GLM model.
- GLMBinomial() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial
-
- GLMExponentialDistribution - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
This interface represents a probability distribution from the exponential family.
- GLMFamily - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
Family provides a convenient way to specify the error distribution
and link function used in GLM model.
- GLMFamily(GLMExponentialDistribution, LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMFamily
-
Construct an instance of Family.
- GLMFamily(GLMBinomial) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMFamily
-
Construct a Binomial family.
- GLMFamily(GLMGamma) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMFamily
-
Construct a Gamma family.
- GLMFamily(GLMGaussian) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMFamily
-
Construct a Gaussian family.
- GLMFamily(GLMInverseGaussian) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMFamily
-
Construct an Inverse Gaussian family.
- GLMFamily(GLMPoisson) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMFamily
-
Construct a Poisson family.
- GLMFitting - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm
-
This interface represents a fitting method for estimating β in a
Generalized Linear Model (GLM).
- GLMGamma - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
This is the Gamma distribution of the error distribution in GLM model.
- GLMGamma() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma
-
- GLMGaussian - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
This is the Gaussian distribution of the error distribution in GLM model.
- GLMGaussian() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian
-
- GLMInverseGaussian - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
This is the Inverse Gaussian distribution of the error distribution in GLM model.
- GLMInverseGaussian() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian
-
- GLMModelSelection - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
Given a set of observations {y, X}, we would like to construct a GLM to explain the data.
- GLMModelSelection(GLMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection
-
Constructs automatically a GLM model to explain the observations.
- GLMModelSelection.ModelNotFound - Exception in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
Throw a ModelNotFound exception when fail to construct a model to
explain the data.
- GLMPoisson - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
-
This is the Poisson distribution of the error distribution in GLM model.
- GLMPoisson() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson
-
- GLMProblem - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
This is a Generalized Linear regression problem.
- GLMProblem(Vector, Matrix, boolean, GLMFamily) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMProblem
-
Construct a GLM problem.
- GLMProblem(LMProblem, GLMFamily) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMProblem
-
Construct a GLM problem from a linear regression problem.
- GLMResiduals - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
Residual analysis of the results of a Generalized Linear Model regression.
- GLMResiduals(GLMProblem, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMResiduals
-
Performs residual analysis for a GLM regression.
- GlobalSearchByLocalMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local
-
This minimizer is a global optimization method.
- GlobalSearchByLocalMinimizer(LocalSearchCellFactory.MinimizerFactory, RandomLongGenerator, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a GlobalSearchByLocalMinimizer to solve unconstrained minimization
problems.
- GlobalSearchByLocalMinimizer(RandomLongGenerator, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a GlobalSearchByLocalMinimizer to solve unconstrained minimization
problems.
- GlobalSearchByLocalMinimizer() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a GlobalSearchByLocalMinimizer to solve unconstrained minimization
problems.
- GMT - Static variable in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
GMT
- GOLDEN_RATIO - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
the Golden ratio
- GoldenMinimizer - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This is the golden section univariate minimization algorithm.
- GoldenMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.GoldenMinimizer
-
Construct a univariate minimizer using the Golden method.
- GoldenMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
-
This is the solution to a Golden section univariate optimization.
- GoldfeldQuandtTrotter - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite
-
Goldfeld, Quandt and Trotter propose the following way to coerce a non-positive definite Hessian
matrix to become symmetric, positive definite.
- GoldfeldQuandtTrotter(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.GoldfeldQuandtTrotter
-
Constructs a symmetric, positive definite matrix using the Goldfeld-Quandt-Trotter algorithm.
- GolubKahanSVD - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd
-
Golub-Kahan algorithm does the SVD decomposition of a tall matrix in two stages.
- GolubKahanSVD(Matrix, boolean, boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
-
Run the Golub-Kahan SVD decomposition on a tall matrix.
- GolubKahanSVD(Matrix, boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
-
Runs the Golub-Kahan SVD decomposition on a tall matrix.
- GomoryMixedCutMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane
-
This cutting-plane implementation uses Gomory's mixed cut method.
- GomoryMixedCutMinimizer() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer
-
Construct a Gomory mixed cutting-plane minimizer to solve an MILP problem.
- GomoryMixedCutMinimizer.MyCutter - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane
-
This is Gomory's mixed cut.
- GomoryPureCutMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane
-
This cutting-plane implementation uses Gomory's pure cut method for pure integer programming,
in which all variables are integral.
- GomoryPureCutMinimizer() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer
-
Construct a Gomory pure cutting-plane minimizer to solve pure ILP problems,
in which all variables are integral.
- GomoryPureCutMinimizer.MyCutter - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane
-
This is Gomory's pure cut.
- Gradient - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
The gradient of a scalar field is a vector field which points in the direction of the greatest
rate of increase of the scalar field, and of which the magnitude is the greatest rate of change.
- Gradient(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.Gradient
-
Construct the gradient vector for a multivariate function f at point x.
- gradient(T) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.minmax.MinMaxProblem
-
g(x, ω) = ∇|e(x, ω)| is the gradient function of the absolute error, |e(x, ω)|, for a given ω.
- GradientFunction - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
The gradient function, g(x), evaluates the gradient of a real scalar function f at a point x.
- GradientFunction(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
-
Construct the gradient function of a real scalar function f.
- GramSchmidt - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr
-
The Gram-Schmidt process is a method for orthogonalizing a set of vectors in an inner product space.
- GramSchmidt(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
Run the Gram-Schmidt process to orthogonalize a matrix.
- GramSchmidt(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
Run the Gram-Schmidt process to orthogonalize a matrix.
- Graph<V,E extends HyperEdge<V>> - Interface in com.numericalmethod.suanshu.graph
-
A graph is a representation of a set of objects where some pairs of the objects are connected by
links.
- GraphTraversal<V> - Interface in com.numericalmethod.suanshu.graph.algorithm.traversal
-
A spanning tree T of a connected, undirected graph G is a tree composed of all the
vertices and some (or perhaps all) of the edges of G.
- GraphTraversal.Node<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
-
This is a node in a spanning tree.
- GraphUtils - Class in com.numericalmethod.suanshu.graph
-
These are the utility functions to manipulate Graph.
- GraphUtils.EdgeFactory<V,N,E extends Edge<N>,X> - Interface in com.numericalmethod.suanshu.graph
-
This interface specifies how an edge is created for two nodes.
- GraphUtils.GraphFactory<G> - Interface in com.numericalmethod.suanshu.graph
-
The factory to construct instances of the graph type.
- GRAVITATIONAL_G - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The Newtonian constant of gravitation \(G\) in meters cubed per kilogram per second squared
(m3 kg-1 s-2).
- GreaterThanConstraints - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint
-
The domain of an optimization problem may be restricted by greater-than or equal-to constraints.
- grid(double, double) - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011Solver.Builder
-
- GridSearchCetaMaximizer - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer
-
Searches (by brute force) for the maximal point of C(η) among a
grid of values.
- GridSearchCetaMaximizer() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
- GridSearchCetaMaximizer(double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
Constructs a maximizer with a given grid size for even grid search.
- GridSearchCetaMaximizer(GridSearchMinimizer.GridDefinition) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
Constructs a maximizer with a user-defined grid.
- GridSearchMinimizer - Class in com.numericalmethod.suanshu.optimization.univariate
-
This performs a grid search to find the minimum of a univariate function.
- GridSearchMinimizer(int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer
-
Constructs an instance with a given number of grid points for even grid.
- GridSearchMinimizer(double) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer
-
Constructs an instance with a given grid size for even grid.
- GridSearchMinimizer(GridSearchMinimizer.GridDefinition) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer
-
Constructs an instance with a user-defined grid.
- GridSearchMinimizer.GridDefinition - Interface in com.numericalmethod.suanshu.optimization.univariate
-
- GridSearchMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.univariate
-
This is the solution to the GridSearchMinimizer.
- groupCount() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.IndependentCoVAR
-
Returns the total number of independent groups.
- GroupResampler - Class in com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate
-
- GroupResampler(Matrix, Resampler) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate.GroupResampler
-
Constructs a re-sampler that treats each row as a group object, shuffling
the groups/rows.
- GroupResampler(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate.GroupResampler
-
Constructs a re-sampler that treats each row as a group object, shuffling
the groups/rows.
- GroupResamplerFactory - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler
-
Creates re-samplers that do re-sampling for the whole group of stocks
together.
- GroupResamplerFactory() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
-
- groups() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.IndependentCoVAR
-
Returns the grouped variable indices.
- Gs() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Gets the list of Gk's produced in the process of diagonalizing the
tridiagonal matrix.
- GSAAcceptanceProbabilityFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
-
The GSA acceptance probability function.
- GSAAcceptanceProbabilityFunction(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.GSAAcceptanceProbabilityFunction
-
Constructs a GSA acceptance probability function.
- GSAAnnealingFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction
-
The GSA proposal/annealing function.
- GSAAnnealingFunction(double, RandomLongGenerator, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction
-
Constructs a GSA annealing function.
- GSAMarkovLength(int) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction
-
The Markov length for GSA, i.e.
- GSATemperatureFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction
-
The GSA temperature function.
- GSATemperatureFunction(double, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction
-
Constructs a GSA temperature function.
- GumbelDistribution - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate
-
The Gumbel distribution is a special case (Type I) of the generalized extreme value distribution,
with \(\xi=0\).
- GumbelDistribution() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.GumbelDistribution
-
Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\).
- GumbelDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.GumbelDistribution
-
Create an instance with the given parameter values.
- i() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets the value of i.
- i - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
the row index
- I - Static variable in class com.numericalmethod.suanshu.number.complex.Complex
-
a number representing 0.0 + 1.0i, the square root of -1
- I - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- identity(int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a new identity matrix.
- IdentityHashSet<T> - Class in com.numericalmethod.suanshu.misc.datastructure
-
This class implements the Set interface with a hash table, using reference-equality in place of
object-equality when comparing keys and values.
- IdentityHashSet() - Constructor for class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
Construct an empty IdentityHashSet.
- IdentityHashSet(Collection<T>) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
Construct an IdentityHashSet with a collection of items.
- IdentityPreconditioner - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
This identity preconditioner is used when no preconditioning is applied.
- IdentityPreconditioner() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
-
- ifelse(double[], DoubleUtils.ifelse) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Return a value with the same shape as test which is filled with
elements selected from either yes or no depending on
whether the element of test is true or false.
- IID - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate
-
An i.i.d.
- IID(RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.IID
-
Construct a rvg that outputs vectors that have i.i.d.
- ILPBranchAndBoundMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb
-
This is a Branch-and-Bound algorithm that solves Integer Linear Programming problems.
- ILPBranchAndBoundMinimizer(ILPBranchAndBoundMinimizer.ActiveListFactory) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer
-
Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.
- ILPBranchAndBoundMinimizer() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer
-
Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.
- ILPBranchAndBoundMinimizer.ActiveListFactory - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb
-
This factory constructs a new instance of ActiveList for each Integer Linear Programming problem.
- ILPNode - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb
-
This is the branch-and-bound node used in conjunction with
ILPBranchAndBoundMinimizer to
solve an Integer Linear Programming problem.
- ILPNode(ILPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
Construct a BB node and associate it with an ILP problem.
- ILPProblem - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem
-
A linear program in real variables is said to be integral if it has at least one optimal solution which is integral.
- ILPProblemImpl1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem
-
This implementation is an ILP problem, in which the variables can be real or integral.
- ILPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints, int[], double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
Construct an ILP problem, in which the variables can be real or integral.
- imaginary() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the imaginary part of this complex number.
- ImmutableMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles
-
This is a read-only view of a
Matrix instance.
- ImmutableMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
Construct a read-only version of a matrix.
- ImmutableVector - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles
-
This is a read-only view of a
Vector instance.
- ImmutableVector(Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
Construct a read-only version of a vector.
- ImplicitModelPCA - Class in com.numericalmethod.suanshu.stats.factor.implicitmodelpca
-
Given a (de-meaned) time series of vectored observations, we decompose them
into a reduced dimension of linear sum of implicit factors.
- ImplicitModelPCA(Matrix, int) - Constructor for class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA
-
Constructs an implicit-model that will have K implicit factors.
- ImplicitModelPCA(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA
-
Constructs an implicit-model that will have one and only one implicit
factors.
- ImplicitModelPCA(Matrix, double) - Constructor for class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA
-
Constructs an implicit-model that will have the number of implicit
factors such that the variance explained is bigger than a threshold
- ImplicitModelPCA.Result - Class in com.numericalmethod.suanshu.stats.factor.implicitmodelpca
-
the regression results
- ImportanceSampling - Class in com.numericalmethod.suanshu.stats.random.variancereduction
-
Importance sampling is a general technique for estimating properties of a particular
distribution, while only having samples generated from a different distribution rather than the
distribution of interest.
- ImportanceSampling(UnivariateRealFunction, UnivariateRealFunction, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.ImportanceSampling
-
- inactiveSize() - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Get the number of inactive indices.
- incomingArcs(V) - Method in interface com.numericalmethod.suanshu.graph.DiGraph
-
Gets the set of all incoming arcs associated with a vertex in this graph.
- incomingArcs(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseDiGraph
-
- incomingArcs(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- incomingArcs(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- IndependentCoVAR - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008
-
This algorithm finds the independent variables based on the covariance matrix.
- IndependentCoVAR(Matrix, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.IndependentCoVAR
-
Runs the algorithm with the given covariance matrix.
- index() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
Gets the index of this entry in the sparse vector, counting from 1.
- index - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
the index to the variable, counting from 1.
- index - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
the index of a variable, an inequality, an equality, etc., counting from 1
- index - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
the index of an integral variable
- Infantino2010PCA - Class in com.numericalmethod.suanshu.finance.trading.infantino2010
-
The objective is to predict the next H-period accumulated returns from the
past H-period
dimensionally reduced returns.
- Infantino2010PCA(int, int, int, int, boolean) - Constructor for class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA
-
- Infantino2010PCA.Signal - Class in com.numericalmethod.suanshu.finance.trading.infantino2010
-
- Infantino2010Regime - Class in com.numericalmethod.suanshu.finance.trading.infantino2010
-
Detects the current regime (mean reversion or momentum) by cross-sectional volatility.
- Infantino2010Regime(int) - Constructor for class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010Regime
-
- Infantino2010Regime.Regime - Enum in com.numericalmethod.suanshu.finance.trading.infantino2010
-
- informationCriteria() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
Gets the model selection criteria.
- initialCondition() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
Initializes the grid with the initial conditions.
- initialReturns - Variable in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- initials - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- InitialsFactory - Interface in com.numericalmethod.suanshu.optimization.multivariate.initialization
-
Some optimization algorithms, e.g., Nelder-Mead, Differential-Evolution, require a set of initial points to work with.
- initialSigma2 - Variable in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- innerProduct(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- innerProduct(SparseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- InnerProduct - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
The Frobenius inner product is the component-wise inner product of two matrices as though they are vectors.
- InnerProduct(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.InnerProduct
-
Compute the inner product of two matrices.
- innerProduct(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- innerProduct(DenseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- innerProduct(Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the inner or dot product of two vectors.
- innerProduct(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- innerProduct(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- innerProduct(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Inner product in the Euclidean space is the dot product.
- innerProduct(H) - Method in interface com.numericalmethod.suanshu.algebra.structure.HilbertSpace
-
<⋅,⋅> : H × H → F
Inner product formalizes the geometrical notions such as the length of a vector and the angle between two vectors.
- InnovationsAlgorithm - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
The innovations algorithm is an efficient way to obtain a one step least square linear predictor
for a univariate linear time series with known auto-covariance and these properties (not limited
to ARMA processes):
{xt} can be non-stationary.
E(xt) = 0 for all t.
This class implements the part of the innovations algorithm that computes the prediction error
variances, v and prediction coefficients θ.
- InnovationsAlgorithm(int, AutoCovarianceFunction) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
-
Constructs an instance of InnovationsAlgorithm for a univariate time series with
known auto-covariance structure.
- intArray2doubleArray(int...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert an int array to a double array.
- intArray2List(int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert an int array to a list.
- IntegerDomain(int, int[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
Construct the integral domain for an integral variable.
- IntegerDomain(int, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
Construct the integral domain for an integral variable.
- IntegerDomain(int, int, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
Construct the integral domain for an integral variable.
- Integral - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
The class represents an integral of a function, in the Lebesgue sense.
- Integral(FiltrationFunction) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integral
-
Construct an integral from an integrand.
- IntegralConstrainedCellFactory - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
This implementation defines the constrained Differential Evolution operators that solve an Integer Programming problem.
- IntegralConstrainedCellFactory(DEOptimCellFactory, IntegralConstrainedCellFactory.IntegerConstraint) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory
-
Construct an instance of IntegralConstrainedCellFactory.
- IntegralConstrainedCellFactory.AllIntegers - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
This integral constraint makes all variables in the objective function integral variables.
- IntegralConstrainedCellFactory.IntegerConstraint - Interface in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
The integral constraints are defined by implementing this interface.
- IntegralConstrainedCellFactory.SomeIntegers - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
This integral constraint makes some variables in the objective function integral variables.
- IntegralDB - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
This class evaluates the following class of integrals.
- IntegralDB(FiltrationFunction) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDB
-
Construct an integral for f with respect to dB.
- IntegralDt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
This class evaluates the following class of integrals.
- IntegralDt(FiltrationFunction) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDt
-
Construct an integral for f with respect to dt.
- IntegralExpectation - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
-
This class computes the expectation of the following class of integrals.
- IntegralExpectation(Integral, double, double, int, int, long) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralExpectation
-
Compute the expectation for the integral of a stochastic process.
- IntegralExpectation(Integral, double, double, int, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralExpectation
-
Compute the expectation for the integral of a stochastic process.
- integrate(ODE1stOrder, double[]) - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.ODEIntegrator
-
This is the integration method that approximates the solution of a
first order ODE.
- integrate(ODE1stOrder, double[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaIntegrator
-
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
-
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature
-
- integrate(UnivariateRealFunction, double, double) - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Integrator
-
Integrate function f from a to b,
\[
\int_a^b\! f(x)\, dx
\]
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Romberg
-
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
- integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
- integrate(UnivariateRealFunction, double, double, SubstitutionRule) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
Integrate a function, f, from a to b possibly using change of variable.
- Integrator - Interface in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
This defines the interface for the numerical integration of definite integrals of univariate functions.
- intercept() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Checks if an intercept term is added to the linear regression.
- interpolate(BivariateGrid) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
-
- interpolate(BivariateGrid) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicSpline
-
- interpolate(BivariateGrid) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BilinearInterpolation
-
- interpolate(BivariateGrid) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateGridInterpolation
-
Construct a real valued function from a grid of observations.
- interpolate(MultivariateGrid) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateGridInterpolation
-
Construct a real valued function from a grid of observations.
- interpolate(MultivariateGrid) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.RecursiveGridInterpolation
-
- Interpolation - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
-
Interpolation is a method of constructing new data points within the range of a discrete set of
known data points.
- Interval<T extends Comparable<? super T>> - Class in com.numericalmethod.suanshu.interval
-
For a partially ordered set, there is a binary relation, denoted as ≤, that indicates that,
for certain pairs of elements in the set, one of the elements precedes the other.
- Interval(T, T) - Constructor for class com.numericalmethod.suanshu.interval.Interval
-
Construct an interval.
- IntervalRelation - Enum in com.numericalmethod.suanshu.interval
-
Allen's Interval Algebra is a calculus for temporal reasoning that was introduced by James F.
- Intervals<T extends Comparable<? super T>> - Class in com.numericalmethod.suanshu.interval
-
This is a disjoint set of intervals.
- Intervals() - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Construct an empty set of intervals.
- Intervals(Interval<T>) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Construct a set that contains only one interval.
- Intervals(T, T) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Construct a set that contains only one interval [begin, end].
- Intervals(Interval<T>...) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Construct a set of intervals.
- Intervals(Intervals<T>) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
-
Copy constructor.
- IntTimeTimeSeries - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime
-
This is a univariate time series indexed by integers.
- IntTimeTimeSeries.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime
-
This is the TimeSeries.Entry for an integer number -indexed univariate time series.
- intValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- intValue() - Method in class com.numericalmethod.suanshu.number.Real
-
- intValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
- InvalidLicense - Error in com.numericalmethod.suanshu.misc.license
-
This is the
LicenseError thrown when calling a class or method that is not yet licensed.
- InvalidLicense(String) - Constructor for error com.numericalmethod.suanshu.misc.license.InvalidLicense
-
- invdet() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix
-
One over the determinant of H: 1/|H|, which is an integer.
- Inverse - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
For a square matrix A, the inverse, A-1, if
exists, satisfies
A.multiply(A.inverse()) == A.ONE()
There are multiple ways to compute the inverse of a matrix.
- Inverse(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.Inverse
-
Constructs the inverse of a matrix.
- Inverse(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.Inverse
-
Constructs the inverse of a matrix.
- inverse() - Method in interface com.numericalmethod.suanshu.algebra.structure.Field
-
For each a in F, there exists an element b in F such that
a × b = b × a = 1.
- inverse() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- inverse() - Method in class com.numericalmethod.suanshu.number.Real
-
- INVERSE - Static variable in class com.numericalmethod.suanshu.stats.random.variancereduction.AntitheticVariates
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkCloglog
-
- inverse(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkFunction
-
Inverse of the link function, i.e., g-1(x).
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkIdentity
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkInverse
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkInverseSquared
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkLog
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkLogit
-
Inverse of the link function, i.e., g-1(x).
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkProbit
-
- inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkSqrt
-
- INVERSE_FINE_STRUCTURE_ALPHA1 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The inverse fine-structure constant \(\alpha^{-1}\) (dimensionless).
- inverseCovariance() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.daspremont2008.CovarianceEstimation
-
- inverseCovariance() - Method in interface com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionSolver
-
Get the estimated inverse Covariance matrix of the selection problem.
- inverseCovariance() - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
-
Gets the inverse of the estimated covariance matrix.
- inverseCovariance() - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Get the inverse of the estimated covariance matrix.
- InverseIteration - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
-
Inverse iteration is an iterative eigenvalue algorithm.
- InverseIteration(Matrix, double, InverseIteration.StoppingCriterion) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Construct an instance of InverseIteration to find the corresponding eigenvector.
- InverseIteration(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Construct an instance of InverseIteration to find the corresponding eigenvector.
- InverseIteration.StoppingCriterion - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
-
This interface defines the convergence criterion.
- InverseNonExistent() - Constructor for exception com.numericalmethod.suanshu.algebra.structure.Field.InverseNonExistent
-
Construct an instance of InverseNonExistent
- InverseTransformSampling - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
-
Inverse transform sampling (also known as inversion sampling, the inverse probability integral
transform, the inverse transformation method, Smirnov transform, golden rule, etc.)
is a basic method for pseudo-random number sampling,
i.e.
- InverseTransformSampling(ProbabilityDistribution, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.InverseTransformSampling
-
Construct a random number generator to sample from a distribution.
- InverseTransformSampling(ProbabilityDistribution) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.InverseTransformSampling
-
Construct a random number generator to sample from a distribution.
- InverseTransformSamplingEVDRNG - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.rng
-
Generate random numbers according to a given univariate extreme value distribution, by
inverse transform sampling.
- InverseTransformSamplingEVDRNG(UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
-
Create an instance with the given extreme value distribution.
- InverseTransformSamplingEVDRNG(UnivariateEVD, long...) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
-
Create an instance with the given extreme value distribution and seeds for this random number
generator.
- InverseTransformSamplingExpRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.exp
-
This is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method.
- InverseTransformSamplingExpRNG(double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.exp.InverseTransformSamplingExpRNG
-
Construct a random number generator to sample from the exponential distribution using the inverse transform sampling method.
- InverseTransformSamplingExpRNG() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.exp.InverseTransformSamplingExpRNG
-
Construct a random number generator to sample from the standard exponential distribution using the inverse transform sampling method.
- InverseTransformSamplingGammaRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.gamma
-
- InverseTransformSamplingGammaRNG(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.InverseTransformSamplingGammaRNG
-
Deprecated.
Construct a random number generator to sample from the gamma distribution using the inverse transform sampling method.
- InverseTransformSamplingGammaRNG() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.InverseTransformSamplingGammaRNG
-
Deprecated.
Construct a random number generator to sample from the standard gamma distribution using the inverse transform sampling method.
- InverseTransformSamplingTruncatedNormalRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated
-
A random variate x defined as
\[
x = \Phi^{-1}( \Phi(\alpha) + U\cdot(\Phi(\beta)-\Phi(\alpha)))\sigma + \mu
\]
with \(\Phi\) the cumulative distribution function and \(\Phi^{-1}\) its inverse, U a
uniform random number on (0, 1), follows the distribution truncated to the range (a,
b).
- InverseTransformSamplingTruncatedNormalRNG(double, double, double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
-
Construct a rng that samples from a truncated Normal distribution using inverse sampling
technique.
- InverseTransformSamplingTruncatedNormalRNG(double, double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
-
Construct a rng that samples from a truncated Normal distribution using inverse sampling
technique.
- InverseTransformSamplingTruncatedNormalRNG(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
-
Construct a rng that samples from a truncated standard Normal distribution using inverse
sampling technique.
- InvertingVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This is the inverting-variable transformation.
- InvertingVariable(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
Construct an InvertingVariable substitution rule.
- invOfwAtwA() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
(wA' * wA)-1
- IPMinimizer<T extends IPProblem,S extends MinimizationSolution<Vector>> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer
-
An Integer Programming minimizer minimizes an objective function subject to equality/inequality
constraints as well as integral constraints.
- IPProblem - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer
-
An Integer Programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers.
- IPProblemImpl1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer
-
This is an implementation of a general Integer Programming problem in which some variables take only integers.
- IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[], double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
Construct a constrained optimization problem with integral constraints.
- IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.IPProblemImpl1
-
Construct a constrained optimization problem with integral constraints.
- ir() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
-
- is(IntervalRelation, Interval<T>) - Method in class com.numericalmethod.suanshu.interval.Interval
-
Check whether this and Y satisfies a certain Allen's interval relation.
- isAcyclic(UnDiGraph<V, UndirectedEdge<V>>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Check if an undirected graph is acyclic.
- isAcyclic() - Method in class com.numericalmethod.suanshu.graph.type.SparseDAGraph
-
Runs validity check to ensure that this DA graph is indeed acyclic.
- isAllZeros(double[], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if a double array contains only 0s, entry-by-entry.
- isArray(Table) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Check if a table is a row or a column.
- isBalanced() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Checks if the panel is balanced, i.e., all subjects have the same number
of observations (times).
- isBetween(Interval<T>, Interval<T>) - Method in enum com.numericalmethod.suanshu.interval.IntervalRelation
-
Check if X and Y satisfy a certain relation.
- isBracketing(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
Check whether [xl, xu] is bracketing x.
- isCandidate() - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.BBNode
-
Check if this node is a possible solution to the original problem, e.g., not pruned.
- isCandidate() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
- isColumn(Table) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Check if a table is a column.
- isConnected(UnDiGraph<V, ? extends UndirectedEdge<V>>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Check whether an undirected graph is connected.
- isConverged() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
This is the convergence criterion.
- isConverged() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
This genetic algorithm terminates if
the minimum does not improve for a fixed number of iterations, or
the maximum number of iterations is exceeded.
- isConverged() - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
-
Checks if the algorithm converges.
- isCyclic() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS
-
Checks if the graph is cyclic.
- isCyclic - Variable in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node
-
- isCyclic() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node
-
Check whether this node is on a cyclic path of the graph.
- isDiagonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a square matrix is a diagonal matrix, up to a precision.
- isEmpty() - Method in class com.numericalmethod.suanshu.graph.community.EdgeBetweeness
-
Checks if there is no edge, e.g., all vertices are isolated.
- isEmpty() - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.ActiveList
-
Returns true if this collection contains no elements.
- isEmpty() - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- isFat(Table) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Check if a table is fat.
- isFeasible() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Check if this table is feasible.
- isFixedIndex(int, Map<Integer, Double>) - Static method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
Checks whether a particular index corresponds a fixed variable/value.
- isFixedIndex(int) - Method in class com.numericalmethod.suanshu.analysis.function.SubFunction
-
Checks whether a particular index corresponds a fixed variable/value.
- isFree(int) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Check whether xi is a free variable after handling the box constraints.
- isFree(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- isFree(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- isHessenberg(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Check if H is upper Hessenberg.
- isIdempotent(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is idempotent.
- isIdentity(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is an identity matrix, up to a precision.
- isInBox(Vector, Vector, Vector) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
-
Check if a solution is within a box.
- isInfinite(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
-
Check if a complex number is an infinity;
i.e., either the real or the imaginary part is infinite, c.f.,
Double.isInfinite(),
and the number is not a
NaN.
- isInKernel(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
- isLASSO() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSProblem
-
Checks if the LASSO variation of LARS is used.
- isLowerBidiagonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is lower bidiagonal, up to a precision.
- isLowerTriangular(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is lower triangular, up to a precision.
- isMagicSquare(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
- isMinFound() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the convergence criterion
- isMinFound() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BrentMinimizer.Solution
-
the convergence criterion
- isMinFound() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.FibonaccMinimizer.Solution
-
This algorithm stops only after a pre-specified number of iterations.
- isMinFound() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.GoldenMinimizer.Solution
-
- isNaN(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a vector contains any NaN entry.
- isNaN(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
-
Check if a complex number is an NaN;
i.e., either the real or the imaginary part is an NaN.
- isNegative(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if d is negative.
- isNegligible(Matrix, int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
-
Checks if
H[i,j] is negligible by Steward's deflation criterion.
- isNegligible(Matrix, int, int, double) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DeflationCriterion
-
Checks whether a sub-diagonal element is sufficiently small.
- isNullOrEmpty(String) - Static method in class com.numericalmethod.suanshu.misc.StringUtils
-
Checks if a string is either null or empty.
- isNullRejected(double) - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Use the p-value to check whether the null hypothesis can be rejected for a given significance level.
- isNumber(double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if a double is a number, i.e., it is not ∞ or
NaN.
- isOK() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.check.CorrelationCheck
-
- isOK() - Method in interface com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.check.PairingCheck
-
- isOrthogonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is orthogonal, up to a precision.
- isPositive(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if d is positive.
- isPositiveDefinite(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a square matrix is positive definite; the matrix needs not be symmetric.
- isPositiveSemiDefinite(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a square matrix is positive definite, up to a precision.
- isPow2(int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if an integer is a power of 2.
- isProposalAccepted(Vector, Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.HybridMCMC
-
- isProposalAccepted(Vector, Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
-
- isProposalAccepted(Vector, Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
Decides whether the given proposed state should be accepted, or whether the system should
remain in it's current state.
- isProposalAccepted(Vector, Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.Metropolis
-
- isProposalAccepted(Vector, Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.MetropolisHastings
-
- isProposalAccepted(RealScalarFunction, RandomLongGenerator, Vector, Vector) - Static method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.MetropolisUtils
-
Uses the given LOG density function to determine whether the given state transition should be
accepted.
- isProposalAccepted(Vector, Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
-
- isQuasiTriangular(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is quasi (upper) triangular, up to a precision.
- isReal(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
-
Check if this complex number is a real number;
i.e., the imaginary part is 0.
- isReal(Number) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Check if a number is a real number.
- isReducedRowEchelonForm(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is in the reduced row echelon form, up to a precision.
- isReducible(Matrix, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Check if H is upper Hessenberg and is reducible.
- isReducible() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearEqualityConstraints
-
Check if we can reduce the number of linear equalities.
- isResidualSmall(double) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.AbsoluteTolerance
-
- isResidualSmall(double) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.RelativeTolerance
-
- isResidualSmall(double) - Method in interface com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.Tolerance
-
Checks if the updated residual satisfies the tolerance criteria.
- isRow(Table) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Check if a table is a row.
- isRowEchelonForm(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is in the row echelon form, up to a precision.
- isSameDimension(Table, Table) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Check if two tables have the same dimension.
- isSatisfied(Constraints, Vector, double) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.ConstraintsUtils
-
Checks if the constraints are satisfied.
- isSatisfied(Constraints, Vector) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.ConstraintsUtils
-
Checks if the constraints are satisfied.
- isScalar(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
- isSelected(int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection
-
Checks whether a particular indexed factor is selected in the model.
- isSingular(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a square matrix is singular, i.e, having no inverse, up to a precision.
- isSkewSymmetric(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is skew symmetric.
- isSpanned(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Check whether a vector is in the span of the basis.
- isSquare(Table) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Check if a table is square.
- isStopped(Vector, double...) - Method in class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AfterIterations
-
- isStopped(Vector, double...) - Method in class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AfterNoImprovement
-
- isStopped(Vector, double...) - Method in class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AndStopConditions
-
- isStopped(Vector, double...) - Method in class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AtThreshold
-
- isStopped(Vector, double...) - Method in class com.numericalmethod.suanshu.misc.algorithm.stopcondition.OrStopConditions
-
- isStopped(Vector, double...) - Method in interface com.numericalmethod.suanshu.misc.algorithm.stopcondition.StopCondition
-
This is called after each iteration to determine whether the termination conditions are met,
e.g., convergence.
- isStronglyConnected(DiGraph<V, ? extends Arc<V>>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Check whether a directed graph is strongly connected.
- isSymmetric(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is symmetric.
- isSymmetricPositiveDefinite(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a square matrix is symmetric and positive definite.
- isTall(Table) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Check if a table is tall.
- isTridiagonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is tridiagonal, up to a precision.
- isTrue(double, int) - Method in interface com.numericalmethod.suanshu.number.DoubleUtils.which
-
Decide whether x is to be selected.
- isUnique() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.QPSolution
-
Return true if the quadratic programming problem has only one
solution.
- isUnreduced(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
A bi-diagonal matrix is unreduced if it has no 0 on both the super and main
diagonals.
- isUpperBidiagonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is upper bidiagonal, up to a precision.
- isUpperTriangular(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is upper triangular, up to a precision.
- isValidated(Matrix) - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
-
Checks whether a matrix satisfies the row and column sums.
- isWeekend(DateTime) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Check if the given time is a weekend.
- isZero() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Check if the kernel has zero dimension, that is, if A has full rank.
- isZero(Vector, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a vector is a zero vector, i.e., all its entries are 0, up to a precision.
- isZero(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Check if d is zero.
- iter - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- iter - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the current iteration count
- iterate(PrimalDualSolution, Vector, Vector, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.AntoniouLu2007
-
- iterate(PrimalDualSolution, Vector, Vector, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.SDPT3v4
-
- IteratesMonitor<S> - Class in com.numericalmethod.suanshu.misc.algorithm.iterative.monitor
-
- IteratesMonitor() - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.IteratesMonitor
-
Construct a monitor to keep track of the states in all iterations.
- iteration(Elliott2005DLM, double[]) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.ElliottOnlineFilter
-
- iteration - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- IterationBody<T> - Interface in com.numericalmethod.suanshu.misc.parallel
-
This interface defines the code snippet to be run in parallel.
- IterationMonitor<S> - Interface in com.numericalmethod.suanshu.misc.algorithm.iterative.monitor
-
To debug an iterative algorithm, such as in
IterativeMethod, it is
useful to keep track of the all states generated in the iterations.
- IterativeC2Maximizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2
-
A maximization problem is simply minimizing the negative of the objective function.
- IterativeC2Maximizer(T) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.IterativeC2Maximizer
-
Construct a multivariate maximizer to maximize an objective function.
- IterativeC2Maximizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.IterativeC2Maximizer
-
Construct a multivariate maximizer to maximize an objective function.
- IterativeC2Maximizer.Solution - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2
-
- IterativeC2Minimizer - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2
-
This is a minimizer that minimizes a twice continuously differentiable, multivariate function.
- IterativeCentralDifference - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2
-
An iterative central difference algorithm to obtain a numerical approximation to Poisson's
equations with Dirichlet boundary conditions.
- IterativeCentralDifference(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2.IterativeCentralDifference
-
Create an instance of this method with the given error bound as the convergence criterion,
and the maximum number of iterations allowed.
- IterativeIntegrator - Interface in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
An iterative integrator computes an integral by a series of sums, which approximates the value of the integral.
- IterativeLinearSystemSolver - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
-
An iterative method for solving an N-by-N (or non-square) linear system
Ax = b involves a sequence of matrix-vector multiplications.
- IterativeLinearSystemSolver.Solution - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
-
This is the solution to a system of linear equations using an iterative
solver.
- IterativeMethod<S> - Interface in com.numericalmethod.suanshu.misc.algorithm.iterative
-
An iterative method is a mathematical procedure that generates a sequence of
improving approximate solutions for a class of problems.
- IterativeMinimizer<P extends OptimProblem> - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained
-
This is an iterative multivariate minimizer.
- IterativeSolution<S> - Interface in com.numericalmethod.suanshu.optimization
-
Many minimization algorithms work by starting from some given initials and iteratively moving
toward an approximate solution.
- iterator() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- iterator() - Method in class com.numericalmethod.suanshu.combinatorics.Twiddle
-
- iterator() - Method in class com.numericalmethod.suanshu.misc.algorithm.CartesianProduct
-
- iterator() - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- iterator() - Method in class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalGrid
-
- iterator() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
- iterator() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.random.rng.ConstantSeeder
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.DynamicCreator
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timegrid.EvenlySpacedGrid
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timegrid.UnitGrid
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.DateTimeGenericTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
-
- IWLS - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
-
This implementation estimates parameters β in a GLM model using the Iteratively
Re-weighted Least Squares algorithm.
- IWLS(double, int) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
Construct an instance to run the Iteratively Re-weighted Least Squares algorithm.
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
The sub-diagonal entries of the unit lower triangular matrix L.
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot
-
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the lower triangular matrix L, such that P * A = L * U.
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
-
- L() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Cholesky
-
Get the lower triangular matrix L.
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewicz
-
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized
-
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskySparse
-
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyWang2006
-
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Get L as in the LDL decomposition.
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LU
-
- L() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition
-
Get the lower triangular matrix L as in the LU decomposition.
- L() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Gets the lower triangular matrix L in the LDL decomposition.
- Label(SimplexTable.LabelType, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
Construct a label for a row or column in the simplex table.
- lag(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries by lagging the time series.
- lag(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries by lagging the time series.
- lag(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries by lagging the time series.
- lag(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries by lagging the time series.
- LaguerrePolynomials - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule
-
Laguerre polynomials are defined by the recurrence relation below.
- LaguerrePolynomials() - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LaguerrePolynomials
-
- LaguerreRule - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule
-
- LaguerreRule(int, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
Create a Laguerre rule of the given order.
- Lai2010NPEBModel - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010
-
The Non-Parametric Empirical Bayes (NPEB) model described in the reference
computes the optimal weights for asset allocation.
- Lai2010NPEBModel(MVOptimizer, int, ReturnsMoments.Estimator, ReturnsResamplerFactory, CetaMaximizer) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.Lai2010NPEBModel
-
Constructs an instance of the model.
- Lai2010NPEBModel(MVOptimizer, int) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.Lai2010NPEBModel
-
Constructs an instance of the model using
MomentsEstimatorLedoitWolf as the default estimator of
covariance matrix, and assuming IID for each individual asset for
re-sampling.
- Lai2010NPEBModel.OptimalWeights - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010
-
- Lai2010OptimizationAlgorithm - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization
-
- Lai2010OptimizationAlgorithm(MVOptimizer, ReturnsMoments.Estimator, ReturnsResamplerFactory, int, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.Lai2010OptimizationAlgorithm
-
- Lai2010OptimizationAlgorithm(double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.Lai2010OptimizationAlgorithm
-
- lambda - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
The norm of the generator with the sign chosen to be the opposite of the first coordinate
of the generator.
- lambda() - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.LedoitWolf2016.Result
-
Gets sample eigenvalues.
- Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
-
Stores the Beta distribution parameters.
- Lambda(int, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
-
Stores the Binomial distribution parameters.
- Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaMixtureDistribution.Lambda
-
Stores the Gamma distribution parameters.
- Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda
-
Construct a Log-Normal distribution.
- Lambda(double, double) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
-
Construct a Normal distribution.
- lambda() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.UnconstrainedLASSOProblem
-
Gets the penalization parameter for the unconstrained form of LASSO.
- lambda1() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Gets the transition intensity from bull market to bear market.
- lambda1 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.ModelParam
-
- lambda2() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Gets the transition intensity from bear market to bull market.
- lambda2 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.ModelParam
-
- lambda_Jacobian - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
lambda Jacobian
- lambdaCol - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- Lanczos - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The Lanczos approximation is a method for computing the Gamma function numerically, published by Cornelius Lanczos in 1964.
- Lanczos(double, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Construct a Lanczos approximation instance.
- Lanczos() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Construct a Lanczos approximation instance using default parameters.
- LARSFitting - Class in com.numericalmethod.suanshu.stats.regression.linear.lasso.lars
-
This class computes the entire LARS sequence of coefficients and fits,
starting from zero to the OLS fit.
- LARSFitting(LARSProblem, double, int) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting
-
Estimates the entire LARS sequence of coefficients and fits, starting
from zero to the OLS fit.
- LARSFitting(LARSProblem, int) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting
-
Estimates the entire LARS sequence of coefficients with the default
epsilon.
- LARSFitting(LARSProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting
-
Estimates the entire LARS sequence of coefficients with the default
epsilon and maximum number of steps.
- LARSFitting.Estimators - Class in com.numericalmethod.suanshu.stats.regression.linear.lasso.lars
-
- LARSProblem - Class in com.numericalmethod.suanshu.stats.regression.linear.lasso.lars
-
Least Angle Regression (LARS) is a regression algorithm for high-dimensional
data.
- LARSProblem(Vector, Matrix, boolean, boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSProblem
-
Constructs a Least Angel Regression (LARS) problem.
- LARSProblem(Vector, Matrix, boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSProblem
-
Constructs a Least Angel Regression (LARS) problem, where an intercept is
included in the model.
- LARSProblem(Vector, Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSProblem
-
Constructs a Least Angel Regression (LARS) problem, where an intercept is
included in the model and the covariates are normalized first.
- LARSProblem(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSProblem
-
Constructs a LASSO variation of the Least Angel Regression (LARS)
problem, where an intercept is included in the model and the covariates
are normalized first.
- LARSProblem(LARSProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSProblem
-
Copy constructor.
- lastEdge(V) - Method in class com.numericalmethod.suanshu.graph.algorithm.shortestpath.Dijkstra
-
- lastEdge(V) - Method in interface com.numericalmethod.suanshu.graph.algorithm.shortestpath.ShortestPath
-
Gets the last edge of a vertex on its shortest distance from the source.
- lcm(int, int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Calculates the least common multiple of integer a and integer b.
- LD() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- LDDecomposition - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Represents a L D LT decomposition of a shifted symmetric tridiagonal matrix
T.
- LDDecomposition(Vector, Vector, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- LDFactorizationFromRoot - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Decomposes (T - σ I) into L D LT where T is a symmetric
tridiagonal matrix, σ is a shift for this factorization, L is a unit lower
triangular matrix, and D is a diagonal matrix.
- LDFactorizationFromRoot(Vector, Vector, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot
-
Creates a decomposition for a symmetric tridiagonal matrix T.
- LDLt - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle
-
The LDL decomposition decomposes a real and symmetric (hence square) matrix A into A = L * D * Lt.
- LDLt(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Run the LDL decomposition on a real and symmetric (hence square) matrix.
- LDLt(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Run the LDL decomposition on a real and symmetric (hence square) matrix.
- LeapFrogging - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid
-
The leap-frogging algorithm is a method for simulating Molecular Dynamics, which is
time-reversible.
- LeapFrogging(RealVectorFunction, Vector, Vector, Vector, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Constructs a new instance with the given parameters.
- LeapFrogging.DynamicsState - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid
-
Contains the entire state (both the position and the momentum) at a given point in time.
- LeastPth<T> - Class in com.numericalmethod.suanshu.optimization.multivariate.minmax
-
The least p-th minmax algorithm minimizes the maximal error/loss (function):
\[
\min_x \max_{\omega \in S} e(x, \omega)
\]
\(e(x, \omega)\) is the error or loss function.
- LeastPth(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.minmax.LeastPth
-
Construct a minmax minimizer using the Least p-th method.
- LeastSquares - Class in com.numericalmethod.suanshu.analysis.curvefit
-
This method obtains a least squares estimate of a polynomial to fit the input data, by a weighted
sum of orthogonal polynomials up to a specified order.
- LeastSquares(int, LeastSquares.Weighting) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.LeastSquares
-
Construct a new instance of this algorithm, which will use a weighted sum of orthogonal
polynomials up to order n (the number of points).
- LeastSquares(int) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.LeastSquares
-
Construct a new instance of this algorithm, which uses uniform weighting for the
observations.
- LeastSquares.Weighting - Interface in com.numericalmethod.suanshu.analysis.curvefit
-
This interface defines a weighting for observations.
- Lebesgue - Class in com.numericalmethod.suanshu.analysis.integration.univariate
-
Lebesgue integration is the general theory of integration of a function with respect to a general measure.
- Lebesgue(double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.Lebesgue
-
Construct a Lebesgue integral.
- LEcuyer - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear
-
This is the uniform random number generator recommended by L'Ecuyer in 1996.
- LEcuyer() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.LEcuyer
-
Construct a LEcuyer pseudo uniform random generator.
- LEcuyer(long, long, long, long, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.LEcuyer
-
Construct a LEcuyer pseudo uniform random generator and then seed.
- LedoitWolf2004 - Class in com.numericalmethod.suanshu.stats.descriptive.covariance
-
To estimate the covariance matrix, Ledoit and Wolf (2004) suggests using the
matrix obtained from the sample covariance matrix through a transformation
called shrinkage.
- LedoitWolf2004() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004
-
Creates the algorithm instance, using an unbiased sample covariance
matrix by default.
- LedoitWolf2004(boolean) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004
-
Creates the algorithm instance, with the option to use an unbiased or
biased sample covariance matrix.
- LedoitWolf2004.Result - Class in com.numericalmethod.suanshu.stats.descriptive.covariance
-
The estimator and some intermediate values computed by the algorithm.
- LedoitWolf2016 - Class in com.numericalmethod.suanshu.stats.covariance.nlshrink
-
This is Ledoit's non-linear shrinkage method for computing covariance
matrixes when the dimension is large compared to the number of observations.
- LedoitWolf2016() - Constructor for class com.numericalmethod.suanshu.stats.covariance.nlshrink.LedoitWolf2016
-
- LedoitWolf2016(boolean) - Constructor for class com.numericalmethod.suanshu.stats.covariance.nlshrink.LedoitWolf2016
-
- LedoitWolf2016.Result - Class in com.numericalmethod.suanshu.stats.covariance.nlshrink
-
the estimator and some intermediate values computed by the algorithm
- leftConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Get the one sided left confidence interval, [0, a]
- leftConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
Compute the one sided left confidence interval, [0, a]
- leftMultiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Left multiplies a matrix.
- leftOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Get the left, one-sided p-value.
- leftOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
Get the left, one-sided p-value.
- leftOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
Get the left, one-sided p-value.
- leftOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
Get the left, one-sided p-value.
- leftOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
Get the left, one-sided p-value.
- leftShift(T[]) - Static method in class com.numericalmethod.suanshu.misc.ArrayUtils
-
Get a left shifted array.
- leftShift(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Perform a in-memory left-shift (by 1 cell} to an array.
- leftShift(double[], int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Perform a in-memory right-shift (by k cells} to an array.
- leftShiftCopy(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get a left shifted (by 1 cell) copy of an array.
- leftShiftCopy(double[], int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get a left shifted (by k cells) copy of an array.
- LegendrePolynomials - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule
-
A Legendre polynomial is defined by the recurrence relation below.
- LegendrePolynomials() - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LegendrePolynomials
-
- LegendreRule - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule
-
- LegendreRule(int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
Create a Legendre rule of the given order.
- Lehmer - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear
-
Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in
[0, 1].
- Lehmer(long, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.Lehmer
-
Construct a Lehmer (pure) linear congruential generator.
- Lehmer(long, long, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.Lehmer
-
Construct a skipping ahead Lehmer (pure) linear congruential generator.
- Lehmer() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.Lehmer
-
Construct a Lehmer (pure) linear congruential generator.
- length() - Method in class com.numericalmethod.suanshu.analysis.sequence.Fibonacci
-
- length() - Method in interface com.numericalmethod.suanshu.analysis.sequence.Sequence
-
Get the number of computed terms in the sequence.
- length() - Method in class com.numericalmethod.suanshu.geometry.LineSegment
-
Get the length of the line segment.
- LessThanConstraints - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint
-
The domain of an optimization problem may be restricted by less-than or equal-to constraints.
- Levene - Class in com.numericalmethod.suanshu.stats.test.variance
-
The Levene test tests for the equality of variance of groups.
- Levene(double...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.Levene
-
Perform the Levene test to test for equal variances across the groups.
- Levene(Levene.Type, double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.Levene
-
Perform the Levene test to test for equal variances across the groups.
- Levene.Type - Enum in com.numericalmethod.suanshu.stats.test.variance
-
the available implementations when computing the absolute deviations
- leverage() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the leverage.
- License - Class in com.numericalmethod.suanshu.misc.license
-
This is the license management system for the library.
- LICENSE_FILE_PROPERTY - Static variable in class com.numericalmethod.suanshu.misc.license.License
-
The system property term for setting license file.
- LicenseError - Error in com.numericalmethod.suanshu.misc.license
-
General error regarding the license, e.g., errors when loading license.
- LicenseError(String) - Constructor for error com.numericalmethod.suanshu.misc.license.LicenseError
-
- LIGHT_SPEED_C - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The speed of light in a vacuum \(c\), \(c_0\) in meters per second (m s-1).
- Lilliefors - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
-
Lilliefors test tests the null hypothesis that data come from a normally distributed population
with an estimated sample mean and variance.
- Lilliefors(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
-
Perform the Lilliefors test to test for the null hypothesis that data come from a normally
distributed population with an estimated sample mean and variance.
- LILSparseMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
The list of lists (LIL) format for sparse matrix stores one list per row,
where each entry stores a column index and value.
- LILSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Construct a sparse matrix in LIL format.
- LILSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Construct a sparse matrix in LIL format.
- LILSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Construct a sparse matrix in LIL format by a list of non-zero entries.
- LILSparseMatrix(LILSparseMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Copy constructor.
- LinearCongruentialGenerator - Interface in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear
-
A linear congruential generator (LCG) produces a sequence of pseudo-random numbers
based on a linear recurrence relation.
- LinearConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
-
This is a collection of linear constraints for a real-valued optimization problem.
- LinearConstraints(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
Construct a collection of linear constraints.
- LinearEqualityConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
-
This is a collection of linear equality constraints.
- LinearEqualityConstraints(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearEqualityConstraints
-
Construct a collection of linear equality constraints.
- LinearFit - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
Find the parameters for the ACER function from the given empirical epsilon, using OLS regression
on the logarithm of the values.
- LinearFit() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.LinearFit
-
Create an instance with the assumption of c = 2.
- LinearFit(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.LinearFit
-
This fitting assumes c is a constant.
- LinearGreaterThanConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
-
This is a collection of linear greater-than-or-equal-to constraints.
- LinearGreaterThanConstraints(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
-
Construct a collection of linear greater-than or equal-to constraints.
- linearInterpolate(double, double, double, double, double) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Linear interpolation between two points.
- LinearInterpolation - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
-
(Piecewise-)Linear interpolation fits a curve by interpolating linearly between two adjacent
data-points.
- LinearInterpolation() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.LinearInterpolation
-
- LinearInterpolator - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation
-
Define a univariate function by linearly interpolating between adjacent points.
- LinearInterpolator(OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.LinearInterpolator
-
Construct a univariate function by linearly interpolating between adjacent points.
- LinearKalmanFilter - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
The Kalman filter, also known as linear quadratic estimation (LQE),
is an algorithm which uses a series of measurements observed over time,
containing noise (random variations) and other inaccuracies,
and produces estimates of unknown variables that tend to be more precise than those that would be
based on a single measurement alone.
- LinearKalmanFilter(DLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Construct a Kalman filter from a univariate controlled dynamic linear model.
- LinearLessThanConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
-
This is a collection of linear less-than-or-equal-to constraints.
- LinearLessThanConstraints(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearLessThanConstraints
-
Construct a collection of linear less-than or equal-to constraints.
- LinearModel - Interface in com.numericalmethod.suanshu.stats.regression.linear
-
A linear model provides fitting and the residual analysis (goodness of fit).
- LinearRepresentation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.
- LinearRepresentation(ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
Construct the linear representation of an ARMA model.
- LinearRepresentation(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
- LinearRoot - Class in com.numericalmethod.suanshu.analysis.function.polynomial.root
-
This is a solver for finding the roots of a linear equation.
- LinearRoot() - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.root.LinearRoot
-
- LinearSystemSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
-
Solve a system of linear equations in the form:
Ax = b,
We assume that, after row reduction, A has no more rows than columns.
- LinearSystemSolver(double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver
-
Construct a solver for a linear system of equations.
- LinearSystemSolver.NoSolution - Exception in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
-
This is the runtime exception thrown when it fails to solve a system of
linear
equations.
- LinearSystemSolver.Solution - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
-
This is the solution to a linear system of equations.
- LineSearch - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch
-
A line search is often used in another minimization algorithm to improve the current solution in one iteration step.
- linesearch(Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch.LineSearch.Solution
-
Get the increment α so that f(x + α * d) is (approximately) minimized.
- linesearch - Variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- LineSearch.Solution - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch
-
This is the solution to a line search minimization.
- LineSegment - Class in com.numericalmethod.suanshu.geometry
-
Represent a line segment.
- LineSegment(Point, Point) - Constructor for class com.numericalmethod.suanshu.geometry.LineSegment
-
Create a line segment with two given endpoints.
- link() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMFamily
-
Get the link function of this distribution.
- LinkCloglog - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the complementary log-log link function:
g(x) = log(-log(1 - x))
- LinkCloglog() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkCloglog
-
- LinkFunction - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This interface represents a link function g(x) in Generalized Linear Model (GLM).
- LinkIdentity - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the identity link function:
g(x) = x
- LinkIdentity() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkIdentity
-
- LinkInverse - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the inverse link function:
g(x) = 1/x
- LinkInverse() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkInverse
-
- LinkInverseSquared - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the inverse-squared link function:
g(x) = 1/x2
- LinkInverseSquared() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkInverseSquared
-
- LinkLog - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the log link function:
g(x) = log(x)
- LinkLog() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkLog
-
- LinkLogit - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the logit link function:
\[
g(x) = \log(\frac{\mu}{1-\mu})
\]
- LinkLogit() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkLogit
-
- LinkProbit - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the Probit link function,
which is the inverse of cumulative distribution function of the standard Normal distribution N(0, 1).
- LinkProbit() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkProbit
-
- LinkSqrt - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
-
This class represents the square-root link function:
g(x) = sqrt(x)
- LinkSqrt() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkSqrt
-
- linshrink_tau() - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.LedoitWolf2016.Result
-
Gets linear shrinkage tau in ascending order.
- LjungBox - Class in com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
-
The Ljung-Box test (named for Greta M.
- LjungBox(double[], int, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.LjungBox
-
Perform the Ljung-Box test to check auto-correlation in a time series.
- LLD() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- LMBeta - Class in com.numericalmethod.suanshu.stats.regression.linear
-
Beta coefficients are the outcomes of fitting a linear regression model.
- LMBeta(Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMBeta
-
Constructs an instance of Beta.
- LMDiagnostics - Class in com.numericalmethod.suanshu.stats.regression.linear.residualanalysis
-
This class collects some diagnostics measures for the goodness of fit based on the residulas for
a linear regression model.
- LMDiagnostics(LMResiduals) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMDiagnostics
-
Constructs an instance of the Diagnostics from the results of residual analysis.
- LMInformationCriteria - Class in com.numericalmethod.suanshu.stats.regression.linear.residualanalysis
-
The information criteria measure the goodness of fit of an estimated statistical model.
- LMInformationCriteria(LMResiduals) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMInformationCriteria
-
Computes the information criteria from residual analysis.
- LMProblem - Class in com.numericalmethod.suanshu.stats.regression.linear
-
This is a linear regression or a linear model (LM) problem.
- LMProblem(Vector, Matrix, boolean, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Constructs a linear regression problem.
- LMProblem(Vector, Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Constructs a linear regression problem, assuming a constant term (the intercept).
- LMProblem(Vector, Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Constructs a linear regression problem, assuming equal weights to all observations.
- LMProblem(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Constructs a linear regression problem, assuming
a constant term (the intercept)
equal weights assigned to all observations
- LMProblem(LMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Copy constructor.
- LMResiduals - Class in com.numericalmethod.suanshu.stats.regression.linear.residualanalysis
-
This is the residual analysis of the results of a linear regression model.
- LMResiduals(LMProblem, Vector) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Performs residual analysis for a linear regression problem.
- loading(int) - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the loading vector of the i-th principal component.
- loading(int) - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
- loadings() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FAEstimator
-
Gets the rotated loading matrix.
- loadings() - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the matrix of variable loadings.
- loadings() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
- loadings() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbySVD
-
- LocalSearchCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell
-
- LocalSearchCellFactory<P extends OptimProblem,T extends IterativeMinimizer<OptimProblem>> - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local
-
- LocalSearchCellFactory(LocalSearchCellFactory.MinimizerFactory<T>, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory
-
Construct an instance of a LocalSearchCellFactory.
- LocalSearchCellFactory.LocalSearchCell - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local
-
A LocalSearchCell implements the two genetic operations.
- LocalSearchCellFactory.MinimizerFactory<U extends IterativeMinimizer<OptimProblem>> - Interface in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local
-
This factory constructs a new Minimizer for each mutation operation.
- log(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the log of a vector, element-by-element.
- log(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute log(x).
- log(BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute log(x) up to a scale.
- log(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Natural logarithm of a complex number.
- log(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the logs of values.
- logAcceptanceRatio(RealScalarFunction, Vector, Vector) - Static method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.MetropolisUtils
-
Computes the log of the acceptance ratio.
- LogBeta - Class in com.numericalmethod.suanshu.analysis.function.special.beta
-
This class represents the log of Beta function log(B(x, y)).
- LogBeta() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.LogBeta
-
- logDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
- logDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
- logDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
- logDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
- logDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
-
- logDensity(double) - Method in interface com.numericalmethod.suanshu.stats.evt.evd.univariate.UnivariateEVD
-
Get the logarithm of the probability density function at \(x\), that is, \(\log(f(x))\).
- logGamma(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Compute log-gamma for a positive value x.
- logGamma(BigDecimal) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Compute log-gamma for a positive value x to arbitrary precision.
- LogGamma - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The log-Gamma function, \(\log (\Gamma(z))\), for positive real numbers, is the log of the Gamma function.
- LogGamma(LogGamma.Method, Lanczos) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma
-
Construct an instance of log-Gamma.
- LogGamma() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma
-
Construct an instance of log-Gamma.
- LogGamma.Method - Enum in com.numericalmethod.suanshu.analysis.function.special.gamma
-
the available methods to compute \(\log (\Gamma(z))\)
- logGammaQuick(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.gamma.Lanczos
-
Compute log-gamma for a positive value x.
- LogisticBeta - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
-
Beta coefficient estimator, β^, of a logistic regression model.
- LogisticBeta(Vector, LogisticResiduals) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticBeta
-
Construct an instance of Beta.
- LogisticProblem - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
-
A logistic regression problem is a variation of the OLS regression problem.
- LogisticProblem(LMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticProblem
-
Constructs a logistic regression problem from a linear regression problem.
- LogisticRegression - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
-
A logistic regression (sometimes called the logistic model or logit model) is used for prediction
of the probability of occurrence of an event by fitting data to a logit function logistic curve.
- LogisticRegression(LogisticProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
-
Constructs a Logistic instance.
- LogisticRegression(LMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
-
Constructs a Logistic instance.
- LogisticResiduals - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
-
Residual analysis of the results of a logistic regression.
- logLikelihood() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Compute the log-likelihood at the fitted value.
- logLikelihood() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FAEstimator
-
Gets the log-likelihood value.
- logLikelihood() - Method in class com.numericalmethod.suanshu.stats.hmm.ForwardBackwardProcedure
-
Gets the likelihood of the given observations.
- logLikelihood - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHMMEM.TrainedModel
-
the log-likelihood
- logLikelihood() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.GLMFitting
-
- logLikelihood() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
- logLikelihood() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
- logLikelihood(LogisticProblem) - Static method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
-
Constructs the log-likelihood function for a logistic regression problem.
- logMu - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda
-
the log-mean μ ∈ R
- LogNormalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.
- LogNormalDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
Construct a log-normal distribution.
- LogNormalMixtureDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Log-Normal distribution to model the observations.
- LogNormalMixtureDistribution(LogNormalMixtureDistribution.Lambda[], boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution
-
Constructs a log-normal distribution for each state in the HMM model.
- LogNormalMixtureDistribution(LogNormalMixtureDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution
-
Constructs a log-normal distribution for each state in the HMM model.
- LogNormalMixtureDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the log-normal distribution parameters
- LogNormalRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
-
This random number generator samples from the log-normal distribution.
- LogNormalRNG(NormalRNG) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.LogNormalRNG
-
Construct a random number generator to sample from the log-normal distribution.
- LogNormalRNG(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.LogNormalRNG
-
Construct a random number generator to sample from the log-normal distribution.
- logProbability(int[], double[]) - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
Gets the probability of observing the observations and having gone thru
the state sequence.
- logProbability(int[], int[]) - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
Gets the probability of observing the observations and having gone thru
the state sequence.
- logProbability(HmmInnovation[]) - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
-
Gets the probability of observing the observations and having gone thru
the state sequence.
- logSigma - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda
-
the log-standard deviation; shape
- longValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- longValue() - Method in class com.numericalmethod.suanshu.number.Real
-
- longValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
- LoopBody - Interface in com.numericalmethod.suanshu.misc.parallel
-
The implementation of this interface contains the code inside a for-loop
construct.
- lower() - Method in class com.numericalmethod.suanshu.interval.RealInterval
-
Get the lower bound of this interval.
- lower() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
- lower - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- lowerBound() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.CauchyPolynomial
-
Cauchy's lower bound on polynomial zeros is the unique positive root of the Cauchy polynomial.
- lowerBound() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
-
Gets the lower bounds.
- lowerBoundConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
-
Split the box constraints and get the greater-than-the-lower-bounds part.
- LowerBoundConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
-
This is a lower bound constraints such that for all xi's,
xi ≥ b
- LowerBoundConstraints(RealScalarFunction, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LowerBoundConstraints
-
Construct a lower bound constraints for all variables in a function.
- lowerBounds() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
-
Gets the lower bounds.
- LowerTriangularMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle
-
A lower triangular matrix has 0 entries where column index > row index.
- LowerTriangularMatrix(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Constructs a lower triangular matrix of dimension dim * dim.
- LowerTriangularMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Constructs a lower triangular matrix from a 2D double[][] array.
- LowerTriangularMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Constructs a lower triangular matrix from a matrix.
- LowerTriangularMatrix(LowerTriangularMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Copy constructor.
- LPBoundedMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
This is the solution to a bounded linear programming problem.
- LPBoundedMinimizer(SimplexTable) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
Constructs the solution for a bounded linear programming problem.
- LPCanonicalProblem1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem
-
This is a linear programming problem in the 1st canonical form (following the convention in the reference):
min c'x
s.t.
- LPCanonicalProblem1(Vector, Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
-
Construct a linear programming problem in the canonical form.
- LPCanonicalProblem1(Vector, LinearGreaterThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
-
Construct a linear programming problem in the canonical form.
- LPCanonicalProblem1(LPCanonicalProblem2) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
-
Convert a linear programming problem from the 2nd canonical form to the 1st canonical form.
- LPCanonicalProblem2 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem
-
This is a linear programming problem in the 2nd canonical form (following the convention in the wiki):
min c'x
s.t.
- LPCanonicalProblem2(Vector, Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2
-
Construct a linear programming problem in the canonical form.
- LPCanonicalProblem2(Vector, LinearLessThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2
-
Construct a linear programming problem in the canonical form.
- LPCanonicalProblem2(LPCanonicalProblem1) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2
-
Convert a linear programming problem from the 1st canonical form to the 2nd canonical form.
- LPCanonicalSolver - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
This is an LP solver that solves a canonical LP problem in the following form.
- LPCanonicalSolver() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPCanonicalSolver
-
- LPDimensionNotMatched - Exception in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when the dimensions of the objective function and constraints of a linear programming problem are inconsistent.
- LPDimensionNotMatched(String) - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPDimensionNotMatched
-
Construct an instance of LPDimensionNotMatched.
- LPEmptyCostVector - Exception in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when there is no objective function in a linear programming problem.
- LPEmptyCostVector() - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPEmptyCostVector
-
- LPException - Exception in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when there is any problem when solving a linear programming problem.
- LPException() - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPException
-
Construct an instance of LPException.
- LPInfeasible - Exception in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when the LP problem is infeasible, i.e., no solution.
- LPInfeasible() - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPInfeasible
-
- LPMinimizer - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp
-
An LP minimizer minimizes the objective of an LP problem, satisfying all the constraints.
- LPNoConstraint - Exception in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when there is no linear constraint found for the LP problem.
- LPNoConstraint() - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPNoConstraint
-
- LPProblem - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem
-
A linear programming (LP) problem minimizes a linear objective function subject to a collection of linear constraints.
- LPProblemImpl1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem
-
This is an implementation of a linear programming problem,
LPProblem.
- LPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Construct a general linear programming problem.
- LPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearEqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Construct a general linear programming problem with only greater-than-or-equal-to and
equality constraints.
- LPRuntimeException - Exception in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when there is any problem when constructing a linear programming problem.
- LPRuntimeException() - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException
-
Construct an instance of LPRuntimeException.
- LPRuntimeException(String) - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException
-
Construct an instance of LPRuntimeException.
- LPSimplexMinimizer - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
A simplex LP minimizer can be read off from the solution simplex table.
- LPSimplexSolution - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
The solution to a linear programming problem using a simplex method contains an
LPSimplexMinimizer.
- LPSimplexSolver<P extends LPProblem> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
A simplex solver works toward an LP solution by sequentially applying Jordan exchange to a simplex table.
- LPSolution<T extends LPMinimizer> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp
-
A solution to an LP problem contains all information about solving an LP problem such as
whether the problem has a solution (bounded), how many minimizers it has, and the minimum.
- LPSolver<P extends LPProblem,S extends LPSolution<?>> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp
-
An LP solver solves a Linear Programming (LP) problem.
- LPStandardProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem
-
This is a linear programming problem in the standard form:
min c'x
s.t.
- LPStandardProblem(Vector, LinearEqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPStandardProblem
-
Construct a linear programming problem in the standard form.
- LPTwoPhaseSolver - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
This implementation solves a linear programming problem,
LPProblem, using a two-step approach.
- LPTwoPhaseSolver(LPSimplexSolver<LPCanonicalProblem1>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
Construct an LP solver to solve LP problems.
- LPTwoPhaseSolver() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
Construct an LP solver to solve LP problems.
- LPUnbounded - Exception in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when the LP problem is unbounded.
- LPUnbounded(int) - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPUnbounded
-
Construct an instance of LPUnbounded.
- LPUnboundedMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
This is the solution to an unbounded linear programming problem.
- LPUnboundedMinimizer(SimplexTable, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
Construct the solution for an unbounded linear programming problem.
- LPUnboundedMinimizerScheme2 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
This is the solution to an unbounded linear programming problem found in scheme 2.
- LPUnboundedMinimizerScheme2(SimplexTable, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
-
Construct the solution for an unbounded linear programming problem as a result of applying scheme 2.
- LSProblem - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
-
This is the problem of solving a system of linear equations.
- LSProblem(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Constructs a system of linear equations Ax = b.
- Lt() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
-
Gets the transpose of the lower triangular matrix, L'.
- Lt() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Get the transpose of L as in the LDL decomposition.
- Lt() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Gets the transpose of the lower triangular matrix L in the LDL decomposition.
- LU - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle
-
LU decomposition decomposes an n x n matrix A so that P * A = L * U.
- LU(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LU
-
Run the LU decomposition on a square matrix.
- LU(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LU
-
Run the LU decomposition on a square matrix.
- LUDecomposition - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle
-
LU decomposition decomposes an n x n matrix A so that P * A = L * U.
- LUSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
-
Use LU decomposition to solve Ax = b where A is square and
det(A) != 0.
- LUSolver() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LUSolver
-
- m() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
-
Gets the number of interior x-axis grid points in the solution.
- M() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
-
Gets the number of interior time-axis grid points in the solution.
- m() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the number of interior time-axis grid points in the solution.
- m() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets the dimension of the system, i.e., m = the dimension of y.
- m() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSProblem
-
Gets the number of covariates (number of columns of X), excluding
the intercept.
- m0() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
-
Get the the mean of x0.
- m0() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
-
Get the the mean of x0.
- MA() - Method in class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
-
Get the MA coefficients.
- MA(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the i-th MA coefficient; MA(0) = 1.
- MA(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the i-th MA coefficient; MA(0) = 1.
- MACH_EPS - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
the machine epsilon
This is the difference between 1 and the smallest exactly representable number greater than
1.
- MACH_SCALE - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
the scale for the machine epsilon
- MADecomposition - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
This class decomposes a time series into the trend, seasonal and stationary random components
using the Moving Average Estimation method with symmetric window.
- MADecomposition(double[], double[], int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Decompose a time series into the trend, seasonal and stationary random components using the
Moving Average Estimation method.
- MADecomposition(double[], int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Decompose a time series into the trend, seasonal and stationary random components using the
default filter.
- MADecomposition(double[], int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Decompose a periodic time series into the seasonal and stationary random components using no
MA filter.
- MAGNETIC_FLUX_QUANTUM_PHI0 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The magnetic flux quantum \(\Phi_0\) in webers (Wb).
- MAGNETIC_MU0 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The magnetic constant \(\mu_0\) in henries per meter (H m-1) or newtons per ampere
squared (N A-2).
- main(String[]) - Static method in class com.numericalmethod.suanshu.misc.license.FloatingLicenseServer
-
- MAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
-
This class represents a univariate MA model.
- MAModel(double, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model.
- MAModel(double, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model with unit variance.
- MAModel(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model with zero-mean.
- MAModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model with unit variance and zero-mean.
- MAModel(MAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Copy constructor.
- mapClusterIndices(List<List<Integer>>) - Static method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModelUtils
-
- marginalInverseTransform(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
Inverse of marginal transform.
- marginalTransform(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
Transform to exponential margins under the GEV model.
- MarketImpact1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Constructs the constraint coefficient arrays of a market impact term in the
compact form.
- MarketImpact1(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
-
Constructs a market impact term.
- MARMAModel - Class in com.numericalmethod.suanshu.stats.evt.timeseries
-
Simulation of max autoregressive moving average processes, i.e., MARMA(p, q) processes.
- MARMAModel(UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
-
Create an instance with a given GEV distribution for generating innovations.
- MARMAModel(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
-
Create an instance with the AR and MA coefficients, using
FrechetDistribution as the
GEV distribution.
- MARMAModel(double[], double[], UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
-
Create an instance with the AR and MA coefficients, and a GEV distribution for generating
innovations.
- MARMASim - Class in com.numericalmethod.suanshu.stats.evt.timeseries
-
Generate random numbers based on a given MARMA model.
- MARMASim(MARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMASim
-
- MARMASim(MARMAModel, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMASim
-
Create an instance with the given
MARMAModel, but override the innovation generation
by the the given generator.
- MARMASim(MARMAModel, RandomNumberGenerator, double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARMASim
-
Create an instance with the given
MARMAModel and initial values, but override the
innovation generation by the the given generator.
- MARModel - Class in com.numericalmethod.suanshu.stats.evt.timeseries
-
This is equivalent to MARMA(p, 0).
- MARModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARModel
-
Create an instance with the AR coefficients, using
FrechetDistribution as the GEV
distribution.
- MARModel(double[], UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MARModel
-
Create an instance with the AR coefficients, and a GEV distribution for generating
innovations.
- MarsagliaBray1964 - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.normal
-
The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method
for generating a pair of independent standard normal random variables.
- MarsagliaBray1964(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.MarsagliaBray1964
-
Construct a random number generator to sample from the standard Normal distribution.
- MarsagliaBray1964() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.MarsagliaBray1964
-
Construct a random number generator to sample from the standard Normal distribution.
- MarsagliaTsang2000 - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.gamma
-
Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled
normal variate.
- MarsagliaTsang2000(double, double, RandomStandardNormalGenerator, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the gamma distribution.
- MarsagliaTsang2000(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the gamma distribution.
- MarsagliaTsang2000() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the standard gamma distribution.
- Mass(X, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.discrete.ProbabilityMassFunction.Mass
-
Creates an instance with an outcome and its associated probability.
- MAT - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
MAT is the inverse operator of
SVEC.
- MAT(Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MAT
-
Constructs the MAT of a vector.
- MathTable - Class in com.numericalmethod.suanshu.misc.datastructure
-
A mathematical table consists of numbers showing the results of calculation with varying
arguments.
- MathTable(String...) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Constructs an empty table by headers.
- MathTable(int) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Constructs an empty table.
- MathTable.Row - Class in com.numericalmethod.suanshu.misc.datastructure
-
A row is indexed by a number and contains multiple values.
- Matrix - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles
-
This interface defines a
Matrix as a
Ring, a
Table, and a few more methods not already defined in its mathematical definition.
- MatrixAccess - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles
-
This interface defines the methods for accessing entries in a matrix.
- MatrixAccessException - Exception in com.numericalmethod.suanshu.algebra.linear.matrix
-
This is the runtime exception thrown when trying to access an invalid entry in a matrix, e.g., A[0, 0].
- MatrixAccessException() - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.MatrixAccessException
-
Construct an instance of MatrixAccessException.
- MatrixAccessException(String) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.MatrixAccessException
-
Construct an instance of MatrixAccessException with a message.
- MatrixCoordinate - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
The location of a matrix entry is specified by a 2D coordinates (i, j), where i and
j are the row-index and column-index of the entry respectively.
- MatrixCoordinate(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
Construct a matrix coordinate specifying an entry location.
- MatrixFactory - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
These are the utility functions to create a new matrix/vector from existing ones.
- MatrixMathOperation - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation
-
This interface defines some standard operations for generic matrices.
- MatrixMeasure - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
A measure, μ, of a matrix, A, is a map from the Matrix space to the Real line.
- MatrixMeasure() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
- MatrixMismatchException - Exception in com.numericalmethod.suanshu.algebra.linear.matrix
-
This is the runtime exception thrown when an operation acts on matrices that have incompatible dimensions.
- MatrixMismatchException() - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.MatrixMismatchException
-
Construct an instance of MatrixMismatchException.
- MatrixMismatchException(String) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.MatrixMismatchException
-
Construct an instance of MatrixMismatchException with a message.
- MatrixPropertyUtils - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles
-
These are the boolean operators that take matrices or vectors and check if they satisfy a
certain property.
- MatrixRing - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles
-
A matrix ring is the set of all
n × n matrices over an arbitrary
Ring R.
- MatrixRootByDiagonalization - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
The square root of a matrix extends the notion of square root from numbers to matrices.
- MatrixRootByDiagonalization(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixRootByDiagonalization
-
Constructs the square root of a Matrix by diagonalization.
- MatrixSingularityException - Exception in com.numericalmethod.suanshu.algebra.linear.matrix
-
This is the runtime exception thrown when an operation acts on a singular matrix, e.g., applying LU decomposition to a singular matrix.
- MatrixSingularityException() - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.MatrixSingularityException
-
Construct an instance of MatrixSingularityException.
- MatrixSingularityException(String) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.MatrixSingularityException
-
Construct an instance of MatrixSingularityException with a message.
- MatrixTable - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles
-
A matrix is represented by a rectangular table structure with accessors.
- MatrixUtils - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
These are the utility functions to apply to matrices.
- MatthewsDavies - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite
-
Matthews and Davies propose the following way to coerce a non-positive definite Hessian matrix to
become symmetric, positive definite.
- MatthewsDavies(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Constructs a symmetric, positive definite matrix using the Matthews-Davies algorithm.
- max(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the maximal entry in a matrix.
- max(DateTime, DateTime) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Return the later of two DateTime instances.
- max(double...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the maximum of the values.
- max(int...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the maximum of the values.
- Max - Class in com.numericalmethod.suanshu.stats.descriptive.rank
-
The maximum of a sample is the biggest value in the sample.
- Max() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
Construct an empty Max calculator.
- Max(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
Construct a Max calculator,
initialized with a sample.
- Max(Max) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
Copy constructor.
- max_abs_cor() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the estimated sequence of maximal absolute correlations.
- MAX_D - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
-
- MAX_ITERATION - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
-
- MAX_P - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
-
- MAX_Q - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
-
- maxBinSize() - Method in class com.numericalmethod.suanshu.misc.algorithm.Bins
-
Gets the maximal size of the bins.
- maxClusterSize() - Method in class com.numericalmethod.suanshu.graph.community.GirvanNewman
-
Get the size of the maximal cluster.
- maxDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Get the biggest abscissae.
- maxEdge() - Method in class com.numericalmethod.suanshu.graph.community.EdgeBetweeness
-
Gets the edge with the maximal edge-betweeness.
- maxEdge() - Method in class com.numericalmethod.suanshu.graph.community.GirvanNewman
-
Gets the edge with the maximal edge-betweeness.
- MaximaDistribution - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate
-
The distribution of \(M\), where \(M=\max(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn
from of a random variable \(X\) with cdf \(F(x)\).
- MaximaDistribution(ProbabilityDistribution, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
Create an instance with the probability distribution of \(X\), and the number of iid samples
to be drawn.
- MaximizationSolution<T> - Interface in com.numericalmethod.suanshu.optimization
-
This is the solution to a maximization problem.
- maximizer() - Method in interface com.numericalmethod.suanshu.optimization.MaximizationSolution
-
Get the maximizer (solution) to the maximization problem.
- maximum() - Method in interface com.numericalmethod.suanshu.optimization.MaximizationSolution
-
Get the (approximate) maximum found.
- MaximumLikelihoodFitting - Interface in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting
-
This interface defines model fitting by maximum likelihood algorithm.
- maxIndex(boolean, int, int, double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get the index of the maximum of the values, skipping
Double.NaN.
- maxIndex(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get the index of the maximum of the values, skipping
Double.NaN.
- maxIterations - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
- maxIterations - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- maxIterations - Variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
the maximum number of iterations
- maxIterations - Variable in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer
-
the maximum number of iterations
- MaxIterationsExceededException(int) - Constructor for exception com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.MaxIterationsExceededException
-
Construct a new MaxIterationsExceededException, indicating the number of iterations.
- Maxmizer<P extends OptimProblem,S extends MaximizationSolution<?>> - Interface in com.numericalmethod.suanshu.optimization
-
This interface represents an optimization algorithm that maximizers a real valued objective
function, one or multi dimension.
- maxOrder() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.DividedDifferences
-
Get the maximum order which is limited by the number of points given for the computation.
- maxPQ() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the maximum of AR length or MA length.
- maxPQ() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the maximum of AR length or MA length.
- maxPQ() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the maximum of the ARCH length or GARCH length.
- maxValue() - Method in class com.numericalmethod.suanshu.graph.community.EdgeBetweeness
-
Gets the maximum of edge-betweeness-es.
- maxValue() - Method in class com.numericalmethod.suanshu.graph.community.GirvanNewman
-
Get the maximum of edge-betweeness.
- maxZ() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.KagiModel
-
- McCormickMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
-
- McCormickMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.McCormickMinimizer
-
- MCUtils - Class in com.numericalmethod.suanshu.stats.markovchain
-
These are the utility functions to examine a Markov chain.
- mean() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
Computes the sample mean of the in-sample spread.
- mean() - Method in interface com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MeanEstimator
-
- mean() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MeanEstimatorMaxLevelShift
-
- mean() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
Get the sample mean.
- Mean - Class in com.numericalmethod.suanshu.stats.descriptive.moment
-
The mean of a sample is the sum of all numbers in the sample,
divided by the sample size.
- Mean() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
Construct an empty Mean calculator.
- Mean(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
Construct a Mean calculator,
initialized with a sample.
- Mean(Mean) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
Copy constructor.
- mean() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Get the sample mean.
- mean() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Get the sample mean.
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution
-
- mean() - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the mean of this distribution.
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
Gets the mean of this distribution.
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- mean() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Gets the mean of this distribution.
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
Gets the mean of this distribution.
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.AbstractBivariateEVD
-
- mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
- mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
\[
\mu + \frac{\sigma}{1-\xi}
\]
for \(\xi < 1\).
- mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
-
- mean() - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the sample means that were subtracted.
- mean() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbySVD
-
- mean() - Method in interface com.numericalmethod.suanshu.stats.random.Estimator
-
Gets the expectation of the estimator.
- mean() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralExpectation
-
Compute the mean of the integral.
- mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- mean() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- mean() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- mean() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- mean1() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Get the mean of the first sample.
- mean2() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Get the mean of the second sample.
- MeanEstimator - Interface in com.numericalmethod.suanshu.finance.trading.meanreversion.volarb
-
Defines how to estimate the mean price.
- MeanEstimator - Interface in com.numericalmethod.suanshu.stats.random
-
- MeanEstimatorMaxLevelShift - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.volarb
-
- MeanEstimatorMaxLevelShift(int, double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MeanEstimatorMaxLevelShift
-
- MeanEstimatorMaxLevelShift(int, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MeanEstimatorMaxLevelShift
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
Gets the median of this distribution.
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- median() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Gets the median of this distribution.
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
- median() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
\[
\mu + \frac{\sigma( 2^{\xi} -1)}{\xi}
\]
- median() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
-
- median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- median() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- median() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Deprecated.
- median() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Deprecated.
- mergeIntervals(RealInterval[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenBoundUtils
-
- MersenneExponent - Enum in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation
-
The value of a Mersenne Exponent p is a parameter for creating a Mersenne-Twister random
number generator with a period of 2p.
- MersenneTwister - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister
-
Mersenne Twister is one of the best pseudo random number generators
available.
- MersenneTwister() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
Constructs a random number generator to sample uniformly from [0, 1).
- MersenneTwister(long...) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
Constructs a random number generator to sample uniformly from [0, 1).
- MersenneTwister(MersenneTwisterParam) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
Constructs a new instance, which uses parameters and the state contained
in the given
MersenneTwisterParam instance.
- MersenneTwister(MersenneTwisterParam, long...) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
- MersenneTwisterParam - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister
-
- MersenneTwisterParam() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- MersenneTwisterParam(int, int, int, int, int, int, int, int, int, int, int, int, int, long, long) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- MersenneTwisterParamSearcher - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation
-
Searches for Mersenne-Twister parameters.
- MersenneTwisterParamSearcher(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
-
Constructs a new instance which uses the given RNG to do the parameter search.
- MersenneTwisterParamSearcher(RandomLongGenerator, MersenneExponent) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
-
Constructs a new instance which uses the given RNG to do the parameter search, with the given
period parameter.
- Metropolis - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis
-
This basic Metropolis implementation assumes using symmetric proposal function.
- Metropolis(RealScalarFunction, RealVectorFunction, Vector, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.Metropolis
-
Constructs a new instance with the given parameters.
- Metropolis(RealScalarFunction, Vector, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.Metropolis
-
Constructs a new instance, which draws the offset of the next proposed state from the
previous state from a standard Normal distribution, with the given variance and zero
covariance.
- Metropolis(RealScalarFunction, Vector, Matrix, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.Metropolis
-
Constructs a new instance, which draws the offset of the next proposed state from the
previous state from a standard Normal distribution, multiplied by the given scale matrix.
- MetropolisAcceptanceProbabilityFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
-
Uses the classic Metropolis rule, f_{t+1}/f_t.
- MetropolisAcceptanceProbabilityFunction() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.MetropolisAcceptanceProbabilityFunction
-
- MetropolisHastings - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis
-
A generalization of the
Metropolis algorithm, which allows asymmetric proposal
functions.
- MetropolisHastings(RealScalarFunction, ProposalFunction, MetropolisHastings.ProposalDensityFunction, Vector, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.MetropolisHastings
-
Constructs a new instance with the given parameters.
- MetropolisHastings.ProposalDensityFunction - Interface in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis
-
Defines the density of a proposal function, i.e.
- MetropolisUtils - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis
-
Utility functions for Metropolis algorithms.
- Midpoint - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes
-
The midpoint rule computes an approximation to a definite integral,
made by finding the area of a collection of rectangles whose heights are determined by the values of the function.
- Midpoint(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Midpoint
-
Construct an integrator that implements the Midpoint rule.
- MilsteinSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete
-
Milstein scheme is a first-order approximation to a continuous-time SDE.
- MilsteinSDE(SDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.MilsteinSDE
-
Discretize a continuous-time SDE using the Milstein scheme.
- min(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the minimal entry in a matrix.
- min(DateTime, DateTime) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Return the earlier of two DateTime instances.
- min(double...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the minimum of the values.
- min() - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.BruteForceMinimizer.Solution
-
- Min - Class in com.numericalmethod.suanshu.stats.descriptive.rank
-
The minimum of a sample is the smallest value in the sample.
- Min() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
Construct an empty Min calculator.
- Min(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
Construct a Min calculator,
initialized with a sample.
- Min(Min) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
Copy constructor.
- MIN_P - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
-
- MIN_Q - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
-
- minDomain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Get the smallest abscissae.
- MinimaDistribution - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate
-
The distribution of \(M\), where \(M=\min(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn
from of a random variable \(X\) with cdf \(F(x)\).
- MinimaDistribution(ProbabilityDistribution, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
- MinimalResidualSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Minimal Residual method (MINRES) is useful for solving a symmetric n-by-n linear system
(possibly indefinite or singular).
- MinimalResidualSolver(PreconditionerFactory, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
Construct a MINRES solver.
- MinimalResidualSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
Construct a MINRES solver.
- MinimizationSolution<T> - Interface in com.numericalmethod.suanshu.optimization
-
This is the solution to a minimization problem.
- minimizer() - Method in class com.numericalmethod.suanshu.misc.algorithm.bb.BranchAndBound
-
- minimizer() - Method in interface com.numericalmethod.suanshu.optimization.MinimizationSolution
-
Get the minimizer (solution) to the minimization problem.
- Minimizer<P extends OptimProblem,S extends MinimizationSolution<?>> - Interface in com.numericalmethod.suanshu.optimization
-
This interface represents an optimization algorithm that minimizes a real valued objective
function, one or multi dimension.
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
This is the same as the u vector, such that the direction of arbitrarily negative can
be computed by adjusting λ.
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
-
- minimizer() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.QPSolution
-
Get a minimizing vector.
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.BruteForceMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- minimizer() - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer.Solution
-
- minimizers() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
Get all optimal minimizers.
- minimum() - Method in class com.numericalmethod.suanshu.misc.algorithm.bb.BranchAndBound
-
- minimum() - Method in interface com.numericalmethod.suanshu.optimization.MinimizationSolution
-
Get the (approximate) minimum found.
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
Get the (approximate) minimum found.
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.BruteForceMinimizer.Solution
-
Deprecated.
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- minimum() - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer.Solution
-
- MinimumWeights - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint
-
This constraint puts lower bounds on weights.
- MinimumWeights(Vector) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint.MinimumWeights
-
- minIndex(boolean, int, int, double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get the index of the minimum of the values, skipping
Double.NaN.
- minIndex(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get the index of the minimum of the values, skipping
Double.NaN.
- MinMaxMinimizer<T> - Interface in com.numericalmethod.suanshu.optimization.multivariate.minmax
-
A minmax minimizer minimizes a minmax problem.
- MinMaxProblem<T> - Interface in com.numericalmethod.suanshu.optimization.multivariate.minmax
-
A minmax problem is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss while maximizing the potential gain.
- minorMatrix(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Gets the minor matrix of a given matrix, by removing a specified row and a specified column.
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- minus(Matrix) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixRing
-
this - that
- minus(DenseData) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Subtract the elements in this by that, element-by-element.
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Computes the difference between two diagonal matrices.
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- minus(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- minus(MatrixAccess, MatrixAccess) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A1 - A2
- minus(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- minus(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- minus(SparseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- minus(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- minus(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- minus(ComplexMatrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- minus(GenericFieldMatrix<F>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- minus(RealMatrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- minus(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- minus(DenseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- minus(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- minus(Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
A vector subtracts another vector, element-by-element.
- minus(Vector, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Subtracts a constant from a vector, element-by-element.
- minus(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- minus(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- minus(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- minus(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
\(this - that\)
- minus(double) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Subtract a constant from all entries in this vector.
- minus(G) - Method in interface com.numericalmethod.suanshu.algebra.structure.AbelianGroup
-
- : G × G → G
The operation "
-" is not in the definition of of an additive group but can be deduced.
- minus(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- minus(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- minus(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
- minus(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
-
Subtract one double array from another, entry-by-entry.
- minus(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- minus(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- minus(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- minusWeekdayPeriod(DateTime, Period) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Subtract a weekday-period (i.e., skipping weekends) from a
DateTime.
- minZ() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.KagiModel
-
- MixedRule - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
The mixed rule is good for functions that fall off rapidly at infinity, e.g., \(e^{x^2}\) or \(e^x\)
The integral region is \((0, +\infty)\).
- MixedRule(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.MixedRule
-
Construct a MixedRule substitution rule.
- MixtureDistribution - Interface in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
This is the conditional distribution of the observations in each state
(possibly differently parameterized) of a mixture hidden Markov model.
- MixtureHMM - Class in com.numericalmethod.suanshu.stats.hmm.mixture
-
This is the mixture hidden Markov model (HMM).
- MixtureHMM(Vector, Matrix, MixtureDistribution) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHMM
-
Constructs a mixture hidden Markov model.
- MixtureHMM(MixtureHMM) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHMM
-
Copy constructor.
- MixtureHMMEM - Class in com.numericalmethod.suanshu.stats.hmm.mixture
-
The EM algorithm is used to find the unknown parameters of a hidden Markov
model (HMM) by making use of the forward-backward algorithm.
- MixtureHMMEM(double[], MixtureHMM, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHMMEM
-
Constructs a mixture HMM model by training an initial model using the
Baum-Welch algorithm.
- MixtureHMMEM.TrainedModel - Class in com.numericalmethod.suanshu.stats.hmm.mixture
-
the result of the EM algorithm
- ML() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
-
Gets the maximum log-likelihood.
- MMAModel - Class in com.numericalmethod.suanshu.stats.evt.timeseries
-
This is equivalent to MARMA(0, q).
- MMAModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MMAModel
-
Create an instance with the MA coefficients, using
FrechetDistribution as the GEV
distribution.
- MMAModel(double[], UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.timeseries.MMAModel
-
Create an instance with the MA coefficients, and a GEV distribution for generating
innovations.
- mod(long, long) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Compute the positive modulus of a number.
- mode() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution
-
- mode() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution
-
- mode() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution
-
- mode() - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the mode of this distribution.
- mode() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution
-
- mode() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.AbstractBivariateEVD
-
- model - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHMMEM.TrainedModel
-
the newly trained model as a result of the EM algorithm
- ModelNotFound(String) - Constructor for exception com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection.ModelNotFound
-
Construct a ModelNotFound exception with an error message.
- ModelParam(double, double, double, double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.ModelParam
-
- ModelParam(double, double, double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.ModelParam
-
- ModelResamplerFactory - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler
-
- ModelResamplerFactory(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
-
- ModelResamplerFactory() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
-
- modpow(long, long, long) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
be mod m
- modulus() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the modulus.
- modulus() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- modulus() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.LEcuyer
-
- modulus() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.Lehmer
-
- modulus() - Method in interface com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.LinearCongruentialGenerator
-
Get the modulus of this linear congruential generator.
- modulus() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.MRG
-
- MOLAR_GAS_R - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The molar gas constant \(R\) in joule per kelvin mole (J mol-1 K-1).
- moment(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution
-
- moment(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution
-
- moment(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution
-
- moment(Vector) - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
-
The moment generating function is the expected value of etX.
- moment(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- moment(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
The moment generating function is the expected value of etX.
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- moment(Vector) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.AbstractBivariateEVD
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
-
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Deprecated.
- moment(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Deprecated.
- moment1() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
-
- moment2() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
-
- moments() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
-
- Moments - Class in com.numericalmethod.suanshu.stats.descriptive.moment
-
Compute the central moment of a data set incrementally.
- Moments(int) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Construct an empty moment calculator, computing all moments up to and including the order-th moment.
- Moments(int, double...) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Construct a moment calculator, computing all moments up to and including the order-th moment.
- Moments(Moments) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Copy constructor.
- MomentsEstimatorLedoitWolf - Class in com.numericalmethod.suanshu.finance.trading.returns.moments
-
- MomentsEstimatorLedoitWolf() - Constructor for class com.numericalmethod.suanshu.finance.trading.returns.moments.MomentsEstimatorLedoitWolf
-
- Monoid<G> - Interface in com.numericalmethod.suanshu.algebra.structure
-
A monoid is a group with a binary operation (×), satisfying the group axioms:
closure
associativity
existence of multiplicative identity
- moveColumn2End(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Swaps a column of a permutation matrix with the last column.
- moveRow2End(int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Swaps a row of the permutation matrix with the last row.
- MovingAverage - Class in com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
-
This applies a linear filter to a univariate time series using the moving average estimation.
- MovingAverage(double[], MovingAverage.Side) - Constructor for class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage
-
Construct a moving average filter.
- MovingAverage(double[]) - Constructor for class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage
-
Construct a moving average filter using a symmetric window.
- MovingAverage.Side - Enum in com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
-
the available types of moving average filtering
- MovingAverageByExtension - Class in com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles
-
This implements a moving average filter with these properties:
1) both past and future observations are used in smoothing;
2) the head is prepended with the first element in the inputs (x_t = x_1 for t < 1);
3) the tail is appended with the last element in the inputs (x_t = x_n for t > n).
- MovingAverageByExtension(double[]) - Constructor for class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverageByExtension
-
Construct a moving average filter with prepending and appending.
- MR3 - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Computes eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T using
"Algorithm of Multiple Relatively Robust Representations" (MRRR).
- MR3(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Creates an instance for computing eigenvalues and eigenvectors of a given symmetric
tridiagonal matrix T.
- MR3(Vector, Vector, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Creates an instance for computing eigenvalues (and eigenvectors) of a given symmetric
tridiagonal matrix T.
- MR3(Vector, Vector, boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Creates an instance for computing eigenvalues (and eigenvectors) of a given symmetric
tridiagonal matrix T.
- MRG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear
-
A Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form:
- MRG(long, long...) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.MRG
-
Construct a Multiple Recursive Generator.
- MRModel - Interface in com.numericalmethod.suanshu.finance.trading.meanreversion.volarb
-
A Mean Reversion Model computes the target position given the current price.
- MRModelRanged - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.volarb
-
- MRModelRanged(double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MRModelRanged
-
- MRModelRanged(double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MRModelRanged
-
- mu(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the convection coefficient at a given time t and a position
x.
- mu() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Gets the long term mean.
- mu - Variable in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer.MVOptimizer.Result
-
- mu() - Method in class com.numericalmethod.suanshu.finance.trading.returns.moments.ReturnsMoments
-
Gets the mean vector.
- mu - Variable in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
- mu - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
-
the mean
- mu() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.GLMFitting
-
Gets μ as in
E(Y) = μ = g-1(Xβ)
- mu() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
- mu() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
- mu() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get the drift: \(\mu(t,X_t,Z_t,...)\).
- mu() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
-
- mu() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUProcess
-
Get the overall mean.
- mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the intercept (constant) vector.
- mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the intercept vector.
- mu() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the intercept (constant) term.
- mu1 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.ModelParam
-
- mu1() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Gets the drift in the bull market.
- mu2 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.ModelParam
-
- mu2() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Gets the drift in the bear market.
- MultiCubicSpline - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
-
Implementation of natural cubic spline interpolation for an arbitrary number of dimensions.
- MultiCubicSpline() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultiCubicSpline
-
Create an instance with
CubicSpline as the implementation of the univariate cubic
spline interpolation algorithm.
- MultiDimensionalArray<T> - Class in com.numericalmethod.suanshu.misc.datastructure
-
A generic multi-dimensional array, with an arbitrary number of dimensions.
- MultiDimensionalArray(int...) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalArray
-
Creates an instance with the specified size along each dimension.
- MultiDimensionalArray(MultiDimensionalCollection<T>) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalArray
-
A copy constructor that constructs a shallow copy of the given collection of instances.
- MultiDimensionalCollection<T> - Interface in com.numericalmethod.suanshu.misc.datastructure
-
A generic collection with an arbitrary number of dimensions.
- MultiDimensionalGrid - Class in com.numericalmethod.suanshu.misc.datastructure
-
An arbitrary dimensional grid.
- MultiDimensionalGrid(Double[]...) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalGrid
-
Constructs a multi-dimensional grid of points.
- MultiDimensionalGrid(double[]...) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalGrid
-
Constructs a multi-dimensional grid of points.
- MultiDimensionalGrid(MultiDimensionalGrid.Discretization...) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalGrid
-
Constructs a multi-dimensional grid of points.
- MultiDimensionalGrid.Discretization - Class in com.numericalmethod.suanshu.misc.datastructure
-
Specifies the discretization of an interval.
- MultiLinearInterpolation - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
-
Implementation of linear interpolation for an arbitrary number of dimensions.
- MultiLinearInterpolation() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultiLinearInterpolation
-
Create an instance with
LinearInterpolation as the implementation of the univariate
linear interpolation algorithm.
- MultinomialBetaFunction - Class in com.numericalmethod.suanshu.analysis.function.special.beta
-
A multinomial Beta function is defined as:
\[
\frac{\prod_{i=1}^K \Gamma(\alpha_i)}{\Gamma\left(\sum_{i=1}^K
\alpha_i\right)},\qquad\boldsymbol{\alpha}=(\alpha_1,\cdots,\alpha_K)
\]
- MultinomialBetaFunction(int) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.MultinomialBetaFunction
-
Constructs an instance of a multinomial Beta function.
- MultinomialDistribution - Class in com.numericalmethod.suanshu.stats.distribution.multivariate
-
- MultinomialDistribution(int, double...) - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution
-
Constructs an instance of a Multinomial distribution.
- MultinomialRVG - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate
-
A multinomial distribution puts N objects into K bins according to the bins'
probabilities.
- MultinomialRVG(int, double[], RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.MultinomialRVG
-
Constructs a multinomial random vector generator.
- MultinomialRVG(int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.MultinomialRVG
-
Constructs a multinomial random vector generator.
- MultipleExecutionException - Exception in com.numericalmethod.suanshu.misc.parallel
-
This exception is thrown when any of the parallel tasks throws an exception during execution.
- MultipleExecutionException(List<?>, List<ExecutionException>) - Constructor for exception com.numericalmethod.suanshu.misc.parallel.MultipleExecutionException
-
Construct an exception with the (partial) results and all exceptions encountered during
execution.
- MultiplicativeModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
-
The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components.
- MultiplicativeModel(double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel
-
Construct a univariate time series by multiplying the components.
- MultiplicativeModel(double[], double[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel
-
Construct a univariate time series by multiplying the components.
- MultiplierPenalty - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
-
A multiplier penalty function allows different weights to be assigned to the constraints.
- MultiplierPenalty(Constraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- MultiplierPenalty(Constraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- MultiplierPenalty(Constraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- multiply(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.Matrix
-
Right multiply this matrix, A, by a vector.
- multiply(Matrix) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixRing
-
this * that
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
this * that
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Computes the product of two diagonal matrices.
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Left multiplication by G, namely, G * A.
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- multiply(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- multiply(MatrixAccess, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- multiply(MatrixAccess, MatrixAccess) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A1 * A2
- multiply(MatrixAccess, Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A * v
- multiply(double[], double[], double[], int, int, int) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplication
-
Multiplies two matrices, C = A %*% B.
- multiply(double[], double[], double[], int, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock
-
- multiply(double[], double[], double[], int, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByIjk
-
- multiply(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- multiply(MatrixAccess, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Left multiplication by P.
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Left multiplication by P.
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- multiply(SparseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- multiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- multiply(ComplexMatrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- multiply(GenericFieldMatrix<F>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- multiply(RealMatrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- multiply(DenseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- multiply(Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Multiplies two vectors, element-by-element.
- multiply(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- multiply(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Multiply this by that, entry-by-entry.
- multiply(G) - Method in interface com.numericalmethod.suanshu.algebra.structure.Monoid
-
× : G × G → G
- multiply(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- multiply(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Compute the product of this complex number and that complex number.
- multiply(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
- multiply(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
-
Multiply one double array to another, entry-by-entry.
- multiply(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- multiply(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- multiply(Real) - Method in class com.numericalmethod.suanshu.number.Real
-
- multiplyInPlace(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Left multiplication by G, namely, G * A.
- multiplyInPlace(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Right multiplies this matrix, A, by a vector.
- MultipointHybridMCMC - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid
-
A multi-point Hybrid Monte Carlo is an extension of
HybridMCMC, where during the
proposal generation instead of considering only the last configuration after the dynamics
simulation, we pick a proposal from a window of the last M configurations.
- MultipointHybridMCMC(RealScalarFunction, RealVectorFunction, Vector, double, int, int, Vector, Vector, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
-
Constructs a new instance with the given parameters.
- MultipointHybridMCMC(RealScalarFunction, RealVectorFunction, Vector, double, int, int, Vector, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
-
Constructs a new instance with equal weights to the M configurations.
- MultivariateArrayGrid - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
-
- MultivariateArrayGrid(MultiDimensionalCollection<Double>, double[]...) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
-
Create a new instance where the dependent variable is specified by a
MultiDimensionalCollection and the independent variables form the specified grid.
- MultivariateAutoCorrelationFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate
-
This is the auto-correlation function of a multi-dimensional time series {Xt}.
- MultivariateAutoCorrelationFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction
-
- MultivariateAutoCovarianceFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate
-
This is the auto-covariance function of a multi-dimensional time series {Xt},
\[
K(i, j) = E((X_i - \mu_i) \times (X_j - \mu_j)')
\]
For a stationary process, the auto-covariance depends only on the lag, |i - j|.
- MultivariateAutoCovarianceFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.MultivariateAutoCovarianceFunction
-
- MultivariateBrownianRRG - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
-
This is the Random Walk construction of a multivariate Brownian motion.
- MultivariateBrownianRRG(int, TimeGrid, Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateBrownianRRG
-
Construct a random realization generator to produce multi-dimensional Brownian paths at time points specified.
- MultivariateBrownianRRG(int, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateBrownianRRG
-
Construct a random realization generator to produce multi-dimensional Brownian paths at time points specified.
- MultivariateBrownianRRG(int, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateBrownianRRG
-
Construct a random realization generator to produce multi-dimensional Brownian paths at evenly spaced time points [0, 1, ...].
- MultivariateBrownianSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete
-
A multivariate Brownian motion is a stochastic process with the following properties.
- MultivariateBrownianSDE(int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
Construct a standard multi-dimensional Brownian motion.
- MultivariateBrownianSDE(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
Construct a multi-dimensional Brownian motion.
- MultivariateDiscreteSDE - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete
-
This interface represents the discrete approximation of a multivariate SDE.
- MultivariateDLM - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.
- MultivariateDLM(Vector, Matrix, MultivariateObservationEquation, MultivariateStateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
-
Construct a (multivariate) controlled dynamic linear model.
- MultivariateDLM(MultivariateDLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
-
Copy constructor.
- MultivariateDLMSeries - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is a simulator for a multivariate controlled dynamic linear model process.
- MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
-
Simulate a multivariate controlled dynamic linear model process.
- MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
-
Simulate a multivariate controlled dynamic linear model process.
- MultivariateDLMSeries(int, MultivariateDLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
-
Simulate a multivariate controlled dynamic linear model process.
- MultivariateDLMSeries.Entry - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is the TimeSeries.Entry for a multivariate DLM time series.
- MultivariateDLMSim - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is a simulator for a multivariate controlled dynamic linear model process.
- MultivariateDLMSim(MultivariateDLM, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSim
-
Simulates a multivariate controlled dynamic linear model process.
- MultivariateDLMSim.Innovation - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
a simulated innovation
- MultivariateEulerSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete
-
The Euler scheme is the first order approximation of an SDE.
- MultivariateEulerSDE(MultivariateSDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
-
Discretize a multivariate, continuous-time SDE using the Euler scheme.
- MultivariateExponentialFamily - Class in com.numericalmethod.suanshu.stats.distribution.multivariate.exponentialfamily
-
The exponential family is an important class of probability distributions sharing this particular
form.
- MultivariateExponentialFamily(RealScalarFunction, RealVectorFunction, RealVectorFunction, RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.exponentialfamily.MultivariateExponentialFamily
-
Construct a factory to construct probability distribution in the exponential family of this
form.
- MultivariateFiniteDifference - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
-
A partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant.
- MultivariateFiniteDifference(RealScalarFunction, int[]) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.MultivariateFiniteDifference
-
Construct the partial derivative of a multi-variable function.
- MultivariateForecastOneStep - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
-
The innovation algorithm is an efficient way to obtain
a one step least square linear predictor for a multivariate linear time series
with known auto-covariance and these properties (not limited to ARMA processes):
{xt} can be non-stationary.
E(xt) = 0 for all t.
- MultivariateForecastOneStep(MultivariateIntTimeTimeSeries, MultivariateAutoCovarianceFunction) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
-
Construct an instance of InnovationAlgorithm for a multivariate time series with known auto-covariance structure.
- MultivariateFt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This represents the concept 'Filtration', the information available at time t.
- MultivariateFt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
Construct an empty filtration (no information).
- MultivariateFt(MultivariateFt) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
Copy constructor.
- MultivariateFtWt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This is a filtration implementation that includes the path-dependent information,
Wt.
- MultivariateFtWt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFtWt
-
Construct an empty filtration (no information).
- MultivariateFtWt(MultivariateFtWt) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFtWt
-
Copy constructor.
- MultivariateGenericTimeTimeSeries<T extends Comparable<? super T>> - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate
-
This is a multivariate time series indexed by some notion of time.
- MultivariateGenericTimeTimeSeries(T[], Vector[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct a multivariate time series from timestamps and vectors.
- MultivariateGenericTimeTimeSeries(T[], double[][]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct a multivariate time series from timestamps and vectors.
- MultivariateGenericTimeTimeSeries(T[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct a multivariate time series from timestamps and vectors.
- MultivariateGrid - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
-
A multivariate rectilinear (not necessarily uniform) grid of double values.
- MultivariateGridInterpolation - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
-
Interpolation on a rectilinear multi-dimensional grid.
- MultivariateInnovationAlgorithm - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
-
This class implements the part of the innovation algorithm that computes the prediction error
covariances, V and prediction coefficients Θ.
- MultivariateInnovationAlgorithm(int, MultivariateAutoCovarianceFunction) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
-
Run the Innovation Algorithm to compute the prediction parameters,
V and Θ.
- MultivariateIntTimeTimeSeries - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime
-
This is a multivariate time series indexed by integers.
- MultivariateIntTimeTimeSeries.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime
-
This is the TimeSeries.Entry for an integer -indexed multivariate time series.
- MultivariateLinearKalmanFilter - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
The Kalman filter, also known as linear quadratic estimation (LQE),
is an algorithm which uses a series of measurements observed over time,
containing noise (random variations) and other inaccuracies,
and produces estimates of unknown variables that tend to be more precise than those that would be based on a single measurement alone.
- MultivariateLinearKalmanFilter(MultivariateDLM) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Construct a Kalman filter from a multivariate controlled dynamic linear model.
- MultivariateMinimizer<P extends OptimProblem,S extends MinimizationSolution<Vector>> - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained
-
This is a minimizer that minimizes a multivariate function or a
Vector function.
- MultivariateNormalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.multivariate
-
The multivariate Normal distribution or multivariate Gaussian distribution, is a generalization
of the one-dimensional (univariate) Normal distribution to higher dimensions.
- MultivariateNormalDistribution(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution
-
Constructs an instance with the given mean and covariance matrix.
- MultivariateNormalDistribution(int) - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution
-
Constructs an instance of the standard Normal distribution.
- MultivariateObservationEquation - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is the observation equation in a controlled dynamic linear model.
- MultivariateObservationEquation(R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
-
Constructs an observation equation.
- MultivariateObservationEquation(R1toMatrix, R1toMatrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
-
Constructs an observation equation.
- MultivariateObservationEquation(Matrix, Matrix, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
-
Constructs a time-invariant an observation equation.
- MultivariateObservationEquation(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
-
Constructs a time-invariant an observation equation.
- MultivariateObservationEquation(ObservationEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
-
Constructs a multivariate observation equation from a univariate observation equation.
- MultivariateObservationEquation(MultivariateObservationEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
-
Copy constructor.
- MultivariateProbabilityDistribution - Interface in com.numericalmethod.suanshu.stats.distribution.multivariate
-
A multivariate or joint probability distribution for X, Y, ... is a probability
distribution that gives the probability that each of X, Y, ... falls in any particular
range or discrete set of values specified for that variable.
- MultivariateRandomProcess - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
-
This interface represents a multivariate random process a.k.a.
- MultivariateRandomProcess(int, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Construct a multivariate random process.
- MultivariateRandomRealizationGenerator - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
-
This interface defines a generator to construct random realizations from a multivariate stochastic process.
- MultivariateRandomRealizationOfRandomProcess - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
-
This class generates random realizations from a multivariate random/stochastic process.
- MultivariateRandomRealizationOfRandomProcess(MultivariateRandomProcess, int, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
Construct a random realization generator from a multivariate random/stochastic process.
- MultivariateRandomRealizationOfRandomProcess(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
Construct a random realization generator from a multivariate discrete SDE.
- MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, TimeGrid, Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
Construct a random realization generator from a multivariate SDE.
- MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
Construct a random realization generator from a multivariate SDE.
- MultivariateRandomWalk - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random
-
This is the Random Walk construction of a multivariate stochastic process per SDE specification.
- MultivariateRandomWalk(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomWalk
-
Construct a multivariate stochastic process from an SDE.
- MultivariateRealization - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime
-
A multivariate realization is a multivariate time series indexed by real numbers, e.g., real time.
- MultivariateRealization.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime
-
This is the TimeSeries.Entry for a real number -indexed multivariate time series.
- MultivariateRegularGrid - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
-
A regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes
(e.g.
- MultivariateRegularGrid(MultiDimensionalCollection<Double>, MultivariateRegularGrid.EquallySpacedVariable...) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
-
Create a new instance where the dependent variable is specified by a
MultiDimensionalCollection and the independent variables form the specified grid.
- MultivariateRegularGrid.EquallySpacedVariable - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
-
Specify the positioning and spacing along one dimension.
- MultivariateResampler - Interface in com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate
-
This is the interface of a multivariate re-sampler method.
- MultivariateSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
-
This class represents a multi-dimensional, continuous-time Stochastic Differential Equation (SDE) of this form:
\[
dX_t = \mu(t,X_t,Z_t,...)*dt + \sigma(t, X_t, Z_t, ...)*dB_t
\]
- MultivariateSDE(DriftVector, DiffusionMatrix, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateSDE
-
Construct a multi-dimensional diffusion type stochastic differential equation.
- MultivariateSimpleTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime
-
This simple multivariate time series has its vectored values indexed by integers.
- MultivariateSimpleTimeSeries(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries.
- MultivariateSimpleTimeSeries(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries.
- MultivariateSimpleTimeSeries(Vector...) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries.
- MultivariateSimpleTimeSeries(IntTimeTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries from a univariate time series.
- MultivariateStateEquation - Class in com.numericalmethod.suanshu.stats.dlm.multivariate
-
This is the state equation in a controlled dynamic linear model.
- MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
-
Constructs a state equation.
- MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
-
Constructs a state equation.
- MultivariateStateEquation(R1toMatrix, R1toMatrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
-
Constructs a state equation without control variables.
- MultivariateStateEquation(Matrix, Matrix, Matrix, NormalRVG) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
-
Constructs a time-invariant state equation.
- MultivariateStateEquation(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
-
Constructs a time-invariant state equation without control variables.
- MultivariateStateEquation(StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
-
Constructs a multivariate state equation from a univariate state equation.
- MultivariateStateEquation(MultivariateStateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateStateEquation
-
Copy constructor.
- MultivariateTDistribution - Class in com.numericalmethod.suanshu.stats.distribution.multivariate
-
The multivariate T distribution or multivariate Student distribution, is a generalization
of the one-dimensional (univariate) Student's t-distribution to higher dimensions.
- MultivariateTDistribution(int, Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution
-
Constructs an instance with the given mean and scale matrix.
- MultivariateTDistribution(int, int) - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution
-
Constructs an instance of the standard t distribution, mean 0, variance 1.
- MultivariateTimeSeries<T extends Comparable<? super T>,E extends MultivariateTimeSeries.Entry<T>> - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate
-
A multivariate time series is a sequence of vectors indexed by some notion of time.
- MultivariateTimeSeries.Entry<T> - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate
-
This is the TimeSeries.Entry for a multivariate time series.
- mutate() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.Chromosome
-
Construct a Chromosome by mutation.
- mutate() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell
-
- mutate() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
-
- mutate() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
-
- mutate() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
-
- mutate() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell
-
Mutate by a local search in the neighborhood.
- mutate() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
-
Mutate by random disturbs in a neighborhood.
- Mutex - Class in com.numericalmethod.suanshu.misc.parallel
-
Provides mutual exclusive execution of a Runnable.
- Mutex() - Constructor for class com.numericalmethod.suanshu.misc.parallel.Mutex
-
- MVOptimizer - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer
-
Solves for the optimal weight using Mean-Variance optimization.
- MVOptimizer.Result - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer
-
- MVOptimizerLongOnly - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer
-
A long-only MV optimizer.
- MVOptimizerLongOnly() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer.MVOptimizerLongOnly
-
- MVOptimizerMinWeights - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer
-
Solves for weights by (active set) quadratic programming.
- MVOptimizerMinWeights(Vector) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights
-
Constructs the solver with a constraint on the minimum weights (w ≥
w0).
- MVOptimizerNoConstraint - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer
-
Solves for optimal weights by closed-form expressions of w(η) when
there is no limit on short selling.
- MVOptimizerNoConstraint() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer.MVOptimizerNoConstraint
-
- MVOptimizerShrankMean - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer
-
Shrinks the mean towards average before passing the inputs to another
MVOptimizer.
- MVOptimizerShrankMean(MVOptimizer, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer.MVOptimizerShrankMean
-
- MWC8222 - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform
-
Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.
- MWC8222() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.MWC8222
-
Construct a random number generator to sample uniformly from [0, 1].
- MyCutter(ILPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer.MyCutter
-
Construct a Gomory mixed cutter.
- MyCutter(PureILPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer.MyCutter
-
Construct a Gomory pure cutter.
- MySteepestDescent(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent
-
- N() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.NevilleTable
-
Get the number of data points.
- n() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
-
Gets the number of interior y-axis grid points in the solution.
- N() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
-
Gets the number of interior space-axis grid points in the solution.
- n() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the number of interior x-axis grid points in the solution.
- N - Variable in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA
-
- N() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.HConstruction
-
Gets the number of H inversions.
- n - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- n() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Gets the dimension of the square matrices C and As.
- n() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
-
Gets the dimension of the system, i.e., the dimension of x, the
number of variables.
- n(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets the number of columns of Ai.
- n() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the number of rows of the multivariate time series used in
regression.
- n - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
sample size
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.KendallRankCorrelation
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.SpearmanRankCorrelation
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedMean
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedVariance
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile
-
- N() - Method in interface com.numericalmethod.suanshu.stats.descriptive.Statistic
-
Get the size of the sample.
- N() - Method in class com.numericalmethod.suanshu.stats.descriptive.SynchronizedStatistic
-
- N - Variable in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
the number of observations
- NaiveRule - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
This pivoting rule chooses the column with the most negative reduced cost.
- NaiveRule() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
- name - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
the variable name
- NaN - Static variable in class com.numericalmethod.suanshu.number.complex.Complex
-
a number representing the complex Not-a-Number (NaN)
- natural() - Static method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicSpline
-
Creates an instance with end conditions which fits natural
splines, meaning that the second derivative at both ends are zero.
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get the dimension of the Brownian motion (or the number of driving 1D Brownian motions).
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
- nB() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix
-
Get the number of independent Brownian motions.
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
- nB() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE
-
Get the number of independent driving Brownian motions.
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
-
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the number of independent driving Brownian motions.
- nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get the number of driving Brownian motions.
- nChildren() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Get the number of children before populating the next generation.
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.SubMatrixBlock
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- nCols() - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
- nCols() - Method in interface com.numericalmethod.suanshu.misc.datastructure.Table
-
Gets the number of columns.
- nColumns() - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
-
Gets the number of columns in the table.
- NEGATIVE_INFINITY - Static variable in class com.numericalmethod.suanshu.number.complex.Complex
-
a number representing -∞ + -∞i
- NegCetaFunction(Ceta) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.NegCetaFunction
-
- neighbor(V) - Method in interface com.numericalmethod.suanshu.graph.Edge
-
Gets the neighboring vertex connected to vertex.
- neighbor(V) - Method in class com.numericalmethod.suanshu.graph.type.SimpleArc
-
- neighbor(V) - Method in class com.numericalmethod.suanshu.graph.type.SimpleEdge
-
Get the unique neighboring vertex connected to vertex.
- neighbors(V) - Method in interface com.numericalmethod.suanshu.graph.HyperEdge
-
Gets the set of neighboring vertices connected to vertex.
- neighbors(V) - Method in class com.numericalmethod.suanshu.graph.type.SimpleArc
-
- neighbors(V) - Method in class com.numericalmethod.suanshu.graph.type.SimpleEdge
-
- NelderMeadMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2
-
The Nelder-Mead method is a nonlinear optimization technique, which is well-defined for twice
differentiable and unimodal problems.
- NelderMeadMinimizer(double, double, double, double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.NelderMeadMinimizer
-
Construct a Nelder-Mead multivariate minimizer.
- NelderMeadMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.NelderMeadMinimizer
-
Construct a Nelder-Mead multivariate minimizer.
- NelderMeadMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2
-
This is the solution to an optimization problem by the Nelder-Mead method.
- nEqualities() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Get the number of equality constraints.
- NEUTRON_MASS_M - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The neutron mass in kilograms (kg).
- NevilleTable - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation
-
Neville's algorithm is a polynomial interpolation algorithm.
- NevilleTable(int, OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.NevilleTable
-
Construct a Neville table of size n, initialized with data {(x, y)}.
- NevilleTable(OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.NevilleTable
-
Construct a Neville table of size n, initialized with data {(x, y)}.
- NevilleTable() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.NevilleTable
-
Construct an empty Neville table.
- newActiveList() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer.ActiveListFactory
-
Construct a new instance of ActiveList for an Integer Linear Programming problem.
- newCellFactory() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.NewCellFactory
-
Construct a new instance of DEOptimCellFactory for a minimization problem.
- newCellFactory() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.NewCellFactoryCtor
-
Construct a new instance of SimpleCellFactory for a minimization problem.
- newDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution
-
- newDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution
-
- newDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- newDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaMixtureDistribution
-
- newDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- newDistributions() - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureDistribution
-
Get the distributions (possibly differently parameterized) for all
states.
- newDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution
-
- newDistributions() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonMixtureDistribution
-
- newInstance(double[], int, int, DoubleArrayOperation) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- newInstance(Matrix) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.PreconditionerFactory
-
- newInstance() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer.ConstrainedMinimizerFactory
-
- newInstance() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.MinimizerFactory
-
Construct a new instance of Minimizer for a mutation operation.
- newMixtureDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureDistribution
-
Construct a new distribution from a set of parameters, one set per state.
- newMixtureDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonMixtureDistribution
-
- newRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution
-
- newRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution
-
- newRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- newRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaMixtureDistribution
-
- newRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- newRandomNumberGenerators() - Method in interface com.numericalmethod.suanshu.stats.hmm.mixture.distribution.MixtureDistribution
-
Get the random number generators corresponding to the distributions
(possibly differently parameterized) for all states.
- newRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution
-
- newRandomNumberGenerators() - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonMixtureDistribution
-
- newResample() - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
- newResample() - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
- newResample() - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
- newResample() - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
- newResample() - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate.GroupResampler
-
- newResample() - Method in interface com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate.MultivariateResampler
-
Gets a resample from the original sample.
- newResample() - Method in interface com.numericalmethod.suanshu.stats.random.sampler.resampler.ObjectResampler
-
Gets a resample from the original sample.
- newResample() - Method in interface com.numericalmethod.suanshu.stats.random.sampler.resampler.Resampler
-
Gets a resample from the original sample.
- newResampler(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- newResampler(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
-
- newResampler(Matrix) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
-
- newResampler(Matrix) - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.ReturnsResamplerFactory
-
Constructs a new instance of a re-sampling mechanism.
- newSOCPGeneralConstraints(SOCPPortfolioConstraint.Variable...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
- NewtonCotes - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes
-
The Newton-Cotes formulae, also called the Newton-Cotes quadrature rules or simply Newton-Cotes rules,
are a group of formulae for numerical integration (also called quadrature) based on evaluating the integrand at equally-spaced points.
- NewtonCotes(int, NewtonCotes.Type, double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
Construct an instance of the Newton-Cotes quadrature.
- NewtonCotes.Type - Enum in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes
-
There are two types of the Newton-Cotes method: OPEN and CLOSED.
- NewtonPolynomial - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
-
Newton polynomial is the interpolation polynomial for a given set of data points in the Newton
form.
- NewtonPolynomial() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.NewtonPolynomial
-
- NewtonRaphsonImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer.NewtonRaphsonImpl
-
- NewtonRaphsonMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
-
The Newton-Raphson method is a second order steepest descent method that is based on
the quadratic approximation of the Taylor series.
- NewtonRaphsonMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer
-
Construct a multivariate minimizer using the Newton-Raphson method.
- NewtonRaphsonMinimizer.NewtonRaphsonImpl - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
-
- NewtonRoot - Class in com.numericalmethod.suanshu.analysis.root.univariate
-
The Newton-Raphson method is as follows: one starts with an initial guess which is reasonably
close to the true root, then the function is approximated by its tangent line (which can be
computed using the tools of calculus), and one computes the x-intercept of this tangent line
(which is easily done with elementary algebra).
- NewtonRoot(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.root.univariate.NewtonRoot
-
Constructs an instance of Newton's root finding algorithm.
- NewtonSystemRoot - Class in com.numericalmethod.suanshu.analysis.root.multivariate
-
This class solves the root for a non-linear system of equations.
- NewtonSystemRoot(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.root.multivariate.NewtonSystemRoot
-
Constructs an instance of Newton's root finding algorithm for a system of non-linear
equations.
- newVariation(RealScalarFunction, EqualityConstraints) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory
-
Construct a new instance of SQPASEVariation for an SQP problem.
- newVariation(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.VariationFactory
-
Construct a new instance of SQPASVariation for an SQP
problem.
- nExogenousFactors() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Gets the number of factors, excluding the intercept.
- next() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
-
- next(int, UnivariateRealFunction, double, double, double) - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.IterativeIntegrator
-
Compute a refined sum for the integral.
- next(int, UnivariateRealFunction, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
- next(int, UnivariateRealFunction, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
- next() - Method in class com.numericalmethod.suanshu.stats.distribution.discrete.ProbabilityMassSampler
-
Gets the next random element from the range of the probability distribution.
- next(Vector) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSim
-
Gets the next innovation.
- next(double) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSim
-
Get the next innovation.
- next() - Method in class com.numericalmethod.suanshu.stats.hmm.HMMRNG
-
Gets the next simulated innovation: state and observation.
- next() - Method in interface com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedGenerator.Generator
-
Returns the next value in the underlying generated sequence.
- next() - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedGenerator
-
Returns the next value in the generated sequence.
- next() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast
-
Gets the next forecast.
- next(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast
-
Gets the next n-step forecasts.
- next() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Gets the next forecast.
- next(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Gets the next n-step forecasts.
- nextDouble() - Method in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
Gets the next simulated observation.
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.evt.markovchain.ExtremeValueMC
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.evt.timeseries.MARMASim
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.hmm.HMMRNG
-
Gets the next simulated observation.
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Gets the next simulated state.
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRLG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ContextRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BernoulliTrial
-
Get the next random double, which is either 1 (success) or 0 (failure).
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.Cheng1978
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.VanDerWaerden1969
-
Deprecated.
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BinomialRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BurnInRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.exp.Ziggurat2000Exp
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.KunduGupta2007
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.MarsagliaTsang2000
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.XiTanLiu2010a
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.XiTanLiu2010b
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.InverseTransformSampling
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.LogNormalRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.BoxMuller
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.ConcurrentStandardNormalRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.MarsagliaBray1964
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.NormalRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.Ziggurat2000
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.Zignor2005
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.poisson.Knuth1969
-
- nextDouble() - Method in interface com.numericalmethod.suanshu.stats.random.rng.univariate.RandomNumberGenerator
-
Get the next random double.
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.ThinRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.LEcuyer
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.Lehmer
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.MRG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.MWC8222
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR0
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR3
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.UniformRNG
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomWalk
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUSim
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
-
- nextDouble() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
- nextInt() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR0
-
- nextInt() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR3
-
- nextLogTrial(RandomNumberGenerator, double) - Static method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability ep.
- nextLogTrial() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability ep.
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRLG
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ThreadIDRLG
-
- nextLong() - Method in interface com.numericalmethod.suanshu.stats.random.rng.univariate.RandomLongGenerator
-
Get the next random long.
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.LEcuyer
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.Lehmer
-
All built-in linear random number generators in this library
ultimately call this function to generate random numbers.
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.MRG
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.MWC8222
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR0
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR3
-
- nextLong() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.UniformRNG
-
- nextMatrix() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
-
Constructs a random matrix based on the row and column sums.
- nextN(RandomNumberGenerator, int) - Static method in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
Generates n random numbers from a given random number generator.
- nextN(RandomVectorGenerator, int) - Static method in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
Generates n random vectors from a given random vector generator.
- nextP(double, double, double) - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Gets the evolution of pt, the conditional
probability
of being in an uptrend given all information, i.e.,
\(p_t = P(\alpha_t = 1 | \mathcal{F}_t)\).
- nextPair() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Get the next random (e2_t, h_t).
- nextProposal(Vector, double) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.AnnealingFunction
-
Gets the next proposal, given the current state and the temperature.
- nextProposal(Vector, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.BoxGSAAnnealingFunction
-
- nextProposal(Vector, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction
-
- nextProposal(Vector, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction
-
- nextProposedState(Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.HybridMCMC
-
- nextProposedState(Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
-
- nextProposedState(Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
Proposes a next state for the system.
- nextProposedState(Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.Metropolis
-
- nextProposedState(Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.MetropolisHastings
-
- nextProposedState(Vector) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
-
- nextRealization() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateBrownianRRG
-
- nextRealization() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationGenerator
-
Construct a realization of a multivariate stochastic process.
- nextRealization() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
- nextRealization() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomRealizationGenerator
-
Construct a realization of a univariate stochastic process.
- nextRealization() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
- nextState() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Gets the next simulated state.
- nextTime() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get the next time point in the time grid.
- nextTime() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomProcess
-
Get the next time point in the time grid.
- nextTrial(RandomNumberGenerator, double) - Static method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability p.
- nextTrial() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability p.
- nextVector() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRVG
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.BurnInRVG
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.HypersphereRVG
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.IID
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.MultinomialRVG
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.NormalRVG
-
- nextVector() - Method in interface com.numericalmethod.suanshu.stats.random.rng.multivariate.RandomVectorGenerator
-
Get the next random vector.
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.ThinRVG
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.UniformDistributionOverBox
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomWalk
-
- nextVector() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMASim
-
- nextWeekDay(DateTime) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Get the next weekday, i.e., skipping Saturdays and Sundays.
- nextX(double, double, double) - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Gets the evolution of xt, logit of the
conditional probability from (0, 1) onto \((-\infty, +\infty)\),
i.e.,
\(x_t = \log{\frac{p_t}{1-p_t}}\).
- nFactors() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Gets the number of factors.
- nFactors() - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the number of variables in the original data.
- nFactors() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Gets the number of factors, including the intercept if any.
- nGreaterThanInequalities() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Get the number of greater-than-or-equal-to constraints.
- nGroups() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get the number of groups of observations.
- nidx - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
number of unique values in dis_G_list
- nlshrink_tau() - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.LedoitWolf2016.Result
-
Gets nonlinear shrinkage tau in ascending order.
- nNoChanges - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- nNonZeros() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- nNonZeros() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- nNonZeros() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- nNonZeros() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseStructure
-
Get the number of non-zero entries in the structure.
- nNonZeros() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- no(double) - Method in interface com.numericalmethod.suanshu.number.DoubleUtils.ifelse
-
Return value for a false element of test.
- nObs() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Gets the number of observations.
- nObs() - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the number of observations in the original data; sample size.
- nObs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
-
Gets the number of observations.
- nObs() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get the total number of observations.
- NoChangeOfVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This is a dummy substitution rule that does not change any variable.
- NoChangeOfVariable(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
Construct an NoChangeOfVariable substitution rule.
- NoConstraints - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint
-
- NoConstraints() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint.NoConstraints
-
- Node(V) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS.Node
-
Constructs a node for a spanning tree.
- Node(V) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node
-
Constructs a node for a spanning tree.
- Node(V) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal.Node
-
Constructs a node for a spanning tree.
- NoModelFitted() - Constructor for exception com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.ElliottOnlineFilter.NoModelFitted
-
- noNaN(double[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Remove the NaN from an array.
- NonlinearFit - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
Fit log-ACER function by sequential quadratic programming (SQP) minimization (of weighted RSS),
using
LinearFit's solution as the initial guess.
- NonlinearFit() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit
-
- NonlinearFit.Result - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
-
- NonlinearShrinkageEstimator - Class in com.numericalmethod.suanshu.stats.covariance.nlshrink
-
The nonlinear shrinkage method for given population eigenvalues.
- NonlinearShrinkageEstimator(Vector, int) - Constructor for class com.numericalmethod.suanshu.stats.covariance.nlshrink.NonlinearShrinkageEstimator
-
- NonlinearShrinkageEstimator(QuEST.Result) - Constructor for class com.numericalmethod.suanshu.stats.covariance.nlshrink.NonlinearShrinkageEstimator
-
- nonlinearShrunkEigenvalues() - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.NonlinearShrinkageEstimator
-
Gets the nonlinear shrinkage eigenvalues in ascending order.
- NonNegativityConstraintOptimProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.problem
-
This is a constrained optimization problem for a function which has all non-negative variables.
- NonNegativityConstraintOptimProblem(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
Construct a constrained optimization problem with only non-negative variables.
- NonNegativityConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
-
These constraints ensures that for all variables are non-negative.
- NonNegativityConstraints(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.NonNegativityConstraints
-
Construct a lower bound constraints for all variables in a function.
- NoPairFoundException - Exception in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
- NoPairFoundException(String) - Constructor for exception com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.NoPairFoundException
-
- norm() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- norm(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- norm(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- norm() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- norm(Vector, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the norm of a vector.
- norm(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the norm of a vector.
- norm() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- norm(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- norm() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- norm() - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Compute the length or magnitude or Euclidean norm of a vector, namely, \(\|v\|\).
- norm(double) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Gets the \(L^p\)-norm \(\|v\|_p\) of this vector.
- norm() - Method in interface com.numericalmethod.suanshu.algebra.structure.BanachSpace
-
|⋅| : B → F
norm assigns a strictly positive length or size to all vectors in the vector space,
other than the zero vector.
- NormalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The Normal distribution has its density a Gaussian function.
- NormalDistribution() - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
Construct an instance of the standard Normal distribution with mean 0 and standard deviation 1.
- NormalDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
Construct a Normal distribution with mean mu and standard deviation sigma.
- NormalMixtureDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Normal distribution to model the observations.
- NormalMixtureDistribution(NormalMixtureDistribution.Lambda[], boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution
-
Constructs a Normal distribution for each state in the HMM model.
- NormalMixtureDistribution(NormalMixtureDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution
-
Constructs a Normal distribution for each state in the HMM model.
- NormalMixtureDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
the Normal distribution parameters
- NormalOfExpFamily1 - Class in com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily
-
Normal distribution, univariate, unknown mean, known variance.
- NormalOfExpFamily1(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily.NormalOfExpFamily1
-
- NormalOfExpFamily2 - Class in com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily
-
Normal distribution, univariate, unknown mean, unknown variance.
- NormalOfExpFamily2() - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily.NormalOfExpFamily2
-
- NormalRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.normal
-
This is a random number generator that generates random deviates according to the Normal
distribution.
- NormalRNG(double, double, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.NormalRNG
-
Construct a random number generator to sample from the Normal distribution.
- NormalRNG(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.NormalRNG
-
Construct a random number generator to sample from the Normal distribution.
- NormalRVG - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate
-
A multivariate Normal random vector is said to be p-variate normally distributed if every linear
combination of its p components has a univariate normal distribution.
- NormalRVG(Vector, Matrix, double, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.NormalRVG
-
Constructs a multivariate Normal random vector generator.
- NormalRVG(Vector, Matrix, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.NormalRVG
-
Constructs a multivariate Normal random vector generator.
- NormalRVG(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.NormalRVG
-
Constructs a multivariate Normal random vector generator.
- NormalRVG(int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.NormalRVG
-
Constructs a standard multivariate Normal random vector generator.
- NoRootFoundException - Exception in com.numericalmethod.suanshu.analysis.root.univariate
-
This is the
Exception thrown when it fails to find a root.
- NoRootFoundException(double, double) - Constructor for exception com.numericalmethod.suanshu.analysis.root.univariate.NoRootFoundException
-
Construct a NoRootFoundException.
- NoShortSelling - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint
-
Weights cannot be negative.
- NoShortSelling(int) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.constraint.NoShortSelling
-
Constructs the constraint with the number of assets in the portfolio.
- NoSolution(String) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.NoSolution
-
Construct an LinearSystemSolver.NoSolution exception.
- notAKnot() - Static method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicSpline
-
Creates an instance with end conditions which fits not-a-knot
splines, meaning that continuity of the third derivative at the second
and the next-to-last knots are forced.
- nParams() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the number of parameters for the estimation/fitting.
- NPEBPortfolioMomentsEstimator - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb
-
Uses Non-Parametric Empirical Bayes (NPEB) approach to estimate the first and
the second moments of the weighted portfolios.
- NPEBPortfolioMomentsEstimator(ReturnsMoments.Estimator, MVOptimizer, ReturnsResamplerFactory, int) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.NPEBPortfolioMomentsEstimator
-
- nPopulation() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Get the size of the population pool, that is the number of chromosomes.
- nquant - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
number of points we select between in F[i]
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.SubMatrixBlock
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- nRows() - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
- nRows() - Method in interface com.numericalmethod.suanshu.misc.datastructure.Table
-
Gets the number of rows.
- nSamples() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
Get the number of samples in the empirical distribution.
- nSim - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera
-
- nStableIterations - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- nStates() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Gets the number of states.
- nSymbols() - Method in class com.numericalmethod.suanshu.stats.hmm.discrete.DiscreteHMM
-
Gets the number of observation symbols per state.
- nullDeviance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticResiduals
-
Gets the null deviance.
- nullity() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the nullity of A.
- nullity(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
- NullMonitor<S> - Class in com.numericalmethod.suanshu.misc.algorithm.iterative.monitor
-
- NullMonitor() - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.NullMonitor
-
- numberOfChildren(DiGraph<V, ? extends Arc<V>>, V) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Gets the number of children.
- numberOfClusters() - Method in class com.numericalmethod.suanshu.graph.community.GirvanNewman
-
Gets the number of connected clusters.
- numberOfEdges(Graph<V, ?>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Gets the number of edges in this graph.
- numberOfParents(DiGraph<V, ? extends Arc<V>>, V) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Gets the number of parents.
- numberOfVertices(Graph<V, ?>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Gets the number of vertices in this graph.
- NumberUtils - Class in com.numericalmethod.suanshu.number
-
These are the utility functions to manipulate
Numbers.
- NumberUtils.Comparable<T extends Number> - Interface in com.numericalmethod.suanshu.number
-
We need a precision parameter to determine whether two numbers are close enough to be treated
as equal.
- numint - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
number of intervals
- nVariables() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Gets the number of variables in the original data set.
- P() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the permutation matrix, P, such that P * A = L * U.
- P() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
- P() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
- P() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
Gets P, the pivoting matrix in the QR decomposition.
- P() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QR
-
- P() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get P, the pivoting matrix in the QR decomposition.
- P() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
- P() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LU
-
- P() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition
-
Get the permutation matrix P as in P * A = L * U.
- p() - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the number of interior y-axis grid points in the solution.
- p() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.QuadraticFunction
-
- p() - Method in class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularized
-
Get p, the shape parameter.
- p - Variable in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
- p() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Gets the dimension of the system, i.e., p = the dimension of y, the number of
variables.
- p() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
-
Gets the size of b.
- p - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- p - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
number of variables
- p() - Method in class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
-
Get the number of AR terms.
- p - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
-
the success probability in each trial
- p() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState
-
Gets the momentum.
- p() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Gets the current momentum.
- p() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the number of AR terms.
- p() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the order of the VECM model.
- p() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the number of AR terms.
- p() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the number of GARCH terms.
- Package - Class in com.numericalmethod.suanshu.misc.license
-
- Pair - Class in com.numericalmethod.suanshu.analysis.function.tuple
-
An ordered pair (x,y) is a pair of mathematical objects.
- Pair(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.Pair
-
Construct a pair.
- PairComparatorByAbscissaFirst - Class in com.numericalmethod.suanshu.analysis.function.tuple
-
- PairComparatorByAbscissaFirst() - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.PairComparatorByAbscissaFirst
-
- PairComparatorByAbscissaFirst(double) - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.PairComparatorByAbscissaFirst
-
- PairComparatorByAbscissaOnly - Class in com.numericalmethod.suanshu.analysis.function.tuple
-
- PairComparatorByAbscissaOnly() - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.PairComparatorByAbscissaOnly
-
- PairComparatorByAbscissaOnly(double) - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.PairComparatorByAbscissaOnly
-
- PairingCheck - Interface in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.check
-
- PairingModel - Interface in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
Given a set of symbols, their prices and other information, we find mean
reverting pairs for trading.
- PairingModel1 - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
- PairingModel1(double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel1
-
- PairingModel2 - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
- PairingModel2(double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel2
-
- PairingModel3 - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
- PairingModel3(double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel3
-
- PairingModel4 - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
- PairingModel4(double, double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel4
-
- PairingModel5 - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
- PairingModel5(double, double, double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModel5
-
- PairingModelUtils - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
- PairingModelUtils() - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.PairingModelUtils
-
- PanelData<S> - Class in com.numericalmethod.suanshu.stats.regression.linear.panel
-
A panel data refers to multi-dimensional data frequently involving
measurements over time.
- PanelData(String, String, String[]) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Constructs a panel of two-dimensional data.
- PanelData.Row - Class in com.numericalmethod.suanshu.stats.regression.linear.panel
-
This is one row of the data in a panel.
- PanelData.Transformation - Interface in com.numericalmethod.suanshu.stats.regression.linear.panel
-
Transforms the data, e.g., taking log.
- PanelRegression - Interface in com.numericalmethod.suanshu.stats.regression.linear.panel
-
Panel (data) analysis is a statistical method, widely used in social science,
epidemiology, and econometrics, which deals with two-dimensional (cross
sectional/times series) panel data.
- parallel - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
This indicate if the algorithm is to run in parallel (multi-core).
- ParallelDoubleArrayOperation - Class in com.numericalmethod.suanshu.number.doublearray
-
This is a multi-threaded implementation of the array math operations.
- ParallelDoubleArrayOperation() - Constructor for class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- ParallelExecutor - Class in com.numericalmethod.suanshu.misc.parallel
-
This class provides a framework for executing an algorithm in parallel.
- ParallelExecutor() - Constructor for class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Creates an instance using default concurrency number, which is the
number of available processors returned by
- ParallelExecutor(String) - Constructor for class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Creates an instance with a specified executor name.
- ParallelExecutor(int) - Constructor for class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Creates an instance with a specified concurrency number.
- ParallelExecutor(String, int) - Constructor for class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Creates an instance with a specified concurrency number, and a name of
the executor.
- parent() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal.Node
-
Gets the parent of the node.
- parent(V) - Method in interface com.numericalmethod.suanshu.graph.Tree
-
Gets the unique parent of a vertex.
- parent(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- parent(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- parents(V) - Method in interface com.numericalmethod.suanshu.graph.DiGraph
-
Gets the set of all parents of this vertex.
- parents(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseDiGraph
-
- parents(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- parents(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- parse(String) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Construct a number from a String.
- parseArray(String...) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
-
Convert an array of numbers in
String to an array of numbers in
Number.
- PartialDerivativesByCenteredDifferencing - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
-
This implementation computes the partial derivatives by centered differencing.
- PartialDerivativesByCenteredDifferencing() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
-
- PartialFunction - Class in com.numericalmethod.suanshu.analysis.function.tuple
-
A partial function from X to Y is a function f: X' → Y, where X'
is a subset of X.
- PartialFunction(double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.PartialFunction
-
Construct a partial function from {(x,y)}.
- paste(Collection<String>, String) - Static method in class com.numericalmethod.suanshu.misc.StringUtils
-
Concatenates Strings into one String.
- path - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- PattonPolitisWhite2009 - Class in com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block
-
This class implements the stationary and circular block bootstrapping method
with optimized block length.
- PattonPolitisWhite2009(double[], long, PattonPolitisWhite2009ForObject.Type, ConcurrentCachedRLG, ConcurrentCachedRNG) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009(double[], long, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009(double[], PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009(double[], PattonPolitisWhite2009ForObject.Type) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009(double[]) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject<X> - Class in com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block
-
This class implements the stationary and circular block bootstrapping method
with optimized block length.
- PattonPolitisWhite2009ForObject(X[], Class<X>, PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject(X[], Class<X>, PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject, PattonPolitisWhite2009ForObject.Type) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject(X[], Class<X>, PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject(X[], Class<X>, long, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject(X[], Class<X>, long, PattonPolitisWhite2009ForObject.Type, ConcurrentCachedRLG, ConcurrentCachedRNG) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject.AutoCorrelationForObject - Interface in com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block
-
- PattonPolitisWhite2009ForObject.AutoCovarianceForObject - Interface in com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block
-
- PattonPolitisWhite2009ForObject.Type - Enum in com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block
-
- PCA - Interface in com.numericalmethod.suanshu.stats.factor.pca
-
Principal Component Analysis (PCA) is a mathematical procedure that uses an
orthogonal transformation to convert a set of observations of possibly
correlated variables into a set of values of uncorrelated variables called
principal components.
- PCAbyEigen - Class in com.numericalmethod.suanshu.stats.factor.pca
-
This class performs Principal Component Analysis (PCA) on a data matrix,
using eigen decomposition on the correlation or covariance matrix.
- PCAbyEigen(Matrix, boolean, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
Performs Principal Component Analysis, using the eigen method,
on a data matrix with an optional correlation (or covariance) matrix
provided.
- PCAbyEigen(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
Performs Principal Component Analysis, using the eigen method, on a data
matrix.
- PCAbyEigen(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
Performs Principal Component Analysis, using the eigen method and using
correlation matrix, on a data matrix.
- PCAbySVD - Class in com.numericalmethod.suanshu.stats.factor.pca
-
This class performs Principal Component Analysis (PCA) on a data matrix,
using the preferred Singular Value Decomposition (SVD) method.
- PCAbySVD(Matrix, Vector, Vector) - Constructor for class com.numericalmethod.suanshu.stats.factor.pca.PCAbySVD
-
Performs Principal Component Analysis, using the preferred SVD method, on
a data matrix with (optional) mean vector and scaling vector provided.
- PCAbySVD(Matrix, boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.factor.pca.PCAbySVD
-
Performs Principal Component Analysis, using the preferred SVD method, on
a data matrix (possibly centered and/or scaled).
- PCAbySVD(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.factor.pca.PCAbySVD
-
Performs Principal Component Analysis, using the preferred SVD method, on
a centered and scaled data matrix.
- PDE - Interface in com.numericalmethod.suanshu.analysis.differentialequation.pde
-
A partial differential equation (PDE) is a differential equation that contains unknown
multivariable functions and their partial derivatives.
- PDESolutionGrid2D - Interface in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference
-
A solution to a bivariate PDE, which is applicable to methods which produce the solution as a
two-dimensional grid.
- PDESolutionTimeSpaceGrid1D - Interface in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference
-
A solution to an one-dimensional PDE, which is applicable to methods which
produce the solution
as a grid of time and space.
- PDESolutionTimeSpaceGrid2D - Interface in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference
-
A solution to a two-dimensional PDE, which is applicable to methods which produce the solution
as a three-dimensional grid of time and space.
- PDESolver - Interface in com.numericalmethod.suanshu.analysis.differentialequation.pde
-
A PDE solver solves a set of PDEs.
- PDETimeSpaceGrid1D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference
-
This grid numerically solves a 1D PDE, e.g., using the Crank-Nicolson scheme.
- PDETimeSpaceGrid1D(int) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
Constructs a time-space grid.
- PeaksOverThreshold - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot
-
Peaks Over Threshold (POT) method estimates the parameters for generalized Pareto distribution
(GPD) using maximum likelihood on the observations that are over a given threshold.
- PeaksOverThreshold(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot.PeaksOverThreshold
-
Create an instance for POT method with a given threshold.
- PeaksOverThresholdOnClusters - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot
-
Similar to
POT, but only use the peak observations in clusters for the
parametric estimation.
- PeaksOverThresholdOnClusters(ClusterAnalyzer) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot.PeaksOverThresholdOnClusters
-
Create an instance with a
ClusterAnalyzer which is used to find clusters from
observations.
- PearsonMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
-
This is the Pearson method.
- PearsonMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.PearsonMinimizer
-
Construct a multivariate minimizer using the Pearson method.
- pearsonStat(Matrix, Matrix, boolean) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
-
Compute the Pearson's cumulative test statistic, which asymptotically approaches a
χ2 distribution.
- penalizedCardinality(Matrix) - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionProblem
-
Gets the value of a cardinality-penalized function.
- penalizedL1(Matrix) - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionProblem
-
Gets the value of an L1-penalized function.
- PenaltyFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
-
A function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties.
- PenaltyFunction() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.PenaltyFunction
-
- PenaltyMethodMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
-
The penalty method is an algorithm for solving a constrained minimization problem with general
constraints.
- PenaltyMethodMinimizer(PenaltyMethodMinimizer.PenaltyFunctionFactory, double, IterativeC2Minimizer) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
Construct a constrained minimizer using the penalty method.
- PenaltyMethodMinimizer(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
Construct a constrained minimizer using the penalty method.
- PenaltyMethodMinimizer() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
Construct a constrained minimizer using the penalty method.
- PenaltyMethodMinimizer.PenaltyFunctionFactory - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
-
For each constrained optimization problem, the solver creates a new penalty function for it.
- permutation(int, int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
-
Compute the permutation function.
- permutation(int, int) - Static method in class com.numericalmethod.suanshu.number.big.BigIntegerUtils
-
Compute the permutation function.
- PermutationMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype
-
A permutation matrix is a square matrix that has exactly one entry '1' in each row and each
column and 0's elsewhere.
- PermutationMatrix(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Construct an identity permutation matrix.
- PermutationMatrix(int[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Construct a permutation matrix from an 1D double[].
- PermutationMatrix(PermutationMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Copy constructor.
- perpendicularDistance(Point) - Method in class com.numericalmethod.suanshu.geometry.LineSegment
-
- PerturbationAroundPoint - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
-
The initial population is generated by adding a variance around a given initial.
- PerturbationAroundPoint(RealScalarFunction, SimpleCellFactory, int, Vector, Vector, long) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.PerturbationAroundPoint
-
Generate an initial pool of chromosomes by adding a variance around a given initial.
- phi - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- phi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get all the AR coefficients.
- phi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get all the AR coefficients.
- phiPolynomial() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the polynomial (1 - φ).
- PhysicalConstants - Class in com.numericalmethod.suanshu.misc
-
A collection of fundamental physical constants.
- PI - Static variable in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
the value of PI
- PI() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Gets the initial state probabilities.
- pi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the impact matrix.
- PI() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
Get the coefficients of the linear representation of the time series.
- PI_SQ - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
\(\pi^2\)
- Pivot(int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
-
Construct a pivot.
- PLANCK_H - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The Planck constant \(h\) in joule seconds (J s).
- PLANCK_REDUCED_HBAR - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The reduced Planck constant (or Dirac constant) \(\hbar\), defined as the Planck constant
divided by 2π, in joule seconds (J s).
- plusWeekdayPeriod(DateTime, Period) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Add a weekday-period (i.e., skipping weekends) to a DateTime.
- Point - Class in com.numericalmethod.suanshu.geometry
-
Represent a n-dimensional point.
- Point(double...) - Constructor for class com.numericalmethod.suanshu.geometry.Point
-
Create a point with given coordinates.
- Point(Vector) - Constructor for class com.numericalmethod.suanshu.geometry.Point
-
Create a point with given coordinates.
- points(double, double) - Method in interface com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer.GridDefinition
-
- PoissonDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The Poisson distribution (or Poisson law of small numbers) is a discrete probability distribution
that expresses the probability of a given number of events occurring in a fixed interval of time
and/or space if these events occur with a known average rate and independently of the time since
the last event.
- PoissonDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
Construct a Poisson distribution.
- PoissonEquation2D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2
-
Poisson's equation is an elliptic PDE that takes the following general form.
- PoissonEquation2D(double, double, BivariateRealFunction, BivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
Constructs a Poisson's equation problem.
- PoissonMixtureDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
-
The HMM states use the Poisson distribution to model the observations.
- PoissonMixtureDistribution(Double[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.PoissonMixtureDistribution
-
Constructs a Poisson distribution for each state in the HMM model.
- PolygonalChain - Interface in com.numericalmethod.suanshu.geometry.polyline
-
A polygonal chain, polygonal curve, polygonal path, or piecewise linear curve, is a connected
series of line segments.
- PolygonalChainByArray - Class in com.numericalmethod.suanshu.geometry.polyline
-
- PolygonalChainByArray(List<? extends Point>) - Constructor for class com.numericalmethod.suanshu.geometry.polyline.PolygonalChainByArray
-
Create a new instance which uses the given vertices.
- Polynomial - Class in com.numericalmethod.suanshu.analysis.function.polynomial
-
A polynomial is a
UnivariateRealFunction that represents a finite length expression constructed from variables and constants,
using the operations of addition, subtraction, multiplication, and constant non-negative whole number exponents.
- Polynomial(double...) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Construct a polynomial from an array of coefficients.
- Polynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
Copy constructor.
- PolyRoot - Class in com.numericalmethod.suanshu.analysis.function.polynomial.root
-
This is a solver for finding the roots of a polynomial equation.
- PolyRoot() - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRoot
-
- PolyRootSolver - Interface in com.numericalmethod.suanshu.analysis.function.polynomial.root
-
A root (or a zero) of a polynomial p is a member x in the domain of p such that p(x) vanishes.
- pop() - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.ActiveList
-
Get the next node.
- population - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
This is the (current) population pool.
- PortfolioMoments(double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
-
- PortfolioOptimizationAlgorithm - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization
-
Computes the optimal weights based only on returns.
- PortfolioOptimizationAlgorithm.CovarianceEstimator - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization
-
- PortfolioOptimizationAlgorithm.MeanEstimator - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization
-
- PortfolioOptimizationAlgorithm.SampleCovarianceEstimator - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization
-
- PortfolioOptimizationAlgorithm.SampleMeanEstimator - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization
-
- PortfolioOptimizationAlgorithm.SymbolLookup - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization
-
Provides a lookup for product symbols and indices.
- PortfolioRiskExactSigma - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Constructs the constraint coefficient arrays of the portfolio risk term in
the compact form.
- PortfolioRiskExactSigma(Matrix, PortfolioRiskExactSigma.MatrixRoot) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into
the standard SOCP form when the exact covariance matrix is used.
- PortfolioRiskExactSigma(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into
the standard SOCP form when the exact covariance matrix is used.
- PortfolioRiskExactSigma(Matrix) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into
the standard SOCP form when the exact covariance matrix is used.
- PortfolioRiskExactSigma.DefaultRoot - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
- PortfolioRiskExactSigma.Diagonalization - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
- PortfolioRiskExactSigma.MatrixRoot - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Specifies the method to compute the root of a matrix.
- position(int, int, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- position() - Method in interface com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MRModel
-
Gets the current target position computed by this model.
- position() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MRModelRanged
-
- POSITIVE_INFINITY - Static variable in class com.numericalmethod.suanshu.number.complex.Complex
-
a number representing +∞ + ∞i
- PositiveDefiniteMatrixByPositiveDiagonal - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite
-
This class "converts" a matrix into a symmetric, positive definite matrix, if it is not already
so, by forcing the diagonal entries in the eigen decomposition to a small non-negative number,
e.g., 0.
- PositiveDefiniteMatrixByPositiveDiagonal(Matrix, double, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.PositiveDefiniteMatrixByPositiveDiagonal
-
Constructs a positive definite matrix by forcing the diagonal entries in the eigen
decomposition to a small non-negative number, e.g., 0.
- PositiveSemiDefiniteMatrixNonNegativeDiagonal - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite
-
This class "converts" a matrix into a symmetric, positive semi-definite matrix, if it is not
already so, by forcing the negative diagonal entries in the eigen decomposition to 0.
- PositiveSemiDefiniteMatrixNonNegativeDiagonal(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.PositiveSemiDefiniteMatrixNonNegativeDiagonal
-
Constructs a positive semi-definite matrix by forcing the negative diagonal entries in the
eigen decomposition to 0.
- pow(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- Pow - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
This is a square matrix A to the power of an integer n, An.
- Pow(Matrix, int, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.Pow
-
Construct the power matrix An so that
An = basescale * B
- Pow(Matrix, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.Pow
-
Construct the power matrix An so that
An = (1e100)scale * B
- pow(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- pow(Vector, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Takes a power of a vector, element-by-element.
- pow(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- pow(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- pow(double) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Take the exponentiation of all entries in this vector, entry-by-entry.
- pow(int) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- pow(BigDecimal, BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute a to the power of b.
- pow(BigDecimal, BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute a to the power of b.
- pow(BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute a to the power of n, where n is an integer.
- pow(BigDecimal, int, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Compute a to the power of n, where n is an integer.
- pow(Complex, Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
z1 to the power z2.
- pow(double[], double) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Raise each element in an array to the power of the given exponent.
- PowellImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl
-
- PowellMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection
-
Powell's algorithm, starting from an initial point, performs a series
of line searches in one iteration.
- PowellMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer
-
Construct a multivariate minimizer using the Powell method.
- PowellMinimizer.PowellImpl - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection
-
an implementation of Powell's algorithm
- PowerLawSingularity - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is good for an integral which diverges at one of the end points.
- PowerLawSingularity(PowerLawSingularity.PowerLawSingularityType, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
Construct a PowerLawSingularity substitution rule.
- PowerLawSingularity.PowerLawSingularityType - Enum in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
the type of end point divergence
- PrecisionUtils - Class in com.numericalmethod.suanshu.misc
-
Precision-related utility functions.
- Preconditioner - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
Preconditioning reduces the condition number of the
coefficient matrix of a linear system to accelerate the convergence
when the system is solved by an iterative method.
- PreconditionerFactory - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
This constructs a new instance of
Preconditioner for a coefficient matrix.
- predict(DerivativeFunction, double, double[], Vector[]) - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
-
- previousWeekDay(DateTime) - Static method in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
-
Get the previous weekday, i.e., skipping Saturdays and Sundays.
- price1 - Variable in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- price2 - Variable in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- pricing(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
This is pivot column selection (pricing) rule.
- pricing(SimplexTable) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
-
This is pivot column selection (pricing) rule.
- pricing(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule
-
This is pivot column selection (pricing) rule.
- PrimalDualInteriorPointMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint
-
Solves a Dual Second Order Conic Programming problem using the Primal Dual
Interior Point algorithm.
- PrimalDualInteriorPointMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer
-
Constructs a Primal Dual Interior Point minimizer to solve Dual Second Order Conic
Programming problems.
- PrimalDualInteriorPointMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint
-
This is the solution to a Dual Second Order Conic Programming problem using the Primal Dual
Interior Point algorithm.
- PrimalDualPathFollowingMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing
-
The Primal-Dual Path-Following algorithm is an interior point method that solves Semi-Definite
Programming problems.
- PrimalDualPathFollowingMinimizer(double, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
-
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming
problems.
- PrimalDualPathFollowingMinimizer(double, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
-
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming
problems.
- PrimalDualPathFollowingMinimizer(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
-
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming
problems.
- PrimalDualPathFollowingMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing
-
This is the solution to a Semi-Definite Programming problem using the Primal-Dual
Path-Following algorithm.
- PrimalDualSolution - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint
-
The vector set {x, s, y} is a solution to both the primal and dual SOCP problems.
- PrimalDualSolution(Vector, Vector, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
-
Construct a solution to a primal and a dual SOCP problems.
- prob - Variable in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159.RandomMatrix
-
the probability of observing this matrix
- probability() - Method in class com.numericalmethod.suanshu.stats.distribution.discrete.ProbabilityMassFunction.Mass
-
Gets the probability of the outcome.
- ProbabilityDistribution - Interface in com.numericalmethod.suanshu.stats.distribution.univariate
-
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
- ProbabilityMassFunction<X> - Interface in com.numericalmethod.suanshu.stats.distribution.discrete
-
A probability mass function (pmf) is a function that gives the probability that a discrete random
variable is exactly equal to some value.
- ProbabilityMassFunction.Mass<X> - Class in com.numericalmethod.suanshu.stats.distribution.discrete
-
Stores a possible outcome for a probability distribution and its associated probability.
- ProbabilityMassQuantile<X> - Class in com.numericalmethod.suanshu.stats.distribution.discrete
-
As probability mass function is discrete, there are gaps between values in the domain of its cdf,
The quantile function is:
\[
Q(p)\,=\,\inf\left\{ x\in R : p \le F(x) \right\}
\]
- ProbabilityMassQuantile(Iterable<ProbabilityMassFunction.Mass<X>>, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.discrete.ProbabilityMassQuantile
-
Constructs the quantile function for a probability mass function.
- ProbabilityMassQuantile(Iterable<ProbabilityMassFunction.Mass<X>>) - Constructor for class com.numericalmethod.suanshu.stats.distribution.discrete.ProbabilityMassQuantile
-
Constructs the quantile function for a probability mass function.
- ProbabilityMassSampler<X> - Class in com.numericalmethod.suanshu.stats.distribution.discrete
-
A random sampler that is constructed ad-hoc from a list of values and their probabilities.
- ProbabilityMassSampler(List<ProbabilityMassFunction.Mass<X>>, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.distribution.discrete.ProbabilityMassSampler
-
Creates an instance with the probable values and an RNG.
- problem - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- problem - Variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- problem() - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Get the original covariance selection problem.
- problem() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection
-
Returns the original GLM problem.
- process() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightPhase1
-
Find a feasible table, if any.
- process() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightScheme2
-
Remove equalities and free variables, if possible.
- product(GivensMatrix[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Given an array of Givens matrices {Gi},
computes G, where
G = G1 * G2 * ...
- product(List<GivensMatrix>) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- product(HouseholderReflection[], int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Compute Q from Householder matrices {Qi}.
- product(HouseholderReflection[], int, int, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Compute Q from Householder matrices {Qi}.
- ProductOfWeights - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification
-
Defines portfolio diversification as
\[
D(w) = \prod_i w_i
\]
- ProductOfWeights() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification.ProductOfWeights
-
- Projection - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation
-
Project a vector v on another vector w or a set of vectors (basis) {wi}.
- Projection(Vector, List<Vector>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Projection
-
Project a vector v onto a set of basis {wi}.
- Projection(Vector, Vector[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Projection
-
Project a vector v onto a set of basis {wi}.
- Projection(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Projection
-
Project a vector v onto another vector.
- propagate() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
Propagates the grid to the next time step by solving \(Au=d\).
- proportionVar() - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the proportion of overall variance explained by each of the
principal components.
- proportionVar(int) - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the proportion of overall variance explained by the i-th
principal component.
- proportionVar() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
- ProposalFunction - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction
-
A proposal function goes from the current state to the next state, where a state is a vector.
- ProposalFunction(int, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction.ProposalFunction
-
- PROTON_ELECTRON_MASS_RATIO - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The proton-electron mass ratio \(m_p/m_e\) (dimensionless).
- PROTON_MASS_MP - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The proton mass \(m_p\) in kilograms (kg).
- PrZt0() - Method in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
Computes the stationary probability of in state Z_t = 0.
- PrZt1() - Method in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
Computes the stationary probability of in state Z_t = 1.
- PseudoInverse - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
The Moore-Penrose pseudo-inverse of an m x n matrix A is A+.
- PseudoInverse(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.PseudoInverse
-
Construct the Moore-Penrose pseudo-inverse matrix of a matrix.
- PseudoInverse(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.PseudoInverse
-
Construct the Moore-Penrose pseudo-inverse matrix of A.
- psi() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FAEstimator
-
Gets the estimated (optimal) psi, E(ee'), p.
- psi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the coefficients of the deterministic terms.
- psi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the coefficients of the deterministic terms.
- psi() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the coefficients of the deterministic terms.
- psi(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
- PureILPProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem
-
This is a pure integer linear programming problem, in which all variables are integral.
- PureILPProblem(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.PureILPProblem
-
Construct a pure ILP problem.
- pValue() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FAEstimator
-
Calculates the p-value of the test statistics, given the degree of
freedom.
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarling
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.distribution.CramerVonMises2Samples
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov1Sample
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov2Samples
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get the p-value for the test statistics.
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.rank.KruskalWallis
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
- pValue(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Compute the two-sided p-value for a critical value.
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
- pValue(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Compute the two-sided p-value for a critical value.
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.variance.Bartlett
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
- pValue() - Method in class com.numericalmethod.suanshu.stats.test.variance.Levene
-
- pValueAlternative() - Method in class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarling
-
Gets the alternative p-value (adjusted for ties).
- pvalueZ1() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
Get the p-value for Z1.
- pvalueZ2() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
Get the p-value for Z2.
- pw - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
number of each distinct eigenvalues
- pzw - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
number of eigenvalues that are less or equals to zero
- R() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
- R() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
- R() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QR
-
- R() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get the upper triangular matrix R in the QR decomposition, A = QR.
- R(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the source (or sink) value at a given time t and a position
x.
- r - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
-
the pivot row
- r - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- r(CointegrationMLE, double) - Method in class com.numericalmethod.suanshu.stats.cointegration.JohansenTest
-
Get the (most likely) order of cointegration.
- R1Projection - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
-
- R1Projection(RealVectorFunction, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.R1Projection
-
Construct a \(R^n \rightarrow R\) projection from a \(R^n \rightarrow R^m\) function f.
- R1toConstantMatrix - Class in com.numericalmethod.suanshu.analysis.function.matrix
-
A constant matrix function maps a real number to a constant matrix: \(R^n \rightarrow A\).
- R1toConstantMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.analysis.function.matrix.R1toConstantMatrix
-
Construct a constant matrix function.
- R1toMatrix - Class in com.numericalmethod.suanshu.analysis.function.matrix
-
This is a function that maps from R1 to a Matrix space.
- R1toMatrix() - Constructor for class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
-
- R2() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: R-squared.
- R2toMatrix - Class in com.numericalmethod.suanshu.analysis.function.matrix
-
This is a function that maps from R2 to a Matrix space.
- R2toMatrix() - Constructor for class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
-
- R_bar() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets R_bar, the average of observations over time per subject.
- R_bar() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets R_bar, the average of observations over time per subject.
- RamerDouglasPeucker - Class in com.numericalmethod.suanshu.geometry.polyline
-
The Ramer-Douglas-Peucker algorithm simplifies a
PolygonalChain by removing vertices
which do not affect the shape of the curve to a given tolerance.
- RamerDouglasPeucker(double) - Constructor for class com.numericalmethod.suanshu.geometry.polyline.RamerDouglasPeucker
-
Create an algorithm instance with a given threshold for the maximum distance between the
original chain and a point in the simplified chain.
- Rand1Bin - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
The Rand-1-Bin rule is defined by:
mutation by adding a scaled, randomly sampled vector difference to a third vector
(differential mutation);
crossover by performing a uniform crossover (discrete recombination).
- Rand1Bin(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin
-
Construct an instance of Rand1Bin.
- Rand1Bin.DeRand1BinCell - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
-
This chromosome defines the Rand-1-Bin rule.
- RandomBetaGenerator - Interface in com.numericalmethod.suanshu.stats.random.rng.univariate.beta
-
This is a random number generator that generates random deviates according to the Beta distribution.
- randomCSRSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- randomDenseMatrix(int, int, RandomNumberGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- randomDOKSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- RandomExpGenerator - Interface in com.numericalmethod.suanshu.stats.random.rng.univariate.exp
-
This is a random number generator that generates random deviates according to the exponential distribution.
- RandomGammaGenerator - Interface in com.numericalmethod.suanshu.stats.random.rng.univariate.gamma
-
This is a random number generator that generates random deviates according to the Gamma distribution.
- randomLILSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- RandomLongGenerator - Interface in com.numericalmethod.suanshu.stats.random.rng.univariate
-
A (pseudo) random number generator that generates a sequence of longs that lack any pattern and are uniformly distributed.
- randomLowerTriangularMatrix(int, RandomNumberGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- RandomNumberGenerator - Interface in com.numericalmethod.suanshu.stats.random.rng.univariate
-
A (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern.
- randomPositiveDefiniteMatrix(int, RandomNumberGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a random symmetric, positive definite matrix.
- RandomProcess - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random
-
This interface represents a univariate random process a.k.a.
- RandomProcess(TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomProcess
-
Construct a univariate random process.
- RandomRealizationGenerator - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random
-
This interface defines a generator to construct random realizations from a univariate stochastic process.
- RandomRealizationOfRandomProcess - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random
-
This class generates random realizations from a random/stochastic process.
- RandomRealizationOfRandomProcess(RandomProcess, int, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from a random/stochastic process.
- RandomRealizationOfRandomProcess(RandomProcess, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from a random/stochastic process.
- RandomRealizationOfRandomProcess(DiscreteSDE, TimeGrid, double) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from a discrete SDE.
- RandomRealizationOfRandomProcess(SDE, TimeGrid, double) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from an SDE.
- RandomRealizationOfRandomProcess(SDE, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from an SDE.
- RandomStandardNormalGenerator - Interface in com.numericalmethod.suanshu.stats.random.rng.univariate.normal
-
This is a random number generator that generates random deviates according to the standard Normal
distribution.
- randomSymmetricMatrix(int, RandomNumberGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- randomUpperTriangularMatrix(int, RandomNumberGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- RandomVectorGenerator - Interface in com.numericalmethod.suanshu.stats.random.rng.multivariate
-
A (pseudo) multivariate random number generator samples a random vector from a multivariate distribution.
- RandomWalk - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random
-
This is the Random Walk construction of a stochastic process per SDE
specification.
- RandomWalk(DiscreteSDE, TimeGrid, double) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomWalk
-
Constructs a univariate stochastic process from an SDE.
- RandomWalk(DiscreteSDE, double, double) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomWalk
-
Constructs a univariate stochastic process from an SDE.
- rank() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
- rank() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
Computes the rank by counting the number of non-zero rows in R.
- rank() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QR
-
- rank() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get the numerical rank of A as computed by the QR decomposition.
- rank() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the rank of A.
- rank(Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the numerical rank of a matrix.
- rank(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the numerical rank of a matrix.
- rank() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Get the rank of this vector space.
- rank() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
-
Get the rank of the system, i.e., the number of (real) eigenvalues.
- Rank - Class in com.numericalmethod.suanshu.stats.descriptive.rank
-
Rank is a relationship between a set of items such that, for any two items,
the first is either "ranked higher than", "ranked lower than" or "ranked
equal to" the second.
- Rank(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- Rank(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- Rank(double[], Rank.TiesMethod) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- Rank(double[], double, Rank.TiesMethod) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- rank(int) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Get the rank of the i-th element.
- Rank.TiesMethod - Enum in com.numericalmethod.suanshu.stats.descriptive.rank
-
The method for assigning ranks when some values are equal (called
'ties').
- RankOneMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
-
The Rank One method is a quasi-Newton method
to solve unconstrained nonlinear optimization problems.
- RankOneMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.RankOneMinimizer
-
Construct a multivariate minimizer using the Rank One method.
- ranks() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
Get the ranks of the values.
- Rastrigin - Class in com.numericalmethod.suanshu.analysis.function.special
-
The Rastrigin function is a non-convex function used as a performance test problem for
optimization algorithms.
- Rastrigin(int) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.Rastrigin
-
Constructs a Rastrigin function.
- rate0 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
- rate1 - Variable in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
- ratioTest(SimplexTable, int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
This is pivot row selection (Ratio test) rule.
- ratioTest(SimplexTable, int) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
-
This is pivot row selection (Ratio test) rule.
- RayleighDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The L2 norm of (x1, x2), where xi's are normal, uncorrelated, equal variance and
have the Rayleigh distributions.
- RayleighDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
Construct a Rayleigh distribution.
- RayleighRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
-
This random number generator samples from the Rayleigh distribution using the inverse transform sampling method.
- RayleighRNG(double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.RayleighRNG
-
Construct a random number generator to sample from the Rayleigh distribution.
- rBar() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004.Result
-
Gets the average correlation \(\bar{r}\).
- rbind(Vector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of vectors by rows.
- rbind(SparseVector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of sparse vectors by rows and returns a sparse matrix.
- rbind(List<Vector>) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines a list of array of vectors by rows.
- rbind(Matrix...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of matrices by rows.
- rbind(SparseMatrix...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of sparse matrices by rows.
- rbinom(int, int, Vector, RandomLongGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates n random binomial numbers.
- rbinom(int, int, Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates n random binomial numbers.
- readCSV1d(String) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a single-column CSV file (output by write.csv from R) into a
1-D double array.
- readCSV1d(String, boolean, boolean) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a single-column CSV file (output by write.csv from R) into a
1-D double array.
- readCSV1d(String, boolean, boolean, String) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a single-column CSV file (output by
write.csv from R) into a
1-D
double array, with a given separator which overrides the
default separator.
- readCSV1d(InputStream) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a single-column CSV file (output by write.csv from R) into a
1-D double array.
- readCSV1d(InputStream, boolean, boolean) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a single-column CSV file (output by write.csv from R) into a
1-D double array.
- readCSV1d(InputStream, boolean, boolean, String) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a single-column CSV file (output by
write.csv from R) into a
1-D
double array, with a given separator which overrides the
default separator.
- readCSV2d(String) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a multi-column CSV file (output by write.csv from R) into a
2-D double array.
- readCSV2d(String, boolean, boolean) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a multi-column CSV file (output by write.csv from R) into a
2-D double array.
- readCSV2d(String, boolean, boolean, String) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a multi-column CSV file (output by
write.csv from R) into a
2-D
double array, with a given separator which overrides the
default separator.
- readCSV2d(InputStream) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a multi-column CSV stream (output by write.csv from R) into
a 2-D double array.
- readCSV2d(InputStream, boolean, boolean) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a multi-column CSV stream (output by write.csv from R) into
a 2-D double array.
- readCSV2d(InputStream, boolean, boolean, String) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Read a multi-column CSV stream (output by
write.csv from R) into
a 2-D
double array, with a given separator which overrides the
default separator.
- real() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Get the real part of this complex number.
- Real - Class in com.numericalmethod.suanshu.number
-
A real number is an arbitrary precision number.
- Real(double) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from a double.
- Real(long) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from an integer.
- Real(BigDecimal) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from a BigDecimal.
- Real(BigInteger) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from a BigInteger.
- Real(String) - Constructor for class com.numericalmethod.suanshu.number.Real
-
Construct a Real from a String.
- RealInterval - Class in com.numericalmethod.suanshu.interval
-
This is an interval on the real line.
- RealInterval(Double, Double) - Constructor for class com.numericalmethod.suanshu.interval.RealInterval
-
Construct an interval on the real line.
- Realization - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime
-
This is a univariate time series indexed real numbers.
- Realization.Entry - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime
-
This is the TimeSeries.Entry for a real number -indexed univariate time series.
- realizedReturns(int) - Method in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA.Signal
-
Gets the last H-period accumulated realized return.
- RealMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype
-
- RealMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- RealMatrix(Real[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- RealMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- RealScalarFunction - Interface in com.numericalmethod.suanshu.analysis.function.rn2r1
-
A real valued function a \(R^n \rightarrow R\) function, \(y = f(x_1, ..., x_n)\).
- RealScalarFunctionChromosome - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid
-
This chromosome encodes a real valued function.
- RealScalarFunctionChromosome(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
Construct an instance of RealScalarFunctionChromosome.
- RealScalarSubFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
-
- RealScalarSubFunction(RealScalarFunction, Map<Integer, Double>) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarSubFunction
-
Construct a scalar sub-function.
- RealVectorFunction - Interface in com.numericalmethod.suanshu.analysis.function.rn2rm
-
A vector-valued function a \(R^n \rightarrow R^m\) function, \([y_1,...,y_m] = f(x_1,...,x_n)\).
- RealVectorSpace - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation
-
A vector space is a set of vectors that are closed under some operations.
- RealVectorSpace(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from a matrix (a set of column vectors).
- RealVectorSpace(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from a matrix (a set of column vectors).
- RealVectorSpace(List<Vector>, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from a list of vectors.
- RealVectorSpace(List<Vector>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from a list of vectors.
- RealVectorSpace(double, Vector...) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from an array of vectors.
- RealVectorSpace(Vector...) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from an array of vectors.
- RealVectorSubFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2rm
-
- RealVectorSubFunction(RealVectorFunction, Map<Integer, Double>) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2rm.RealVectorSubFunction
-
Construct a vector-valued sub-function.
- reciprocal(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the reciprocals of values.
- RecursiveGridInterpolation - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate
-
This algorithm works by recursively calling lower order interpolation (hence the cost is
exponential), until the given univariate algorithm can be used when the remaining dimension
becomes one.
- RecursiveGridInterpolation(Interpolation) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.RecursiveGridInterpolation
-
Create an n-dimensional interpolation with a given univariate interpolation algorithm.
- reduce(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
- Reference<T> - Class in com.numericalmethod.suanshu.misc.parallel
-
- Reference() - Constructor for class com.numericalmethod.suanshu.misc.parallel.Reference
-
- reflect(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
-
- reflect(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.Householder4ZeroGenerator
-
- reflect(HouseholderInPlace.Householder, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Reflects (or left transform) a range of columns in the underlying matrix with a given
Householder.
- reflect(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Apply the Householder matrix, H, to a column vector, x.
- reflect(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Apply the Householder matrix, H, to a matrix (a set of column
vectors), A.
- reflectColumns(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
- REFLECTION - Static variable in class com.numericalmethod.suanshu.stats.random.variancereduction.AntitheticVariates
-
- reflectRows(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
- reflectVectors(Vector[], int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Apply the Householder matrix, H, to an array of vectors.
- relations(Interval<T>) - Method in class com.numericalmethod.suanshu.interval.Interval
-
Determine the interval relations between this and Y.
- relativeError(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Compute the relative error for {x1, x0}.
- RelativeTolerance - Class in com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance
-
The stopping criteria is that the norm of the residual r relative to
the input base is equal to or smaller than the specified
tolerance, that is,
||r||2/base ≤ tolerance
- RelativeTolerance(double) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.RelativeTolerance
-
- RelativeTolerance(double, double) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.RelativeTolerance
-
Construct an instance with specified tolerance.
- remainder() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.HornerScheme
-
Get the remainder, P(x0).
- REMAINING_ROWS_PCT_THRESHOLD - Static variable in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.RobustCointegration
-
- remove() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
-
Overridden to avoid the vector being altered.
- remove(Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- removeActive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Remove an active index.
- removeActiveByIndex(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Remove an active constraint by index.
- removeAll(Collection<?>) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- removeContext() - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ContextRNG
-
- removeEdge(E) - Method in interface com.numericalmethod.suanshu.graph.Graph
-
Removes an edge from this graph.
- removeEdge(E) - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
-
- removeEdge(Arc<V>) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- removeEdge(Arc<VertexTree<T>>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- removeInactive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Remove an inactive index.
- removeInactiveByIndex(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
Remove an active constraint by index.
- removeIsolatedVertices(Graph<V, E>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
-
Removes those nodes that have no edges from a graph.
- removeMaxEdge() - Method in class com.numericalmethod.suanshu.graph.community.GirvanNewman
-
Removes the edge with the highest edge-betweeness.
- removeVertex(V) - Method in interface com.numericalmethod.suanshu.graph.Graph
-
Removes a vertex from this graph.
- removeVertex(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
-
- removeVertex(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- removeVertex(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- renameCol(int, Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Renames column i.
- renameRow(int, Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Renames row i.
- rep(double, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Generates an array of doubles of repeated values.
- rep(int, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Generates an array of ints of repeated values.
- replaceInPlace(Matrix, int, int, int, int, Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Replaces a sub-matrix of a matrix with a smaller matrix.
- Resampler - Interface in com.numericalmethod.suanshu.stats.random.sampler.resampler
-
This is the interface of a re-sampler method.
- ResamplerModel - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit
-
- residuals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
-
Gets the residual analysis of this GLM regression.
- residuals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
Gets the residual analysis.
- residuals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyLARS
-
- residuals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyQP
-
- residuals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
-
- residuals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.UnconstrainedLASSObyQP
-
- residuals() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.LinearModel
-
Gets the residual analysis of an OLS regression.
- residuals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
-
- residuals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
-
- residuals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the residuals, ε, the differences between sample and fitted values.
- residuals - Variable in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- Result(NonlinearFit.Result, ACERConfidenceInterval, ACERReturnLevel, EmpiricalACER) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
-
- retainAll(Collection<?>) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- ReturnLevel - Class in com.numericalmethod.suanshu.stats.evt.function
-
Given a GEV distribution of a random variable \(X\), the return level \(\eta\) is the value that
is expected to be exceeded on average once every interval of time \(T\), with a probability of
\(1 / T\).
- ReturnLevel(UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.function.ReturnLevel
-
Construct the return level function for a given univariate extreme value distribution.
- ReturnLevel(UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.evt.function.ReturnLevel
-
Construct the return level function with the inverse function of a univariate extreme value
distribution.
- ReturnPeriod - Class in com.numericalmethod.suanshu.stats.evt.function
-
The return period \(R\) of a level \(\eta\) for a random variable \(X\) is the mean number of
trials that must be done for \(X\) to exceed \(\eta\).
- ReturnPeriod(UnivariateEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.function.ReturnPeriod
-
Construct the return period function for a given univariate extreme value distribution.
- ReturnPeriod(UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.evt.function.ReturnPeriod
-
Construct the return period function with the cumulative distribution function of a
univariate extreme value distribution.
- returns - Variable in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- Returns - Class in com.numericalmethod.suanshu.finance.trading.returns
-
Contains utility methods related to returns computation.
- ReturnsCalculator - Interface in com.numericalmethod.suanshu.finance.trading.returns
-
This interface defines how return is computed from two values of a portfolio.
- ReturnsCalculators - Enum in com.numericalmethod.suanshu.finance.trading.returns
-
Various ways of return calculations.
- ReturnsMatrix - Class in com.numericalmethod.suanshu.finance.trading.returns
-
- ReturnsMatrix(Matrix, ReturnsCalculator) - Constructor for class com.numericalmethod.suanshu.finance.trading.returns.ReturnsMatrix
-
- ReturnsMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.finance.trading.returns.ReturnsMatrix
-
- ReturnsMoments - Class in com.numericalmethod.suanshu.finance.trading.returns.moments
-
Contains the estimated moments of asset returns.
- ReturnsMoments(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.finance.trading.returns.moments.ReturnsMoments
-
Constructs an instance.
- ReturnsMoments.Estimator - Interface in com.numericalmethod.suanshu.finance.trading.returns.moments
-
The interface to estimate moments from returns.
- ReturnsResamplerFactory - Interface in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler
-
This is a factory interface to construct new instances of multivariate
resamplers.
- reverse(T[]) - Static method in class com.numericalmethod.suanshu.misc.ArrayUtils
-
Reverse an array in place.
- reverse(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Reverse a double array.
- reverse(int...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Reverse an int array.
- reverseCopy(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get a reversed copy of a double array.
- reverseCopy(int...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get a reversed copy of a int array.
- ReversedWeibullDistribution - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate
-
The Reversed Weibull distribution is a special case (Type III) of the generalized extreme value
distribution, with \(\xi<0\).
- ReversedWeibullDistribution() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.ReversedWeibullDistribution
-
Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\),
shape \(\alpha=1\).
- ReversedWeibullDistribution(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.ReversedWeibullDistribution
-
Create an instance with the given parameter values.
- reverseRange(double[], int, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Reverses a range of elements in an array.
- rho() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets ρ as discussed in the reference.
- Ridders - Class in com.numericalmethod.suanshu.analysis.differentiation
-
Ridders' method computes the numerical derivative of a function.
- Ridders(UnivariateRealFunction, int, double, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Construct the derivative function of a univariate function using Ridder's method.
- Ridders(UnivariateRealFunction, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Construct the derivative function of a univariate function using Ridder's method.
- Ridders(RealScalarFunction, int[], double, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Construct the derivative function of a vector-valued function using Ridder's method.
- Ridders(RealScalarFunction, int[]) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.Ridders
-
Construct the derivative function of a vector-valued function using Ridder's method.
- Riemann - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
-
This is a wrapper class that integrates a function by using an appropriate integrator together with Romberg's method.
- Riemann(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
Construct an integrator.
- Riemann() - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
-
Construct an integrator.
- rightConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Get the one sided right confidence interval, [a, ∞)
- rightConfidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
Compute the one sided right confidence interval, [a, ∞)
- rightMultiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Right multiplication by G, namely, A * G.
- rightMultiply(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Right multiplication by P.
- rightMultiplyInPlace(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Right multiplication by G, namely, A * G.
- rightOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
Get the right, one-sided p-value.
- rightOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
Get the right, one-sided p-value.
- rightOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
Get the right, one-sided p-value.
- rightOneSidedPvalue(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Compute the one-sided p-value for the statistic greater than a critical value.
- rightOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
Get the right, one-sided p-value.
- rightOneSidedPvalue(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Compute the one-sided p-value for the statistic greater than a critical value.
- rightOneSidedPvalue() - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
Get the right, one-sided p-value.
- rightReflect(HouseholderInPlace.Householder, int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Reflects (or right transform) a range of rows in the underlying matrix with a given
Householder.
- rightReflect(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Apply the Householder matrix, H, to a matrix (a set of row
vectors), A.
- rightShift(T[]) - Static method in class com.numericalmethod.suanshu.misc.ArrayUtils
-
Get a right shifted array.
- rightShift(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Perform a in-memory right-shift (by 1 cell} to an array.
- rightShift(double[], int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Perform a in-memory right-shift (by k cells} to an array.
- rightShiftCopy(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get a right shifted (by 1 cell) copy of an array.
- rightShiftCopy(double[], int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get a right shifted (by k cells) copy of an array.
- Ring<R> - Interface in com.numericalmethod.suanshu.algebra.structure
-
A ring is a set R equipped with two binary operations called addition and multiplication:
+ : R × R → R and
⋅ : R × R → R
To qualify as a ring, the set and two operations, (R, +, ⋅), must satisfy the requirements known as the ring axioms.
- RNGUtils - Class in com.numericalmethod.suanshu.stats.random.rng
-
Provides static methods that wraps random number generators to produce synchronized generators.
- rnorm(int, RandomStandardNormalGenerator) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates n random standard Normals.
- rnorm(int) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates n random standard Normals.
- RNORM - Static variable in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
- RntoMatrix - Interface in com.numericalmethod.suanshu.analysis.function.matrix
-
This interface is a function that maps from Rn to a Matrix space.
- RobustAdaptiveMetropolis - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis
-
A variation of
Metropolis, that uses the estimated covariance of the target
distribution in the proposal distribution, based on a paper by Vihola (2011).
- RobustAdaptiveMetropolis(RealScalarFunction, Matrix, double, double, Vector, RandomStandardNormalGenerator, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
-
Constructs a new instance with the given parameters.
- RobustAdaptiveMetropolis(RealScalarFunction, double, Vector, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
-
Constructs an instance which assumes an initial variance of 1 per variable, uses a gamma of
0.5.
- RobustCointegration - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
This class runs the robust cointegration algorithm on a pair of prices to
determine if their cointegration relationship is stable enough to trade.
- RobustCointegration() - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.RobustCointegration
-
- Romberg - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes
-
Romberg's method computes an integral by generating a sequence of estimations of the integral value and then doing an extrapolation.
- Romberg(IterativeIntegrator) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Romberg
-
Extend an integrator using Romberg's method.
- root() - Method in interface com.numericalmethod.suanshu.graph.RootedTree
-
Gets the root of this tree.
- root() - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- root() - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- root() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- ROOT_2 - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
\(\sqrt{2}\)
- ROOT_2_PI - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
\(\sqrt{2\pi}\)
- ROOT_PI - Static variable in class com.numericalmethod.suanshu.misc.Constants
-
\(\sqrt{\pi}\)
- RootedTree<V,E extends Arc<V>> - Interface in com.numericalmethod.suanshu.graph
-
A rooted tree is a directed graph, and has a root to measure distance from the
root.
- rotate(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- rotate(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
This method re-pivots the tree with a new root vertex.
- round(double, DoubleUtils.RoundingScheme) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Round up or down a number to an integer.
- round(double, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Round a number to the precision specified.
- round(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers
-
- round(Vector) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.IntegerConstraint
-
- round(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers
-
- Row(S, String, double...) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData.Row
-
- rowIndices() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
-
- rowMeans(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the row means.
- rowMeanVector(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the row mean vector of a given matrix.
- rows(Matrix, int[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Construct a sub-matrix from the rows of a matrix.
- rows(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the rows of a matrix.
- rowSums(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the row sums.
- rowSumVector(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the row sum vector of a given matrix.
- RSS() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: sum of squared residuals, \(\sum \epsilon^2\).
- run(SubMatrixBlock, SubMatrixBlock, SubMatrixBlock) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.BlockWinogradAlgorithm
-
- run(SubMatrixBlock, SubMatrixBlock, SubMatrixBlock) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock.BlockAlgorithm
-
Runs the matrix multiplication task.
- run(T) - Method in interface com.numericalmethod.suanshu.misc.parallel.IterationBody
-
Execute a (parallel) task.
- run(int) - Method in interface com.numericalmethod.suanshu.misc.parallel.LoopBody
-
This method contains the code inside the for-loop, as in a native
for-loop like this:
- run() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Run the genetic algorithm.
- run(double[], double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Run the analysis with single-period observations.
- run(double[][], double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Run the analysis with multi-period observations.
- run() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ExplicitImplicitModelPCA
-
- run() - Method in class com.numericalmethod.suanshu.stats.factor.implicitmodelpca.ImplicitModelPCA
-
Runs the regression.
- RungeKutta - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
The Runge-Kutta methods are an important family of implicit and explicit iterative methods for
the approximation of solutions of ordinary differential equations.
- RungeKutta(RungeKuttaStepper, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta
-
Constructs a Runge-Kutta algorithm with the given integrator and the constant step size.
- RungeKutta(RungeKuttaStepper, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta
-
Constructs a Runge-Kutta algorithm with the given integrator and the constant number of
steps.
- RungeKutta1 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the first-order Runge-Kutta formula, which is the same as the Euler method.
- RungeKutta1() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta1
-
- RungeKutta10 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the tenth-order Runge-Kutta formula.
- RungeKutta10() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta10
-
- RungeKutta2 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the second-order Runge-Kutta formula, which can be implemented efficiently with a
three-step algorithm.
- RungeKutta2() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta2
-
- RungeKutta3 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the third-order Runge-Kutta formula.
- RungeKutta3() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta3
-
- RungeKutta4 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the fourth-order Runge-Kutta formula.
- RungeKutta4() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta4
-
- RungeKutta5 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the fifth-order Runge-Kutta formula.
- RungeKutta5() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta5
-
- RungeKutta6 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the sixth-order Runge-Kutta formula.
- RungeKutta6() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta6
-
- RungeKutta7 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the seventh-order Runge-Kutta formula.
- RungeKutta7() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta7
-
- RungeKutta8 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the eighth-order Runge-Kutta formula.
- RungeKutta8() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta8
-
- RungeKuttaFehlberg - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
The Runge-Kutta-Fehlberg method is a version of the classic Runge-Kutta method, which
additionally uses step-size control and hence allows specification of a local truncation error
bound.
- RungeKuttaFehlberg(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
-
- RungeKuttaFehlberg(double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
-
Create a new instance of the Runge-Kutta-Fehlberg method with the given safety factor.
- RungeKuttaIntegrator - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This integrator works with a single-step stepper which estimates the solution for the next step
given the solution of the current step.
- RungeKuttaIntegrator(RungeKuttaStepper) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaIntegrator
-
- RungeKuttaStepper - Interface in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta
-
- RYDBERG_RINF - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The Rydberg constant \(R_{\infty}\) in reciprocal meter (m-1).
- s() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets the value of s.
- S - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
-
This is the auxiliary helper to solve the dual problem.
- s - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
-
This is the auxiliary helper to solve the dual problem.
- s - Variable in exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPUnbounded
-
This is the pricing column that does not have an eligible row that passes the ratio test,
hence the problem is unbounded.
- s - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
-
the pivot column
- S() - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionProblem
-
Gets the original sample covariance matrix.
- S() - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.LedoitWolf2016.Result
-
Sample covariance matrix.
- S() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004.Result
-
Gets the sample covariance matrix S.
- s() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
\[ s = \sum(t_i^2 - t_i) \]
- S() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Gets the covariance (or correlation) matrix.
- S() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
-
Gets the tuned scaling matrix (this changes each time a new sample is drawn).
- S_t_hat(int) - Method in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA.Signal
-
Predicts the accumulated H-period return at time t.
- SAAM - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.nmsaam
-
- SAAM(double, double, int, int, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.nmsaam.SAAM
-
- SAAM(double, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.nmsaam.SAAM
-
- SAAM(double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.nmsaam.SAAM
-
- SAAM(double, double, int) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.nmsaam.SAAM
-
- SAAM(double, int) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.nmsaam.SAAM
-
- sample() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
Get the sample kurtosis (biased estimator).
- sample() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Get the sample skewness (biased estimator).
- SampleAutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
This is the sample Auto-Correlation Function (ACF) for a univariate data set.
- SampleAutoCorrelation(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
Construct the sample ACF for a time series.
- SampleAutoCorrelation(IntTimeTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
Construct the sample ACF for a time series.
- SampleAutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
This is the sample Auto-Covariance Function (ACVF) for a univariate data set.
- SampleAutoCovariance(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCovariance
-
Construct the sample ACVF for a time series.
- SampleAutoCovariance(IntTimeTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCovariance
-
Construct the sample ACVF for a time series.
- SampleAutoCovariance.Type - Enum in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
the available auto-covariance types
- SampleCovariance - Class in com.numericalmethod.suanshu.stats.descriptive.covariance
-
This class computes the Covariance matrix of a matrix, where the (i, j) entry is the
covariance of the i-th column and j-th column of the matrix.
- SampleCovariance(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.SampleCovariance
-
Construct the covariance matrix of a matrix.
- SampleCovariance(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.SampleCovariance
-
Construct the covariance matrix of a matrix.
- SampleCovarianceEstimator() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleCovarianceEstimator
-
- SampleMeanEstimator() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleMeanEstimator
-
- SamplePartialAutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
-
This is the sample partial Auto-Correlation Function (PACF) for a univariate data set.
- SamplePartialAutoCorrelation(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
Construct the sample PACF for a time series.
- SamplePartialAutoCorrelation(IntTimeTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
Construct the sample PACF for a time series.
- scale() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.Pow
-
Get the exponential of the coefficient.
- scale(double[], double) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Scale each element in an array by a multiplier.
- scale() - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the scalings applied to each variable.
- scale() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
Gets the scalings applied to each variable.
- scale() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbySVD
-
- scaleColumn(int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Scale a column:
A[, j] = c * A[, j]
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- scaled(double) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.Matrix
-
Scale this matrix, A, by a constant.
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Multiply the elements in this by a scalar, element-by-element.
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- scaled(MatrixAccess, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- scaled(MatrixAccess, double) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
c * A
- scaled(MatrixAccess, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- scaled(Real) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- scaled(Complex) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- scaled(F) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- scaled(Real) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- scaled(Real) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- scaled(Vector, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Scales a vector, element-by-element.
- scaled(Vector, Real) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Scales a vector, element-by-element.
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- scaled(Real) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- scaled(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- scaled(Real) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- scaled(double) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Scale this vector by a constant, entry-by-entry.
- scaled(Real) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Scale this vector by a constant, entry-by-entry.
- scaled(F) - Method in interface com.numericalmethod.suanshu.algebra.structure.VectorSpace
-
× : F × V → V
The result of applying this function to a scalar,
c, in
F and
v in
V is denoted
cv.
- scaled(Real) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- scaled(double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- scaled(double[], double) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
-
- scaled(double[], double) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
-
Scale each entry of a double array.
- scaled(double[], double) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation
-
- scaled(double[], double) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- scaledAlpha(int) - Method in class com.numericalmethod.suanshu.stats.hmm.ForwardBackwardProcedure
-
Gets the scaled forward probabilities at time t.
- scaledBeta(int) - Method in class com.numericalmethod.suanshu.stats.hmm.ForwardBackwardProcedure
-
Gets the scaled backward probabilities at time t.
- scaledBetas() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the entire sequence of estimated (LARS) regression coefficients,
scaled by the L2 norm of each row.
- ScaledPolynomial - Class in com.numericalmethod.suanshu.analysis.function.polynomial
-
This constructs a scaled polynomial that has neither too big or too small coefficients,
hence avoiding overflow or underflow.
- ScaledPolynomial(Polynomial, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.ScaledPolynomial
-
Construct a scaled polynomial, with a base of the scaling factor.
- ScaledPolynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.ScaledPolynomial
-
Construct a scaled polynomial, with 2 as the base of the scaling factor.
- scaleRow(int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Scale a row:
A[i, ] = c * A[i, ]
- ScientificNotation - Class in com.numericalmethod.suanshu.number
-
Scientific notation expresses a number in this form
x = a * 10b
a is called the significand or mantissa, and 1 ≤ |a| < 10.
- ScientificNotation(double, int) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a number in this form: x = a * 10b.
- ScientificNotation(BigDecimal, int) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a number in this form: x = a * 10b.
- ScientificNotation(BigDecimal) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a number.
- ScientificNotation(BigInteger) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of an integer.
- ScientificNotation(long) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a long.
- ScientificNotation(double) - Constructor for class com.numericalmethod.suanshu.number.ScientificNotation
-
Construct the scientific notation of a double.
- scores() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FAEstimator
-
Gets the matrix of scores, computed using either Thompson's (1951)
scores, or Bartlett's (1937) weighted least-squares scores.
- scores() - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the scores of supplied data on the principal components.
- scoringRule() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis
-
Gets the scoring rule.
- SDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
-
This class represents a univariate, continuous-time Stochastic Differential Equation (SDE) of
the following form.
- SDE(Drift, Diffusion) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.SDE
-
Construct a univariate diffusion type stochastic differential equation.
- SDPDualProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem
-
A dual SDP problem, as in equation 14.4 in the reference, takes the following form.
- SDPDualProblem(Vector, SymmetricMatrix, SymmetricMatrix[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Constructs a dual SDP problem.
- SDPDualProblem.EqualityConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem
-
This is the collection of equality constraints:
\[
\sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0}
\]
- SDPPrimalProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem
-
A Primal SDP problem, as in equation 14.1 in the reference, takes the
following form.
- SDPPrimalProblem(SymmetricMatrix, SymmetricMatrix[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
-
Constructs a primal SDP problem.
- sdPrincipalComponent(int) - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the standard deviation of the i-th principal component.
- sdPrincipalComponents() - Method in interface com.numericalmethod.suanshu.stats.factor.pca.PCA
-
Gets the standard deviations of the principal components (i.e., the
square roots of the eigenvalues of the correlation (or covariance)
matrix).
- sdPrincipalComponents() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
Gets the standard deviations of the principal components (i.e., the
square roots of the eigenvalues of the correlation (or covariance)
matrix).
- sdPrincipalComponents() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbySVD
-
Gets the standard deviations of the principal components (i.e., the
square roots of the eigenvalues of the correlation (or covariance)
matrix, though the calculation is actually done with the singular values
of the data matrix)
- SDPT3v4 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint
-
This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3
version 4.
- search(Vector...) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution
-
- search(Ceta, double, double) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
- search(Ceta, double, double) - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer
-
Searches the maximal point of a given C(η) function within a
given range.
- search(Ceta, double, double) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
-
- search(Ceta, double, double) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
- search(BBNode...) - Method in class com.numericalmethod.suanshu.misc.algorithm.bb.BranchAndBound
-
- search(S...) - Method in interface com.numericalmethod.suanshu.misc.algorithm.iterative.IterativeMethod
-
Search for a solution that optimizes the objective function from the
given starting points.
- search(CentralPath...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
Search for a solution that optimizes the objective function from the
given starting points.
- search() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
Searches for a solution that optimizes the objective function from the default starting
points.
- search(CentralPath) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
Search for a solution that optimizes the objective function from the given starting
points.
- search(CentralPath) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
-
- search(CentralPath...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- search(CentralPath) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- search() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
Search for a solution that optimizes the objective function from the given starting
points.
- search(PrimalDualSolution...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
- search(PrimalDualSolution) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
Searches for a solution that optimizes the objective function from the given starting
point.
- search() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
Searches for a solution that optimizes the objective function from the starting point
given by K.
- search() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
Searches for a minimizer for the quadratic programming problem.
- search(QPSolution...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
- search() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
Searches for a minimizer for the quadratic programming problem.
- search(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
Searches for a minimizer for the quadratic programming problem.
- search(QPSolution) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
Searches for a minimizer for the quadratic programming problem from
the given starting points.
- search(QPSolution...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- search(QPSolution...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
Search for a solution that optimizes the objective function from the
given starting points.
- search(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
Search for a solution that minimizes the objective function from the
given starting point.
- search(Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
Search for a solution that minimizes the objective function from the
given starting point.
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer.Solution
-
- search(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
-
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
Perform a Nelder-Mead search from an initial simplex.
- search(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- search(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- search(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BrentMinimizer.Solution
-
- search(double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BrentMinimizer.Solution
-
- search(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.FibonaccMinimizer.Solution
-
- search(double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.FibonaccMinimizer.Solution
-
- search(double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.GoldenMinimizer.Solution
-
- search(double, double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- search(double, double) - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer.Solution
-
- search(double, double, double) - Method in interface com.numericalmethod.suanshu.optimization.univariate.UnivariateMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- search(double, double) - Method in interface com.numericalmethod.suanshu.optimization.univariate.UnivariateMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- search(int) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
-
Performs a search for parameters for a given id.
- search() - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
-
Performs a search for parameters with no id.
- sec(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the secant of an angle.
- sech(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the hyperbolic secant of a hyperbolic angle.
- seed(long...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.initialization.UniformDistributionOverBox1
-
Seed the random number generator to produce repeatable sequences.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.distribution.discrete.ProbabilityMassSampler
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.markovchain.ExtremeValueMC
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.evt.timeseries.MARMASim
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.hmm.HMMRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedGenerator
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRLG
-
Delegate to the underlying random long generator.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRNG
-
Delegate to the underlying random number generator.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRVG
-
Delegate to the underlying random vector generator.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ContextRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.BurnInRVG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.HypersphereRVG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.IID
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.MultinomialRVG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.NormalRVG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.ThinRVG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.UniformDistributionOverBox
-
- SEED - Static variable in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BernoulliTrial
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.Cheng1978
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.VanDerWaerden1969
-
Deprecated.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BinomialRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.BurnInRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.exp.Ziggurat2000Exp
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.KunduGupta2007
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.MarsagliaTsang2000
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.XiTanLiu2010a
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.gamma.XiTanLiu2010b
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.InverseTransformSampling
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.LogNormalRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.BoxMuller
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.ConcurrentStandardNormalRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.MarsagliaBray1964
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.NormalRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.Ziggurat2000
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.Zignor2005
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.poisson.Knuth1969
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.ThinRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.LEcuyer
-
Seed the random number/vector/scenario generator to produce repeatable experiments.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.Lehmer
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.MRG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.MWC8222
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR0
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR3
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.UniformRNG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate.GroupResampler
-
- seed(long...) - Method in interface com.numericalmethod.suanshu.stats.random.Seedable
-
Seed the random number/vector/scenario generator to produce repeatable experiments.
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateBrownianRRG
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomWalk
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomProcess
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomWalk
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUSim
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMASim
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
-
- seed(long...) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
- Seedable - Interface in com.numericalmethod.suanshu.stats.random
-
A seed-able experiment allow the same experiment to be repeated in exactly the same way.
- select(double[], DoubleUtils.which) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Select the array elements which satisfy the boolean test.
- select(int[], DoubleUtils.which) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Select the array elements which satisfy the boolean test.
- select(GLMProblem, Matrix, int[]) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.ForwardSelection.Step
-
- select(GLMProblem, Matrix, int[]) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.SelectionByAIC
-
- select(GLMProblem, Matrix, int[]) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.SelectionByZValue
-
- SelectionByAIC - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
In each step, a factor is added if the resulting model has the highest AIC, until no factor
addition can result in a model with AIC higher than the current AIC.
- SelectionByAIC() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.SelectionByAIC
-
- SelectionByZValue - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
-
In each step, the most significant factor is added, until all remaining factors are
insignificant.
- SelectionByZValue(double) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.SelectionByZValue
-
Creates an instance with the given significance level [0, 1].
- SemiImplicitExtrapolation - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation
-
- SemiImplicitExtrapolation(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation
-
Create an instance of this algorithm with the given precision parameter and the maximum
number of iterations allowed.
- seq(int, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Generate a sequence of double values with a given start value and
a given constant increment.
- seq(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Generates a sequence of doubles from from up to
to with increments inc.
- seq(double, double, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Generate a sequence of n equi-spaced double values, from
start to end (inclusive).
- seq(int, int, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Generates a sequence of ints from from up to to
with increments inc.
- seq(int, int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Generates a sequence of ints from from up to to
with increments 1.
- Sequence - Interface in com.numericalmethod.suanshu.analysis.sequence
-
A sequence is an ordered list of (real) numbers.
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- set(int, int, double) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixAccess
-
Set the matrix entry at [i,j] to a value.
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- set(int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Deprecated.
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- set(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- set(int, int, F) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.generic.GenericMatrixAccess
-
Set the matrix entry at [i,j] to a value.
- set(int, int, Complex) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- set(int, int, F) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- set(int, int, Real) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- set(int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.CombinedVectorByRef
-
Deprecated.
- set(int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- set(int, DenseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
Replaces a sub-vector v[from : replacement.length] by a
replacement starting at position from.
- set(int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
This method is overridden to always throw VectorAccessException.
- set(int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
Deprecated.
- set(int, double) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Change the value of an entry in this vector.
- set(int, int, double) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
- set(T, int...) - Method in class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalArray
-
- set(T, int...) - Method in interface com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalCollection
-
Replaces the element at the specified position in this list with the specified element.
- set(int, int) - Method in class com.numericalmethod.suanshu.misc.datastructure.SortableArray
-
- set(T) - Method in class com.numericalmethod.suanshu.misc.parallel.Reference
-
- set(RealScalarFunction, EqualityConstraints) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Associate this variation to a particular general constrained minimization problem with only equality constraints.
- set(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPASVariation1
-
Associate this variation to a particular general constrained minimization
problem.
- setColumn(int, double...) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Set the values for a column in the matrix, i.e., [*, j].
- setColumn(int, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Set the values for a column in the matrix, i.e., [*, j].
- setColumn(int, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Changes the matrix column values to a vector value.
- setContext(long) - Method in class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ThreadIDRNG
-
Sets the context of this thread.
- setDeltaT(double) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
-
Sets the value of dt that will be used in the subsequent iterations.
- setDomain(List<Vector>) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.BruteForceMinimizer.Solution
-
Gives the domain values for the brute force search to try.
- setDt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
Set the current time differential.
- setDt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFtWt
-
- setDt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Set the current time differential.
- setDt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
- setFt(Filtration) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.F_Sum_BtDt
-
- setFt(Filtration) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.F_Sum_tBtDt
-
- setFt(Filtration) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.FiltrationFunction
-
Set the filtration for this function.
- setInitials(BBNode...) - Method in class com.numericalmethod.suanshu.misc.algorithm.bb.BranchAndBound
-
- setInitials(S...) - Method in interface com.numericalmethod.suanshu.misc.algorithm.iterative.IterativeMethod
-
Supply the starting points for the search.
- setInitials(CentralPath...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- setInitials(CentralPath...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- setInitials(PrimalDualSolution...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
- setInitials(QPSolution...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
No need to set initials for the dual problem.
- setInitials(QPSolution...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- setInitials(QPSolution...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- setInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
Supply the starting points for the search.
- setInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- setInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
- setInitials(Vector...) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- setLicenseFile(File) - Static method in class com.numericalmethod.suanshu.misc.license.License
-
Overrides the default license file.
- setLicenseKey(String) - Static method in class com.numericalmethod.suanshu.misc.license.License
-
Sets the license key for this invocation.
- setParent(BFS.Node<V>) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS.Node
-
- setParent(V) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal.Node
-
Sets the parent of the node.
- setPopulation(List<Chromosome>) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
- setPopulation(List<Chromosome>) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Set the current generation.
- setRow(int, double...) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Set the values for a row in the matrix, i.e., [i, *].
- setRow(int, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Set the values for a row in the matrix, i.e., [i, *].
- setRow(int, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Changes the matrix row values to a vector value.
- setVisitTime(int) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal.Node
-
- setXt(Vector) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
Set the current value of the stochastic process.
- setXt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Set the current value of the stochastic process.
- setZt(Vector) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt
-
Set the current value of the Gaussian innovation.
- setZt(Vector) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFtWt
-
- setZt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
-
Set the current value of the Gaussian innovation.
- setZt(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
- ShapiroWilk - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
-
The Shapiro-Wilk test tests the null hypothesis that a sample comes from a normally distributed population.
- ShapiroWilk(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk
-
Perform the Shapiro-Wilk test to test for the null hypothesis that a sample comes from a normally distributed population.
- ShapiroWilkDistribution - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
-
Shapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics,
which tests the null hypothesis that a sample comes from a normally distributed population.
- ShapiroWilkDistribution(int) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
Construct a Shapiro-Wilk distribution.
- shellsort(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Sort an array using Shell sort.
- ShortestPath<V> - Interface in com.numericalmethod.suanshu.graph.algorithm.shortestpath
-
In graph theory, a shortest path algorithm finds a path between two vertices in a graph such that
the sum of the weights of its constituent edges is minimized.
- shouldUseParallel(int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized
-
- SHR0 - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform
-
SHR0 is a simple uniform random number generator.
- SHR0() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR0
-
- SHR3 - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform
-
SHR3 is a 3-shift-register generator with period 2^32-1.
- SHR3() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.SHR3
-
- shutdown() - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
-
Shuts down the executor gracefully.
- SiegelTukey - Class in com.numericalmethod.suanshu.stats.test.rank
-
The Siegel-Tukey test tests for differences in scale (variability) between two groups.
- SiegelTukey(double[], double[], double, boolean) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
Perform the Siegel-Tukey test to test for differences in scale (variability) between two
groups.
- SiegelTukey(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
Perform the Siegel-Tukey test to test for differences in scale (variability) between two
groups.
- SiegelTukey(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
Perform the Siegel-Tukey test to test for differences in scale (variability) between two
groups.
- sigma() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- sigma(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the diffusion coefficient at a given time t and a position
x.
- sigma - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.ModelParam
-
- sigma() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
Gets the diffusion volatility.
- sigma - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- Sigma() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- Sigma() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPRiskConstraint
-
- sigma - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
-
the standard deviation
- sigma() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get the diffusion matrix: \(\sigma(t, X_t, Z_t, ...)\).
- sigma() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
-
- sigma() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUProcess
-
Get the volatility.
- sigma() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the white noise covariance matrix.
- sigma() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the white noise covariance matrix.
- sigma() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the white noise variance.
- sigma2 - Variable in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- sigma2() - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.ResamplerModel
-
Gets the conditional variances of residuals over time.
- sigma2() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleAR1Fit
-
- sigma2() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleGARCHFit
-
- sigma2(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Computes the conditional variance based on the past information.
- sigma2(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
-
Computes the conditional variance based on the past information.
- sigma2(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Compute the conditional variance based on the past information.
- sigma_ij(int, int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
- sigma_ij(int, int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
-
Get the Ft adapted function the D[i,j] entry in the diffusion matrix.
- sign() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Gets the sign of the permutation matrix which is also the determinant.
- significand() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
-
Get the significand.
- signum(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the signs of values.
- SimilarMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
Given a matrix A and an invertible matrix P, we construct the similar matrix
B s.t.,
B = P-1AP
- SimilarMatrix(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SimilarMatrix
-
Constructs the similar matrix B = P-1AP.
- simObs(int) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Simulates a sequence of observations per the model specification.
- SimpleAnnealingFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction
-
This annealing function takes a random step in a uniform direction, where the step size depends
only on the temperature.
- SimpleAnnealingFunction(RandomVectorGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction
-
- SimpleAR1Fit - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit
-
This class does a quick AR(1) fitting to the time series, essentially
treating the returns as independent.
- SimpleAR1Fit(Matrix) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleAR1Fit
-
- SimpleAR1Moments - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit
-
- SimpleAR1Moments() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleAR1Moments
-
- SimpleArc<V> - Class in com.numericalmethod.suanshu.graph.type
-
A simple arc has two vertices: head and tail.
- SimpleArc(V, V, double) - Constructor for class com.numericalmethod.suanshu.graph.type.SimpleArc
-
Construct a simple arc.
- SimpleArc(V, V) - Constructor for class com.numericalmethod.suanshu.graph.type.SimpleArc
-
Construct a simple arc.
- SimpleCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
-
- SimpleCellFactory - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid
-
- SimpleCellFactory(double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
-
Construct an instance of a SimpleCellFactory.
- SimpleCellFactory.SimpleCell - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid
-
A SimpleCell implements the two genetic operations.
- SimpleDoubleArrayOperation - Class in com.numericalmethod.suanshu.number.doublearray
-
This is a simple, single-threaded implementation of the array math operations.
- SimpleDoubleArrayOperation() - Constructor for class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation
-
- SimpleEdge<V> - Class in com.numericalmethod.suanshu.graph.type
-
A simple edge has two vertices.
- SimpleEdge(V, V, double) - Constructor for class com.numericalmethod.suanshu.graph.type.SimpleEdge
-
Construct a simple edge.
- SimpleEdge(V, V) - Constructor for class com.numericalmethod.suanshu.graph.type.SimpleEdge
-
Construct a simple edge.
- SimpleGARCHFit - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit
-
This class does a quick GARCH(1,1) fitting to the time series, essentially
treating the returns as independent.
- SimpleGARCHFit(Matrix) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleGARCHFit
-
- SimpleGARCHMoments1 - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit
-
Estimates the moments by GARCH model.
- SimpleGARCHMoments1() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleGARCHMoments1
-
- SimpleGARCHMoments2 - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit
-
Estimates the moments by GARCH model.
- SimpleGARCHMoments2(GARCHResamplerFactory2) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleGARCHMoments2
-
- SimpleGridMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid
-
This minimizer is a simple global optimization method.
- SimpleGridMinimizer(SimpleGridMinimizer.NewCellFactoryCtor, RandomLongGenerator, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
Construct a SimpleGridMinimizer to solve unconstrained minimization problems.
- SimpleGridMinimizer(RandomLongGenerator, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
Construct a SimpleGridMinimizer to solve unconstrained minimization problems.
- SimpleGridMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
Construct a SimpleGridMinimizer to solve unconstrained minimization problems.
- SimpleGridMinimizer.NewCellFactoryCtor - Interface in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid
-
This factory constructs a new SimpleCellFactory for each minimization problem.
- SimpleGridMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid
-
This is the solution to a minimization problem using SimpleGridMinimizer.
- SimpleMatrixMathOperation - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation
-
This is a generic, single-threaded implementation of matrix math operations.
- SimpleMatrixMathOperation() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- SimpleMC - Class in com.numericalmethod.suanshu.stats.markovchain
-
This is a time-homogeneous Markov chain with a finite state space.
- SimpleMC(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
-
Constructs a time-homogeneous Markov chain with a finite state space.
- SimpleTemperatureFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction
-
Abstract class for the common case where \(T^V_t = T^A_t\).
- SimpleTemperatureFunction() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
-
- SimpleTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime
-
This simple univariate time series simply wraps a double[] to form a time series.
- SimpleTimeSeries(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries.
- SimplexCuttingPlaneMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane
-
The use of cutting planes to solve Mixed Integer Linear Programming (MILP) problems was introduced by Ralph E Gomory.
- SimplexCuttingPlaneMinimizer(LPSimplexSolver, SimplexCuttingPlaneMinimizer.CutterFactory) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer
-
Construct a cutting-plane minimizer to solve an MILP problem.
- SimplexCuttingPlaneMinimizer.CutterFactory - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane
-
This factory constructs a new Cutter for each MILP problem.
- SimplexCuttingPlaneMinimizer.CutterFactory.Cutter - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane
-
A Cutter defines how to cut a simplex table, i.e., how to relax a linear program so that
the current non-integer solution is no longer feasible to the relaxation.
- SimplexPivoting - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
A simplex pivoting finds a row and column to exchange to reduce the cost function.
- SimplexPivoting.Pivot - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
the pivot
- SimplexTable - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
This is a simplex table used to solve a linear programming problem using a simplex method.
- SimplexTable(LPCanonicalProblem1, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a canonical linear programming problem.
- SimplexTable(LPCanonicalProblem1) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a canonical linear programming problem.
- SimplexTable(LPProblem, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a general linear programming problem.
- SimplexTable(LPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a general linear programming problem.
- SimplexTable(SimplexTable) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Copy constructor.
- SimplexTable.Label - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
- SimplexTable.LabelType - Enum in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
- simplify(PolygonalChain) - Method in class com.numericalmethod.suanshu.geometry.polyline.RamerDouglasPeucker
-
Simplify the given polygonal chain.
- Simpson - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes
-
Simpson's rule can be thought of as a special case of Romberg's method.
- Simpson(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
Construct an integrator that implements Simpson's rule.
- simStates(int) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Simulates a sequence of states per the model specification.
- SimulatedAnnealingMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing
-
Simulated Annealing is a global optimization meta-heuristic that is inspired by annealing in
metallurgy.
- SimulatedAnnealingMinimizer(TemperatureFunction, AnnealingFunction, TemperedAcceptanceProbabilityFunction, int, StopCondition, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
-
Constructs a new instance.
- SimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
-
- sin(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the sine of a vector, element-by-element.
- sin(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Sine of a complex number.
- sinc(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the unnormalized sinc function of an angle.
- SingularValueByDQDS - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds
-
Computes all the singular values of a bidiagonal matrix.
- SingularValueByDQDS(BidiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds.SingularValueByDQDS
-
Computes all the singular values of a bidiagonal matrix.
- sinh(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Hyperbolic sine of a complex number.
- size() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Get the number of distinct eigenvalues.
- size() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- size() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the number of variables in the linear system.
- size() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- size() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.CombinedVectorByRef
-
- size() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- size() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- size() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- size() - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Get the length of this vector.
- size(int) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
-
- size(int) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateGrid
-
Get the size of the grid in the given dimension xi.
- size(int) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
-
- size() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
- size() - Method in interface com.numericalmethod.suanshu.analysis.function.tuple.OrderedPairs
-
Get the number of points.
- size() - Method in class com.numericalmethod.suanshu.analysis.function.tuple.PartialFunction
-
- size() - Method in class com.numericalmethod.suanshu.analysis.function.tuple.SortedOrderedPairs
-
- size() - Method in class com.numericalmethod.suanshu.interval.Intervals
-
Get the number of disjoint intervals.
- size() - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- size(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalArray
-
- size(int) - Method in interface com.numericalmethod.suanshu.misc.datastructure.MultiDimensionalCollection
-
Returns the size of the collection along the given dimension.
- size() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.Constraints
-
Get the number of constraints.
- size() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralConstraints
-
- size() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
- size() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
- size() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
- size() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Gets the number of constraints.
- size() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get T, the number of hidden states or observations.
- size() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.LinearKalmanFilter
-
Get T, the number of hidden states or observations.
- size - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
-
the size
- size() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
Get the number of rows in the panel.
- size() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timegrid.EvenlySpacedGrid
-
- size() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.timegrid.TimeGrid
-
Get the number of time points.
- size() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timegrid.UnitGrid
-
- size() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Get the length of the history.
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.DateTimeGenericTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- size() - Method in interface com.numericalmethod.suanshu.stats.timeseries.datastructure.TimeSeries
-
Get the length of the time series.
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- size() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
-
- sizeX() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
-
- sizeX() - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateGrid
-
Define the size of the grid along the x-axis.
- sizeX() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
- sizeY() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
-
- sizeY() - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateGrid
-
Define the size of the grid along the y-axis.
- sizeY() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
- Sk - Variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
This is the approximate inverse of the Hessian matrix.
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
Gets the skewness of this distribution.
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- skew() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Gets the skewness of this distribution.
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
Gets the skewness of this distribution.
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
- skew() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
-
- skew() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- skew() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- skew() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- skew() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- skew() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Deprecated.
- skew() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Deprecated.
- Skewness - Class in com.numericalmethod.suanshu.stats.descriptive.moment
-
Skewness is a measure of the asymmetry of the probability distribution.
- Skewness() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Construct an empty Skewness calculator.
- Skewness(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Construct a Skewness calculator,
initialized with a sample.
- Skewness(Skewness) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Copy constructor.
- SmallestSubscriptRule - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
Bland's smallest-subscript rule is for anti-cycling in choosing a pivot.
- SmallestSubscriptRule() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule
-
- SOCPDualProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem
-
This is the Dual Second Order Conic Programming problem.
- SOCPDualProblem(Vector, Matrix[], Vector[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Constructs a dual SOCP problem.
- SOCPDualProblem(SOCPDualProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Copy constructor.
- SOCPDualProblem.EqualityConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem
-
- SOCPGeneralConstraint - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem
-
This represents the SOCP general constraint of this form.
- SOCPGeneralConstraint(Matrix, Vector, Vector, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Constructs a SOCP general constraint.
- SOCPGeneralConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem
-
This represents a set of SOCP general constraints of this form.
- SOCPGeneralConstraints() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Constructs a set of SOCP general constraints.
- SOCPGeneralConstraints(List<SOCPGeneralConstraint>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Constructs a set of SOCP general constraints.
- SOCPGeneralConstraints(SOCPGeneralConstraint[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Constructs a set of SOCP general constraints.
- SOCPGeneralProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem
-
Many convex programming problems can be represented in the following form.
- SOCPGeneralProblem(SOCPGeneralProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
-
Copy constructor.
- SOCPGeneralProblem(Vector, SOCPGeneralConstraint[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
-
Construct a general Second Order Conic Programming problem.
- SOCPGeneralProblem(Vector, List<SOCPGeneralConstraint>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
-
Construct a general Second Order Conic Programming problem.
- SOCPMaximumLoan - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints
-
Transforms a maximum loan constraint into the compact SOCP form.
- SOCPMaximumLoan(Vector, Vector, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
Constructs a maximum loan constraint.
- SOCPMaximumLoan(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
Constructs a maximum loan constraint.
- SOCPNoTradingList1 - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints
-
Transforms a black list (not to trade a new position) constraint into the
compact SOCP form.
- SOCPNoTradingList1(Vector, Matrix, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
Constructs a black list constraint.
- SOCPNoTradingList1(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
Constructs a black list constraint.
- SOCPNoTradingList2 - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar
-
Transforms a black list (not to trade a new position) constraint into the
compact SOCP form.
- SOCPNoTradingList2(Vector, Matrix, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
Constructs a black list constraint.
- SOCPNoTradingList2(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
Constructs a black list constraint.
- SOCPPortfolioConstraint - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
An SOCP constraint for portfolio optimization, e.g., market impact, is
represented by a set of constraints in this form.
- SOCPPortfolioConstraint() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
- SOCPPortfolioConstraint.ConstraintViolationException - Exception in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Exception thrown when a constraint is violated.
- SOCPPortfolioConstraint.Variable - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
the variables involved in SOCPGeneralConstraints
- SOCPPortfolioObjectiveFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Constructs the objective function for portfolio optimization.
- SOCPPortfolioObjectiveFunction(Matrix, double[], SOCPRiskConstraint, SOCPPortfolioConstraint) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Constructs the objective function for an SOCP portfolio optimization (minimization) problem.
- SOCPPortfolioObjectiveFunction(Vector, double[], SOCPRiskConstraint, SOCPPortfolioConstraint) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Constructs the objective function for an SOCP portfolio optimization (minimization) problem.
- SOCPPortfolioObjectiveFunction(Matrix, double, SOCPRiskConstraint) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Constructs the objective function for an SOCP portfolio optimization (minimization) problem
without a market impact term.
- SOCPPortfolioObjectiveFunction(Vector, double, SOCPRiskConstraint) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Constructs the objective function for an SOCP portfolio optimization (minimization) problem
without a market impact term.
- SOCPPortfolioProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Constructs an SOCP problem for portfolio optimization.
- SOCPPortfolioProblem(SOCPPortfolioObjectiveFunction, SOCPPortfolioConstraint[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Constructs an SOCP problem for portfolio optimization.
- SOCPPortfolioProblem(SOCPPortfolioObjectiveFunction, Collection<SOCPPortfolioConstraint>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Constructs an SOCP problem for portfolio optimization.
- SOCPRiskConstraint - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
- SOCPRiskConstraint() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPRiskConstraint
-
- SOCPSectorExposure - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar
-
Transforms a sector exposure constraint into the compact SOCP form.
- SOCPSectorExposure(Vector, Vector[], Vector, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
Constructs a sector exposure constraint.
- SOCPSectorExposure(Vector, Vector[], Vector) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
Constructs a sector exposure constraint.
- SOCPSectorNeutrality - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints
-
Transforms a sector neutral constraint into the compact SOCP form.
- SOCPSectorNeutrality(Vector, Vector[], double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
Constructs a sector neutral constraint.
- SOCPSectorNeutrality(Vector, Vector[]) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
Constructs a sector neutral constraint.
- SOCPSelfFinancing - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints
-
Transforms a self financing constraint into the compact SOCP form.
- SOCPSelfFinancing(Vector, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
Constructs a zero value constraint.
- SOCPSelfFinancing(Vector) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
Constructs a zero value constraint.
- SOCPZeroValue - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints
-
Transforms a zero value constraint into the compact SOCP form.
- SOCPZeroValue(Vector, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPZeroValue
-
Constructs a zero value constraint.
- SOCPZeroValue(Vector) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.socp.constraints.SOCPZeroValue
-
Constructs a zero value constraint.
- solution() - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.BBNode
-
the solution to the sub-problem associated with this node
- Solution(PrimalDualPathFollowingMinimizer, SDPDualProblem, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
-
Solves the semi-definite programming problem using the Homogeneous Self-Dual
Path-Following algorithm.
- Solution(SDPDualProblem, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- Solution(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
- solution() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
- Solution(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
-
- Solution(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- Solution(UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- solve(UpperTriangularMatrix, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
Solve Ux = b.
- solve(UpperTriangularMatrix, Vector, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
- solve(UpperTriangularMatrix, Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
- solve(UpperTriangularMatrix, Matrix, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
- solve(LowerTriangularMatrix, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
Solve Lx = b.
- solve(LowerTriangularMatrix, Vector, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
- solve(LowerTriangularMatrix, Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
- solve(LowerTriangularMatrix, Matrix, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
- solve(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver
-
Get a particular solution for the linear system, Ax = b.
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LUSolver
-
Solve Ax = b.
- solve(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LUSolver
-
Solves AX = B.
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.OLSSolverByQR
-
In the ordinary least square sense, solve
Ax = y
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.OLSSolverBySVD
-
In the ordinary least square sense, solve
Ax = y
- solve(TridiagonalMatrix, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.ThomasAlgorithm
-
Solves a tridiagonal matrix equation.
- solve(LSProblem, IterationMonitor<Vector>) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver
-
Solves iteratively
Ax = b
until the solution converges, i.e., the norm of residual
(b - Ax) is less than or equal to the threshold.
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
- solve(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
-
Return the input vector x.
- solve(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
-
Return P-1x, where P is the diagonal matrix
of A.
- solve(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner
-
Solve Mv = x, where M is the preconditioner matrix.
- solve(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
-
Solve Mz = x using this SSOR preconditioner.
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
- solve(LSProblem) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
- solve(ODE1stOrder) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation.BurlischStoerExtrapolation
-
Perform the extrapolation.
- solve(ODE1stOrder) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation
-
- solve(ODE1stOrderWith2ndDerivative) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation
-
- solve(ODE1stOrder) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.AdamsBashforthMoulton
-
Solve an ODE using the Adams-Bashforth-Moulton method.
- solve(ODE1stOrder) - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.ODESolver
-
Solves the given ODE problem.
- solve(ODE1stOrder) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta
-
Solves a first order ODE.
- solve(ODE1stOrder) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
-
Solve the given ODE using Runge-Kutta-Fehlberg method.
- solve(PoissonEquation2D, int, int) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2.IterativeCentralDifference
-
Solve a Poisson's equation problem, with the given grid resolution parameters.
- solve(WaveEquation1D, int, int) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.ExplicitCentralDifference1D
-
Solve an one-dimensional wave equation, with the resolution parameters of the solution grid.
- solve(WaveEquation2D, int, int, int) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.ExplicitCentralDifference2D
-
Solve a two-dimensional wave equation, with the resolution parameters of the solution grid.
- solve(ConvectionDiffusionEquation1D, int, int) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D
-
Solves a 1 dimensional convection-diffusion equation.
- solve(HeatEquation1D, int, int) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D
-
Solves the given one-dimensional heat equation.
- solve(HeatEquation2D, int, int, int) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod
-
Solve the given two-dimensional heat equation problem, with the given numbers of points along
the three axes in the grid (time, x, and y).
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.CubicRoot
-
Solve \(ax^3 + bx^2 + cx + d = 0\).
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub.JenkinsTraubReal
-
Solve a polynomial equation.
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.LinearRoot
-
Solve ax + b = 0.
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRoot
-
Get the roots/zeros of a polynomial.
- solve(Polynomial) - Method in interface com.numericalmethod.suanshu.analysis.function.polynomial.root.PolyRootSolver
-
- solve(Polynomial, double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuadraticRoot
-
Solve \(ax^2 + bx + c = 0\).
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuadraticRoot
-
- solve(double, double, double, double, double) - Method in interface com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRoot.QuarticSolver
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRoot
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(double, double, double, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFerrari
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(double, double, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFerrari
-
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFerrari
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(double, double, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFormula
-
- solve(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticRootFormula
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(RealVectorFunction, Vector) - Method in class com.numericalmethod.suanshu.analysis.root.multivariate.NewtonSystemRoot
-
Searches for a root, x such that f(x) = 0.
- solve(RealScalarFunction[], Vector) - Method in class com.numericalmethod.suanshu.analysis.root.multivariate.NewtonSystemRoot
-
Searches for a root, x such that f(x) = 0.
- solve(UnivariateRealFunction, double, double, double...) - Method in class com.numericalmethod.suanshu.analysis.root.univariate.BisectionRoot
-
- solve(UnivariateRealFunction, double, double, double...) - Method in class com.numericalmethod.suanshu.analysis.root.univariate.BrentRoot
-
- solve(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.root.univariate.BrentRoot
-
- solve(UnivariateRealFunction, double, double, double...) - Method in class com.numericalmethod.suanshu.analysis.root.univariate.HalleyRoot
-
- solve(UnivariateRealFunction, double) - Method in class com.numericalmethod.suanshu.analysis.root.univariate.HalleyRoot
-
- solve(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, double) - Method in class com.numericalmethod.suanshu.analysis.root.univariate.HalleyRoot
-
Search for a root, x, in the interval [lower, upper] such that f(x) = 0.
- solve(UnivariateRealFunction, double, double, double...) - Method in class com.numericalmethod.suanshu.analysis.root.univariate.NewtonRoot
-
- solve(UnivariateRealFunction, double) - Method in class com.numericalmethod.suanshu.analysis.root.univariate.NewtonRoot
-
- solve(UnivariateRealFunction, UnivariateRealFunction, double) - Method in class com.numericalmethod.suanshu.analysis.root.univariate.NewtonRoot
-
Searches for a root, x, in the interval [lower, upper] such that f(x) =
0.
- solve(UnivariateRealFunction, double, double, double...) - Method in interface com.numericalmethod.suanshu.analysis.root.univariate.Uniroot
-
Search for a root, x, in the interval [lower, upper] such that f(x) = 0.
- solve(Ceta) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
- solve(Ceta) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
-
- solve(Ceta) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
- solve() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011Solver
-
Solves the HJB equations (a nonlinear PDE system) to get the optimal
stopping regions, i.e., the buying region (buy threshold) and the selling
region (sell threshold).
- solve(SDPDualProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
-
- solve(SDPDualProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
-
- solve(SDPDualProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
-
- solve(SOCPDualProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer
-
- solve(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
- solve(LPCanonicalProblem1) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
- solve(SimplexTable) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPSimplexSolver
-
Solve an LP problem by a simplex algorithm on a simplex table
- solve(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
- solve(LPProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
- solve(QuadraticFunction, double) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
-
Solves an unconstrained quadratic programming problem of this form.
- solve(QuadraticFunction) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
-
Solves an unconstrained quadratic programming problem of this form.
- solve(QuadraticFunction, LinearEqualityConstraints, double) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
-
Solves a quadratic programming problem subject to equality constraints.
- solve(QuadraticFunction, LinearEqualityConstraints) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
-
Solves a quadratic programming problem subject to equality constraints.
- solve(QPProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer
-
- solve(QPProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer
-
- solve(QPProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer
-
- solve(BoxOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
-
- solve(ConstrainedOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
- solve(ConstrainedOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
-
- solve(RealScalarFunction, EqualityConstraints) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
-
Minimize a function subject to only equality constraints.
- solve(ConstrainedOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
- solve(RealScalarFunction, RealVectorFunction, GreaterThanConstraints) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
-
Minimize a function subject to only inequality constraints.
- solve(RealScalarFunction, GreaterThanConstraints) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
-
Minimize a function subject to only inequality constraints.
- solve(ConstrainedOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
-
- solve(BruteForceIPProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
-
- solve(ILPProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer
-
- solve(ILPProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer
-
- solve(ConstrainedOptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer
-
- solve(ConstrainedOptimSubProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer
-
- solve(ConstrainedOptimProblem, Map<Integer, Double>) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer
-
Solves a constrained sub-problem by specifying the fixing explicitly.
- solve(OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
-
- solve(OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- solve(MinMaxProblem<T>) - Method in class com.numericalmethod.suanshu.optimization.multivariate.minmax.LeastPth
-
- solve(OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
-
- solve(Function<Vector, R>) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.BruteForceMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.ConjugateGradientMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.FletcherReevesMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.IterativeC2Maximizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch.FletcherLineSearch
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.NelderMeadMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.HuangMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent
-
- solve(RealVectorFunction, RntoMatrix) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
-
Solve the minimization problem to minimize F = vf' * vf.
- solve(RealVectorFunction) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
-
Solve the minimization problem to minimize F = vf' * vf.
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
Solve a minimization problem with a C2 objective function.
- solve(OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.DoubleBruteForceMinimizer
-
- solve(P) - Method in interface com.numericalmethod.suanshu.optimization.Optimizer
-
- solve(UnivariateRealFunction) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer
-
Minimize a univariate function.
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BrentMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.FibonaccMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.GoldenMinimizer
-
- solve(C2OptimProblem) - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer
-
- solve(UnivariateRealFunction) - Method in class com.numericalmethod.suanshu.optimization.univariate.GridSearchMinimizer
-
Minimizes a univariate function.
- SomeIntegers(IPProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers
-
Construct the integral constraint from an Integer Programming problem.
- SORSweep - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
This is a building block for
SOR and
SSOR
to perform the forward or backward sweep.
- SORSweep(Matrix, Vector, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
-
Construct an instance to perform forward or backward sweep for a linear
system Ax = b.
- SortableArray - Class in com.numericalmethod.suanshu.misc.datastructure
-
These arrays can be sorted according to the dictionary order.
- SortableArray(int) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.SortableArray
-
- SortableArray(int...) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.SortableArray
-
- SortableArray(List<Integer>) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.SortableArray
-
- SortedOrderedPairs - Class in com.numericalmethod.suanshu.analysis.function.tuple
-
The ordered pairs are first sorted by abscissa, then by ordinate.
- SortedOrderedPairs(OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.SortedOrderedPairs
-
- SortedOrderedPairs(double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.SortedOrderedPairs
-
- sortInColumnOrder(List<SparseMatrix.Entry>, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Sorts a list of sparse matrix entries in column order (row indices in the same row can be in
arbitrary order) in linear time.
- sortInColumnOrder(SparseMatrix.Entry[], int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Sorts an array of sparse matrix entries in column order (row indices in the same row can be
in arbitrary order) in linear time.
- sortInRowColumnOrder(List<SparseMatrix.Entry>, int, int, boolean, boolean) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Sorts a list of sparse matrix entries in row-column order in linear time.
- sortInRowColumnOrder(SparseMatrix.Entry[], int, int, boolean, boolean) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Sorts an array of sparse matrix entries in row-column order in linear time.
- sortInRowOrder(List<SparseMatrix.Entry>, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Sorts a list of sparse matrix entries in row order (column indices in the same row can be in
arbitrary order) in linear time.
- sortInRowOrder(SparseMatrix.Entry[], int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Sorts an array of sparse matrix entries in row order (column indices in the same row can be
in arbitrary order) in linear time.
- SparseDAGraph<V,E extends Arc<V>> - Class in com.numericalmethod.suanshu.graph.type
-
This class implements the sparse directed acyclic graph representation.
- SparseDAGraph(boolean) - Constructor for class com.numericalmethod.suanshu.graph.type.SparseDAGraph
-
Construct an empty directed acyclic graph.
- SparseDAGraph() - Constructor for class com.numericalmethod.suanshu.graph.type.SparseDAGraph
-
Construct an empty directed acyclic graph.
- SparseDAGraph(DAGraph<V, E>) - Constructor for class com.numericalmethod.suanshu.graph.type.SparseDAGraph
-
(Copy) construct a directed acyclic graph from another directed acyclic graph.
- SparseDiGraph<V,E extends Arc<V>> - Class in com.numericalmethod.suanshu.graph.type
-
This class implements the sparse directed graph representation.
- SparseDiGraph() - Constructor for class com.numericalmethod.suanshu.graph.type.SparseDiGraph
-
Construct an empty graph.
- SparseDiGraph(DiGraph<V, E>) - Constructor for class com.numericalmethod.suanshu.graph.type.SparseDiGraph
-
(Copy) construct a graph from another graph.
- SparseGraph<V,E extends HyperEdge<V>> - Class in com.numericalmethod.suanshu.graph.type
-
This class implements the sparse graph representation.
- SparseGraph() - Constructor for class com.numericalmethod.suanshu.graph.type.SparseGraph
-
Construct an empty graph.
- SparseGraph(Graph<V, E>) - Constructor for class com.numericalmethod.suanshu.graph.type.SparseGraph
-
(Copy) construct a graph from another graph.
- SparseMatrix - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
A sparse matrix stores only non-zero values.
- SparseMatrix.Entry - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
This is a (non-zero) entry in a sparse matrix.
- SparseMatrix.Entry.TopLeftFirstComparator - Enum in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
This Comparator sorts the matrix coordinates first from top to
bottom (rows), and then from left to right (columns).
- SparseMatrix.ValueArray - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
- SparseMatrixUtils - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
- SparseStructure - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
This interface defines common operations on sparse structures such as sparse
vector or sparse matrix.
- SparseTree<V> - Class in com.numericalmethod.suanshu.graph.type
-
This class implements the sparse tree representation.
- SparseTree(V) - Constructor for class com.numericalmethod.suanshu.graph.type.SparseTree
-
Construct a tree with a root.
- SparseUnDiGraph<V,E extends UndirectedEdge<V>> - Class in com.numericalmethod.suanshu.graph.type
-
This class implements the sparse undirected graph representation.
- SparseUnDiGraph() - Constructor for class com.numericalmethod.suanshu.graph.type.SparseUnDiGraph
-
Construct an empty graph.
- SparseUnDiGraph(UnDiGraph<V, E>) - Constructor for class com.numericalmethod.suanshu.graph.type.SparseUnDiGraph
-
(Copy) construct a graph from another graph.
- SparseVector - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
A sparse vector stores only non-zero values.
- SparseVector(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector.
- SparseVector(int, int[], double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector.
- SparseVector(int, List<SparseVector.Entry>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector.
- SparseVector(double...) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector from a double[].
- SparseVector(Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector from a vector.
- SparseVector(SparseVector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Copy constructor.
- SparseVector.Entry - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
- SparseVector.Entry.Comparator - Enum in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
This Comparator sorts the matrix coordinates first from top
to bottom (rows), and then from left to right (columns).
- SparseVector.Iterator - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
-
This wrapper class overrides the
Iterator.remove()
method to throw an exception when called.
- SpearmanRankCorrelation - Class in com.numericalmethod.suanshu.stats.descriptive.correlation
-
Spearman's rank correlation coefficient or Spearman's rho is a non-parametric measure of
statistical dependence between two variables.
- SpearmanRankCorrelation(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.correlation.SpearmanRankCorrelation
-
Construct a Spearman rank calculator initialized with two samples.
- spectralDensity(double) - Method in interface com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVD
-
The density \(h\) of the spectral measure \(H\) on the interval (0,1).
- spectralDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- spectralDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- spectralDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- spectralDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic
-
- spectralDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn
-
- spectralDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- spectralDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic
-
- spectralDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- spectralDensity(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- Spectrum - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
-
A spectrum is the set of eigenvalues of a matrix.
- spread - Variable in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- spread() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
S = A - bB
- SQPActiveSetMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset
-
Sequential quadratic programming (SQP) is an iterative method for nonlinear
optimization.
- SQPActiveSetMinimizer(SQPActiveSetMinimizer.VariationFactory, double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
Construct an SQP Active Set minimizer to solve general minimization
problems with inequality constraints.
- SQPActiveSetMinimizer(double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
Construct an SQP Active Set minimizer to solve general minimization
problems with inequality constraints.
- SQPActiveSetMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
Construct an SQP Active Set minimizer to solve general minimization
problems with inequality constraints.
- SQPActiveSetMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset
-
This is the solution to a general minimization with only inequality
constraints using the SQP Active Set algorithm.
- SQPActiveSetMinimizer.VariationFactory - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset
-
This factory constructs a new instance of SQPASVariation for each
SQP problem.
- SQPActiveSetOnlyEqualityConstraint1Minimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint
-
This implementation is a modified version of Algorithm 15.1 in the reference to solve a general constrained optimization problem with only equality constraints.
- SQPActiveSetOnlyEqualityConstraint1Minimizer(SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
-
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
- SQPActiveSetOnlyEqualityConstraint1Minimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
-
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
- SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint
-
This factory constructs a new instance of SQPASEVariation for each SQP problem.
- SQPActiveSetOnlyEqualityConstraint2Minimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint
-
- SQPActiveSetOnlyEqualityConstraint2Minimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint2Minimizer
-
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
- SQPActiveSetOnlyEqualityConstraint2Minimizer(double, double, int, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint2Minimizer
-
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
- SQPActiveSetOnlyInequalityConstraintMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset
-
This implementation is a modified version of Algorithm 15.2 in the reference
to solve a general constrained optimization problem with only inequality
constraints.
- SQPActiveSetOnlyInequalityConstraintMinimizer(SQPActiveSetMinimizer.VariationFactory, double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
-
Construct an SQP Active Set minimizer to solve general minimization
problems with only inequality constraints.
- SQPActiveSetOnlyInequalityConstraintMinimizer(double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
-
Construct an SQP Active Set minimizer to solve general minimization
problems with only inequality constraints.
- SQPActiveSetOnlyInequalityConstraintMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
-
Construct an SQP Active Set minimizer to solve general minimization
problems with only inequality constraints.
- SQPActiveSetOnlyInequalityConstraintMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset
-
This is the solution to a general minimization problem with only
inequality constraints using the SQP Active Set algorithm.
- SQPASEVariation - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint
-
This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem with only equality constraints
using Sequential Quadratic Programming.
- SQPASEVariation1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint
-
This implementation is a modified version of the algorithm in the reference to solve a general constrained minimization problem
using Sequential Quadratic Programming.
- SQPASEVariation1(double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Construct a variation.
- SQPASEVariation1() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Construct a variation.
- SQPASEVariation2 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint
-
This implementation tries to find an exact positive definite Hessian whenever possible.
- SQPASEVariation2(double, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
-
Construct a variation.
- SQPASEVariation2() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
-
Construct a variation.
- SQPASVariation - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset
-
This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem
using Sequential Quadratic Programming.
- SQPASVariation1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset
-
This implementation is a modified version of Algorithm 15.4 in the reference
to solve a general constrained minimization problem using Sequential
Quadratic Programming.
- SQPASVariation1(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPASVariation1
-
Construct a variation.
- sqrt(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the square roots of a vector, element-by-element.
- sqrt(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Square root of a complex number.
- sqrt(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the square roots of values.
- squared(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the squares of a vector, element-by-element.
- squareQ() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
Get the square Q matrix.
- squareQ() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
- squareQ() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QR
-
- squareQ() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get the square Q matrix.
- SSORPreconditioner - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
SSOR preconditioner is derived from a symmetric coefficient matrix A
which is decomposed as
A = D + L + Lt
The SSOR preconditioning matrix is defined as
M = (D + L)D-1(D + L)t
or, parameterized by ω
M(ω) = (1/(2 - ω))(D / ω + L)(D / ω)-1(D / ω + L)t
- SSORPreconditioner(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
-
Construct an SSOR preconditioner with a symmetric coefficient matrix.
- StandardCumulativeNormal - Interface in com.numericalmethod.suanshu.analysis.function.special.gaussian
-
The cumulative Normal distribution function describes the probability of a Normal random variable falling in the interval \((-\infty, x]\).
- standardDeviation() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Get the standard deviation of the sample,
which is the square root of the variance.
- standardError() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Get the standard errors of the fitted parameters.
- StandardInterval - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
This transformation is for mapping integral region from [a, b] to [-1, 1].
- StandardInterval(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval
-
Construct a StandardInterval substitution rule.
- standardized() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
standard residual = residual / v1 / sqrt(RSS / (n-m))
- StandardNormalRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.normal
-
An alias for
Zignor2005 to provide a default implementation for sampling from the
standard Normal distribution.
- StandardNormalRNG() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.StandardNormalRNG
-
- standarizedInnovations() - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.ResamplerModel
-
Gets the standarized innovations (normalized by the conditional standard
deviation at the time) of the time series.
- standarizedInnovations() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleAR1Fit
-
- standarizedInnovations() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.fit.SimpleGARCHFit
-
- state() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.KagiModel
-
UP trend is -ve; DOWN trend is +ve.
- state - Variable in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLMSim.Innovation
-
the simulated state
- state - Variable in class com.numericalmethod.suanshu.stats.dlm.univariate.DLMSim.Innovation
-
the simulated state
- state() - Method in class com.numericalmethod.suanshu.stats.hmm.HmmInnovation
-
Get the hidden state.
- state() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Gets the current system configuration.
- StateEquation - Class in com.numericalmethod.suanshu.stats.dlm.univariate
-
This is the state equation in a controlled dynamic linear model.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a state equation.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a state equation.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a state equation without control variables.
- StateEquation(double, double, double, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a time-invariant state equation.
- StateEquation(double, double) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Construct a time-invariant state equation without control variables.
- StateEquation(StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation
-
Copy constructor.
- Statistic - Interface in com.numericalmethod.suanshu.stats.descriptive
-
A statistic (singular) is a single measure of some attribute of a sample (e.g., its arithmetic mean value).
- StatisticFactory - Interface in com.numericalmethod.suanshu.stats.descriptive
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.factor.factoranalysis.FAEstimator
-
Get the test statistics of the factor analysis.
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarling
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.distribution.CramerVonMises2Samples
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov1Sample
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov2Samples
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest
-
Get the test statistics.
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.rank.KruskalWallis
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.BreuschPagan
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.variance.Bartlett
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.variance.F
-
- statistics() - Method in class com.numericalmethod.suanshu.stats.test.variance.Levene
-
- statisticsAlternative() - Method in class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarling
-
Gets the alternative Anderson-Darling statistic (adjusted for ties).
- stderr() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMBeta
-
Gets the standard errors of the coefficients β^.
- stderr() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the standard error of the residuals.
- stderr() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Get the asymptotic standard errors of the estimators.
- stderr() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the asymptotic standard errors of the estimated parameters,
φ and θ.
- stdev(double) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
Gets the stdev of the last % portion in the spread.
- stdev() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
Gets the stdev of the spread.
- stdev() - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.CorrelationMatrix
-
Gets the standard deviations of the elements.
- stdev(int) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.CorrelationMatrix
-
Gets the standard deviation of the i-th element.
- stdev() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedVariance
-
- stdinno - Variable in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- SteepestDescentImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- SteepestDescentMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
-
A steepest descent algorithm finds the minimum by moving along the negative of the steepest
gradient direction.
- SteepestDescentMinimizer(LineSearch, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
Construct a multivariate minimizer using a steepest descent method.
- SteepestDescentMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
Construct a multivariate minimizer using a steepest descent method.
- SteepestDescentMinimizer.SteepestDescentImpl - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent
-
This is an implementation of the steepest descent method.
- SteepestDescentSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Steepest Descent method (SDM) solves a symmetric n-by-n linear system.
- SteepestDescentSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
Construct a Steepest Descent method (SDM) solver.
- SteepestDescentSolver(int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
Construct a Steepest Descent method (SDM) solver.
- STEFAN_BOLTZMANN_SIGMA - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
-
The Stegan-Boltzmann constant \(\sigma\) in (W m-2 K-4).
- step() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta1
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta10
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta2
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta3
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta4
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta5
- step(DerivativeFunction, Vector, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta6
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta7
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta8
-
- step(DerivativeFunction, Vector, double, double, double) - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaStepper
-
- step() - Method in class com.numericalmethod.suanshu.misc.algorithm.bb.BranchAndBound
-
- step() - Method in interface com.numericalmethod.suanshu.misc.algorithm.iterative.IterativeMethod
-
Do the next iteration.
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
Do the next iteration.
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.GeneticAlgorithm
-
Run a step in genetic algorithm: produce the next generation of chromosome pool.
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
- step() - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- step() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Performs one step of the leap-frogging algorithm.
- StepFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
-
A step function (or staircase function) is a finite linear combination of indicator functions of
intervals.
- StepFunction(double) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Construct an empty step function.
- StepFunction(OrderedPairs, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Construct a step function from a collection ordered pairs.
- StepFunction(OrderedPairs) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
-
Construct a step function from a collection ordered pairs.
- steps(int) - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Performs n steps of the leap-frogging algorithm.
- StopCondition - Interface in com.numericalmethod.suanshu.misc.algorithm.stopcondition
-
Defines when an algorithm stops (the iterations).
- StringUtils - Class in com.numericalmethod.suanshu.misc
-
Utility methods for string manipulation.
- studentized() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
studentized residual = standardized * sqrt((n-m-1) / (n-m-standardized^2))
- SturmCount - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Computes the Sturm count, the number of negative pivots encountered while factoring tridiagonal
T - σ I = LDLT.
- SturmCount(LDDecomposition, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SturmCount
-
Creates an instance for computing the Sturm count of a given robust representation
(T - σ I = LDLT).
- subA() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection
-
Constructs a covariates subset.
- subarray(double[], int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get a sub-array of the original array with the given indices.
- subarray(int[], int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Get a sub-array of the original array with the given indices.
- subDiagonal(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the sub-diagonal of a matrix as a vector.
- subDiagonal(SparseMatrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the sub-diagonal of a sparse matrix as a sparse vector.
- SubFunction<R> - Class in com.numericalmethod.suanshu.analysis.function
-
A sub-function, g, is defined over a subset of the domain of another
(original) function,
f.
- SubFunction(Function<Vector, R>, Map<Integer, Double>) - Constructor for class com.numericalmethod.suanshu.analysis.function.SubFunction
-
Constructs a sub-function.
- subject() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData.Row
-
Gets the subject of the row.
- subMatrix(Matrix, int, int, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the four corners of a matrix.
- subMatrix(SparseMatrix, int, int, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the four corners of a sparse matrix.
- subMatrix(Matrix, int[], int[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the intersections of rows and columns of a matrix.
- subMatrix(SparseMatrix, int[], int[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the intersections of rows and columns of a sparse matrix.
- SubMatrixBlock - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
Sub-matrix block representation for block algorithm.
- SubMatrixRef - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
This is a 'reference' to a sub-matrix of a larger matrix without copying it.
- SubMatrixRef(Matrix, int[], int[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
Constructs a sub-matrix reference.
- SubMatrixRef(Matrix, int, int, int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
Constructs a sub-matrix reference.
- SubMatrixRef(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
Constructs a reference to the whole matrix.
- SubProblemMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained
-
This minimizer solves a constrained optimization sub-problem where the values
for some variables are held fixed for the original optimization problem.
- SubProblemMinimizer(SubProblemMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem, IterativeSolution<Vector>>>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer
-
- SubProblemMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer
-
- SubProblemMinimizer(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.SubProblemMinimizer
-
- SubProblemMinimizer.ConstrainedMinimizerFactory<U extends ConstrainedMinimizer<ConstrainedOptimProblem,IterativeSolution<Vector>>> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained
-
This factory constructs a new instance of
ConstrainedMinimizer to solve a real valued minimization
problem.
- SubProblemMinimizer.IterativeSolution<Vector> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained
-
- SubstitutionRule - Interface in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
-
A substitution rule specifies \(x(t)\) and \(\frac{\mathrm{d} x}{\mathrm{d} t}\).
- subTree(V) - Method in interface com.numericalmethod.suanshu.graph.RootedTree
-
Gets a sub-tree starting from a vertex.
- subTree(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- subTree(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- subVector(Vector, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets a sub-vector from a vector.
- subVector(SparseVector, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets a sub-vector from a sparse vector.
- subVector(Vector, int[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets a sub-vector from a vector according to a given array
of ordered indices (repetition allowed).
- SubVectorRef - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles
-
Represents a sub-vector backed by the referenced vector, without data
copying.
- SubVectorRef(Vector, int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- SuccessiveOverrelaxationSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
The Successive Overrelaxation method (SOR), is devised by applying
extrapolation to the
Gauss-Seidel method.
- SuccessiveOverrelaxationSolver(double, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
Construct a SOR solver with the extrapolation factor ω.
- sum(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the sum of all elements in the given matrix.
- sum(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the sum of all vector elements.
- sum(int, int) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to to with the increment 1.
- sum(int, int, int) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to to with the increment inc.
- sum(double, double, double) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to to with the increment inc.
- sum(double[]) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Partial summation of the selected terms.
- sum(BigDecimal...) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Sum up the BigDecimal numbers.
- sum(double...) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
-
Sum up big numbers.
- sum(double...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the sum of the values.
- sum(int...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the sum of the values.
- sum2(double...) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
-
Get the sum of squares of the values.
- sum_BtDt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.F_Sum_BtDt
-
Get Σ(Bt)*(dt).
- sum_BtDt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.F_Sum_tBtDt
-
Get Σ(Bt)*(dt).
- sum_tBtDt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.F_Sum_tBtDt
-
Get Σ(t-0.5)*(Bt)*(dt).
- Summation - Class in com.numericalmethod.suanshu.analysis.sequence
-
Summation is the operation of adding a sequence of numbers; the result is their sum or total.
- Summation(Summation.Term, double) - Constructor for class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Construct a summation series.
- Summation(Summation.Term) - Constructor for class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Construct a finite summation series.
- Summation.Term - Interface in com.numericalmethod.suanshu.analysis.sequence
-
Define the terms in a summation series.
- SumOfPenalties - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the costs from a set of constituent penalty functions.
- SumOfPenalties(PenaltyFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.SumOfPenalties
-
Construct a sum-of-penalties penalty function from a set of penalty functions.
- SumOfPoweredWeights - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification
-
Defines portfolio diversification as
\[
D(w) = \sum_i w_i^P
\]
- SumOfPoweredWeights(double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification.SumOfPoweredWeights
-
- SumOfSquaredWeights - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification
-
Defines portfolio diversification as
\[
D(w) = \sum_i w_i^2
\]
- SumOfSquaredWeights() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification.SumOfSquaredWeights
-
- sumOfWeights() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedMean
-
- SumOfWLogW - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification
-
Defines portfolio diversification as
\[
D(w) = \sum_i w_i \ln(w_i)
\]
- SumOfWLogW() - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification.SumOfWLogW
-
- sumsOfPowersOfDifferences(int, double, double...) - Static method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
Compute the power-th moment of an array of data with respect to a mean.
- sumToInfinity(int) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to infinity with increment 1 until the series converges.
- sumToInfinity(double, double) - Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
-
Sum up the terms from from to infinity with increment inc until the series
converges.
- sumUpLastRows(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA
-
Sums up, for each column, the last nRows rows.
- superDiagonal(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the super-diagonal of a matrix as a vector.
- superDiagonal(SparseMatrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the super-diagonal of a sparse matrix as a sparse vector.
- supportsInterval(double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
- supportsInterval(double, double) - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
-
Return whether the given interval (a,b) is supported by this rule.
- supportsInterval(double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
-
- supportsInterval(double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
- supportsInterval(double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
- SVD - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd
-
SVD decomposition decomposes a matrix A of dimension m x n, where m >= n,
such that
U' * A * V = D, or U * D * V' = A.
- SVD(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD
-
Runs the SVD decomposition on a matrix.
- SVD(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD
-
Runs the SVD decomposition on a matrix.
- SVD(Matrix, boolean, double, SVD.Method) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD
-
Runs the SVD decomposition on a matrix.
- svd() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbySVD
-
Gets the Singular Value Decomposition (SVD) of matrix X.
- SVD.Method - Enum in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd
-
- SVDbyMR3 - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3
-
Given a matrix A, computes its singular value decomposition (SVD), using
"Algorithm of Multiple Relatively Robust Representations" (MRRR).
- SVDbyMR3(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
-
Creates a singular value decomposition for a matrix A.
- SVDDecomposition - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd
-
SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that
U' * A * V = D, or U * D * V' = A.
- SVEC - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
SVEC converts a symmetric matrix K = {Kij} into a vector of dimension n(n+1)/2.
- SVEC(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SVEC
-
Construct the SVEC of a matrix.
- svecA() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- svecA() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
-
Computes A^ in "Toh, Todd, Tütüncü, Section 3.1".
- svecA() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- swap(FlexibleTable, int, int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange
-
Constructs a new table by exchanging the r-th row with the s-th column in
A using Jordan Exchange.
- swap(MatrixTable, int, int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange
-
Constructs a new table by exchanging the r-th row with the s-th column in
A using Jordan Exchange.
- swap(int, int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Perform a Jordan Exchange to swap row r with column s.
- swapColumn(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Swaps two columns of a permutation matrix.
- swapColumn(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Swap columns:
- swapInPlace(FlexibleTable, int, int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange
-
- swapRow(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Swaps two rows of a permutation matrix.
- swapRow(int, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Swap rows:
- symbol1 - Variable in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- symbol1() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- symbol2 - Variable in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- symbol2() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- SymmetricEigenByMR3 - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Computes eigen decomposition for a symmetric matrix using "Algorithm of Multiple Relatively
Robust Representations" (MRRR).
- SymmetricEigenByMR3(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
-
Creates an instance for computing the eigen decomposition for a given symmetric matrix
A.
- SymmetricEigenByMR3(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
-
Creates an instance for computing the eigen decomposition for a given symmetric matrix
A.
- SymmetricEigenFor2x2Matrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Computes the eigen decomposition of a 2-by-2 symmetric matrix in the following form by symmetric
QR algorithm.
- SymmetricEigenFor2x2Matrix(double, double, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix
-
- SymmetricKronecker - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
Compute the symmetric Kronecker product of two matrices.
- SymmetricKronecker(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SymmetricKronecker
-
Compute the symmetric Kronecker product of two matrices.
- SymmetricMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle
-
A symmetric matrix is a square matrix such that its transpose equals to itself, i.e.,
A[i][j] = A[j][i]
- SymmetricMatrix(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Construct a symmetric matrix of dimension dim * dim.
- SymmetricMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Construct a symmetric matrix from a 2D double[][] array.
- SymmetricMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Cast an (almost) symmetric matrix into SymmetricMatrix by averaging A(i,j) and
A(j,i).
- SymmetricMatrix(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Cast an (almost) symmetric matrix into SymmetricMatrix.
- SymmetricMatrix(SymmetricMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Copy constructor.
- SymmetricQRAlgorithm - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
-
The symmetric QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form
of a square, symmetric matrix.
- SymmetricQRAlgorithm(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Runs the QR algorithm on a symmetric matrix.
- SymmetricQRAlgorithm(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Runs the QR algorithm on a symmetric matrix.
- SymmetricQRAlgorithm(Matrix, double, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Runs the QR algorithm on a symmetric matrix.
- SymmetricSuccessiveOverrelaxationSolver - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
The Symmetric Successive Overrelaxation method (SSOR) is like
SOR, but it performs in each
iteration one forward sweep followed by one backward sweep.
- SymmetricSuccessiveOverrelaxationSolver(double, int, Tolerance) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
Construct a SSOR solver with the extrapolation factor ω.
- SymmetricSVD - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd
-
This algorithm calculates the Singular Value Decomposition (SVD) of a square, symmetric
matrix A using QR algorithm.
- SymmetricSVD(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Calculates the SVD of A.
- SymmetricSVD(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Calculates the SVD of A.
- SymmetricTridiagonalDecomposition - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization
-
Given a square, symmetric matrix A, we find Q
such that Q' * A * Q = T , where T is a tridiagonal matrix.
- SymmetricTridiagonalDecomposition(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition
-
Runs the tridiagonal decomposition for a square, symmetric
matrix.
- SYNC_RNORM - Static variable in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
- SYNC_UNIFORM - Static variable in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
- synchronizedRLG(RandomLongGenerator) - Static method in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
- synchronizedRNG(RandomNumberGenerator) - Static method in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
- synchronizedRVG(RandomVectorGenerator) - Static method in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils
-
- SynchronizedStatistic - Class in com.numericalmethod.suanshu.stats.descriptive
-
This is a thread-safe wrapper of
Statistic by synchronizing all public methods
so that only one thread at a time can access the instance.
- T() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition
-
Gets the symmetric tridiagonal T matrix.
- T() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization
-
Gets T, such that T = Q * A * Q.
- T() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
- T() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the transformation matrix, T, such that T * A = U.
- T() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Get the transformation matrix, T, such that T * A = U.
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- T() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the transformation matrix, T, such that T * A = U.
- t() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixRing
-
Get the transpose of this matrix.
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
The transpose of a diagonal matrix is the same as itself.
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
t(A)
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
The transpose of a symmetric matrix is the same as itself.
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
The transpose of a permutation matric is the same as its inverse.
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- T() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Get the transformed matrix T.
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- t() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- T() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
-
Gets the time period of interest, that is, the range of t,
(0 < t < T).
- T() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
-
Get the time period of interest, that is, the range of t,
(0 < t < T).
- T() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the time period of interest, that is, the range of t,
(0 < t < T).
- T() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
-
Gets the time period of interest, that is, the range of t,
(0 < t < T).
- T() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
-
Gets the time period of interest, that is, the range of t,
(0 < t < T).
- t(int) - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
-
Gets the value on the time axis at index k.
- t(int) - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the value on the time-axis at index k.
- T() - Method in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA.Signal
-
- T - Variable in class com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010PCA
-
- t() - Method in class com.numericalmethod.suanshu.stats.covariance.covarianceselection.CovarianceSelectionProblem
-
Gets the penalization parameter t for L1 regularization.
- t - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
distinct population eigenvalues
- t() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Rank
-
/[ t = \sum(t_i^3 - t_i) \]
- t() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSOProblem
-
Get the penalization parameter for the constrained form of LASSO.
- t() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMBeta
-
Gets the t- or z- value of the regression coefficients β^.
- t() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFtWt
-
Get the current time.
- T() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timegrid.EvenlySpacedGrid
-
Get the end time.
- t() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
-
Get the current time.
- T - Class in com.numericalmethod.suanshu.stats.test.mean
-
Student's t-test tests for the equality of means,
for the one-sample case, against a hypothetical mean,
and for two-sample case, of two populations.
- T(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.mean.T
-
Construct a one-sample location test of whether the mean of a normally distributed population
has a value specified in a null hypothesis.
- T(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.mean.T
-
Construct Welch's t-test, an adaptation of Student's t-test, for the use with two samples
having possibly unequal variances.
- T(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.mean.T
-
Construct Welch's t-test, an adaptation of Student's t-test, for the use with two samples
having possibly unequal variances.
- T(double[], double[], boolean, double) - Constructor for class com.numericalmethod.suanshu.stats.test.mean.T
-
Construct a two sample location test of the null hypothesis that the means of two normally
distributed populations are equal.
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
Get the lower limit of the integral.
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential
-
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
- ta() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval
-
- ta() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.SubstitutionRule
-
Get the lower limit of the integral.
- Table - Interface in com.numericalmethod.suanshu.misc.datastructure
-
A table is a means of arranging data in rows and columns.
- table - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- tail() - Method in interface com.numericalmethod.suanshu.graph.Arc
-
Get the tail of this arc.
- tail() - Method in class com.numericalmethod.suanshu.graph.type.SimpleArc
-
- tallR() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
- tallR() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
- tallR() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QR
-
- tallR() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get the tall R matrix.
- tan(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Tangent of a complex number.
- tangentAt(SortedOrderedPairs, int) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicHermite.Tangent
-
Compute the tangent at the given index k, from the given collection of points.
- tanh(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
-
Hyperbolic tangent of a complex number.
- tau() - Method in class com.numericalmethod.suanshu.stats.covariance.nlshrink.LedoitWolf2016.Result
-
Gets estimated population eigenvalues in ascending order.
- tau - Variable in class com.numericalmethod.suanshu.stats.covariance.nlshrink.quest.QuEST.Result
-
population eigenvalues in ascending order
- TauEstimator - Class in com.numericalmethod.suanshu.stats.covariance.nlshrink
-
Non-linear shrinkage estimator of population eigenvalues.
- TauEstimator(int, int, Vector, int) - Constructor for class com.numericalmethod.suanshu.stats.covariance.nlshrink.TauEstimator
-
- TauEstimator(int, int, Vector) - Constructor for class com.numericalmethod.suanshu.stats.covariance.nlshrink.TauEstimator
-
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
Get the upper limit of the integral.
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential
-
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
- tb() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval
-
- tb() - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.SubstitutionRule
-
Get the upper limit of the integral.
- TDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The Student t distribution is the probability distribution of t, where
\[
t = \frac{\bar{x} - \mu}{s / \sqrt N}
\]
\(\bar{x}\) is the sample mean;
μ is the population mean;
s is the square root of the sample variance;
N is the sample size;
The importance of the Student's distribution is
when (as in nearly all practical statistical work) the population standard deviation is unknown and has to be estimated from the data.
- TDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
Construct a Student's t distribution.
- temperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.BoltzTemperatureFunction
-
- temperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.ExpTemperatureFunction
-
Matlab's default: @temperatureexp (default) - T = T0 * 0.95^k.
- temperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.FastTemperatureFunction
-
Matlab: @temperaturefast - The temperature is equal to InitialTemperature / k.
- temperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
-
Gets the temperature at time t.
- TemperatureFunction - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction
-
A temperature function defines a temperature schedule used in simulated annealing.
- TemperedAcceptanceProbabilityFunction - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
-
A tempered acceptance probability function computes the probability that the next state
transition will be accepted.
- test(double) - Method in interface com.numericalmethod.suanshu.number.DoubleUtils.ifelse
-
Decide whether x satisfies the boolean test.
- theta - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.GammaMixtureDistribution.Lambda
-
the scale parameter
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial
-
- theta(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMExponentialDistribution
-
The canonical parameter of the distribution in terms of the mean μ.
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma
-
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian
-
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian
-
- theta(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson
-
- theta() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
-
- theta() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou.OUProcess
-
Get the mean reversion rate.
- theta() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get all the MA coefficients.
- theta(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
-
Get the coefficients of the linear predictor.
- theta(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
-
Get the coefficients of the linear predictor.
- theta(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
-
Get the coefficients of the linear predictor.
- theta() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get all the MA coefficients.
- theta(int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
-
Gets the coefficients of the linear predictor.
- thetaPolynomial() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the polynomial (1 + θ).
- ThinRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
-
Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.
- ThinRNG(RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.ThinRNG
-
Constructs a thinned RNG.
- ThinRVG - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate
-
Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.
- ThinRVG(RandomVectorGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.ThinRVG
-
Constructs a thinned RVG.
- ThomasAlgorithm - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
-
Thomas algorithm is an efficient algorithm to solve a linear tridiagonal matrix equation.
- ThomasAlgorithm() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.ThomasAlgorithm
-
- ThreadIDRLG - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.context
-
This uniform number generator generates independent sequences of random numbers per thread, hence
thread-safe.
- ThreadIDRLG(int, long) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ThreadIDRLG
-
Constructs a per-context repeatable RLG.
- ThreadIDRLG(int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ThreadIDRLG
-
Constructs a per-context repeatable RLG.
- ThreadIDRLG() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ThreadIDRLG
-
Constructs a per-context repeatable RLG.
- ThreadIDRLG(long) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ThreadIDRLG
-
Constructs a per-context repeatable RLG.
- ThreadIDRNG - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.context
-
This random number generator generates independent sequences of random numbers per thread, hence
thread-safe.
- ThreadIDRNG(int, long) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ThreadIDRNG
-
Constructs a per-context repeatable RNG.
- threshold(double) - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.elliott2005.Elliott2005DLM
-
Gets a suggested trading threshold based on an OU process.
- throwIfDifferentDimension(Table, Table) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Throws if
A1.nRows() != A2.nRows()
Or
A1.nCols() != A2.nCols()
- throwIfIncompatible4Multiplication(Table, Table) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Throws if
A1.nCols() != A2.nRows()
- throwIfIncompatible4Multiplication(Table, Vector) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Throws if
A.nCols() != v.size()
- throwIfInvalidColumn(Table, int) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Throws if accessing an out of range column.
- throwIfInvalidIndex(Vector, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if an index is a valid index.
- throwIfInvalidRow(Table, int) - Static method in class com.numericalmethod.suanshu.misc.datastructure.DimensionCheck
-
Throws if accessing an out of range row.
- throwIfNotEqualSize(Vector, Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if the input vectors have the same size.
- throwIfNotNull(RuntimeException) - Static method in class com.numericalmethod.suanshu.misc.ExceptionUtils
-
This is a wrapper method that throws a RuntimeException if it is not null.
- Ties<T> - Class in com.numericalmethod.suanshu.combinatorics
-
Count the number of occurrences of each distinctive value.
- Ties(List<T>) - Constructor for class com.numericalmethod.suanshu.combinatorics.Ties
-
Count the number of occurrences of each distinctive value.
- time() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData.Row
-
Gets the time of the row.
- time() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get the current time.
- time(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Get the i-th time point.
- time() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomProcess
-
Get the current time.
- time(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Get the i-th timestamp.
- time(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Get the i-th time.
- time() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
Gets the forecast time.
- TimeGrid - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.timegrid
-
Specify the time points in a grid or axis.
- TimeInterval - Class in com.numericalmethod.suanshu.misc.datastructure.time
-
This is a time interval.
- TimeInterval(DateTime, DateTime) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.time.TimeInterval
-
Construct a time interval from two time points.
- TimeIntervals - Class in com.numericalmethod.suanshu.misc.datastructure.time
-
- TimeIntervals() - Constructor for class com.numericalmethod.suanshu.misc.datastructure.time.TimeIntervals
-
Construct an empty collection of time interval.
- TimeIntervals(DateTime, DateTime) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.time.TimeIntervals
-
Construct a collection consisting of one time interval.
- TimeIntervals(Interval<DateTime>) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.time.TimeIntervals
-
Construct a collection consisting of one time interval.
- TimeIntervals(Interval<DateTime>...) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.time.TimeIntervals
-
Construct a collection of time intervals.
- TimeIntervals(Intervals<DateTime>) - Constructor for class com.numericalmethod.suanshu.misc.datastructure.time.TimeIntervals
-
Copy constructor.
- times() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.HConstruction
-
- times() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
-
Get the entire time grid.
- TimeSeries<T extends Comparable<? super T>,V,E extends TimeSeries.Entry<T,V>> - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure
-
A time series is a serially indexed collection of items.
- TimeSeries.Entry<T,V> - Interface in com.numericalmethod.suanshu.stats.timeseries.datastructure
-
A time series is composed of a sequence of Entrys.
- timestamps() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Get all the timestamps.
- timestamps() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Get all the timestamps.
- to1DArray(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get all matrix entries in the form of an 1D double[].
- to2DArray(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get all matrix entries in the form of a 2D double[][] array.
- toArray() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- toArray() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.CombinedVectorByRef
-
- toArray() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- toArray() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- toArray() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- toArray() - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
-
Cast this vector into a 1D double[].
- toArray() - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- toArray(T[]) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- toArray() - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable.Row
-
Converts the row to a double[], excluding the index.
- toArray() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
Get the sorted sample.
- toArray() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Convert this multivariate time series into an array of vectors.
- toArray() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- toArray() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- toArray() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- toArray() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
-
- toArray() - Method in interface com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.UnivariateTimeSeries
-
Convert this time series into an array, discarding the timestamps.
- toBigDecimal() - Method in class com.numericalmethod.suanshu.number.Real
-
Convert this number to a BigDecimal.
- toColumns(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get an array of all column vectors from a matrix.
- toContext(HouseholderInPlace.Householder) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace.Householder
-
- toDense() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- toDense() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.Densifiable
-
Densify a matrix, i.e., convert a matrix implementation to the standard dense matrix,
DenseMatrix.
- toDense() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- toDense() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- toDouble() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
Cast the complex number to a
Double if it is a real number.
- toGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralLessThanConstraints
-
- toGreaterThanConstraints() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.LessThanConstraints
-
Convert the less-than or equal-to constraints to greater-than or equal-to constraints.
- toGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
-
- toGreaterThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearLessThanConstraints
-
- Tolerance - Interface in com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance
-
The tolerance criteria for an iterative algorithm to stop.
- toLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general.GeneralGreaterThanConstraints
-
- toLessThanConstraints() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.GreaterThanConstraints
-
Convert the greater-than or equal-to constraints to less-than or equal-to constraints.
- toLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
-
- toLessThanConstraints() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
-
- toMatrix() - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
Gets a copy of the flexible table in the form of a matrix.
- toMatrix() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- toMatrix() - Method in interface com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateTimeSeries
-
Convert this multivariate time series into an m x n matrix, where
m is the dimension, and n the length.
- toMatrix() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- toMatrix(UnivariateTimeSeries<?, ?>) - Static method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.UnivariateTimeSeriesUtils
-
Cast a time series into a column matrix, discarding the timestamps.
- TopNOptimizationAlgorithm - Class in com.numericalmethod.suanshu.finance.trading.portfoliooptimization
-
- TopNOptimizationAlgorithm(PortfolioOptimizationAlgorithm, int, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.TopNOptimizationAlgorithm
-
- topologicalOrder(V) - Method in interface com.numericalmethod.suanshu.graph.DAGraph
-
Get the topological order of a vertex.
- topologicalOrder(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseDAGraph
-
- topologicalOrder(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- topologicalOrder(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- toPrimitive(Double[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Convert a Double array to a primitive double array.
- toRows(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get an array of all row vectors from a matrix.
- toStop(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration.StoppingCriterion
-
Check whether we stop with the current eigenvector.
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
- toString(SparseMatrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
- toString(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the String representation of a matrix.
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector
-
- toString() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef
-
- toString() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
-
- toString() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.QuadraticFunction
-
- toString() - Method in class com.numericalmethod.suanshu.analysis.function.tuple.Triple
-
- toString() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
- toString() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.KagiModel
-
- toString() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MeanEstimatorMaxLevelShift
-
- toString() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.volarb.MRModelRanged
-
- toString() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
-
- toString() - Method in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM
-
- toString() - Method in class com.numericalmethod.suanshu.finance.trading.trend.kst1995.KnightSatchellTran1995
-
- toString() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS.Node
-
- toString() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node
-
- toString() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal.Node
-
- toString() - Method in class com.numericalmethod.suanshu.graph.community.EdgeBetweeness
-
- toString() - Method in class com.numericalmethod.suanshu.graph.community.GirvanNewman
-
- toString() - Method in class com.numericalmethod.suanshu.graph.type.SimpleArc
-
- toString() - Method in class com.numericalmethod.suanshu.graph.type.SimpleEdge
-
- toString() - Method in class com.numericalmethod.suanshu.graph.type.SparseDiGraph
-
- toString() - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
-
- toString() - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- toString() - Method in class com.numericalmethod.suanshu.interval.Interval
-
- toString() - Method in class com.numericalmethod.suanshu.interval.Intervals
-
- toString() - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
-
- toString() - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
-
- toString() - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet
-
- toString() - Method in class com.numericalmethod.suanshu.misc.datastructure.SortableArray
-
- toString() - Method in class com.numericalmethod.suanshu.number.complex.Complex
-
- toString(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Print out numbers to a string.
- toString(double[][]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
-
Print out a 2D array, double[][] to a string.
- toString() - Method in class com.numericalmethod.suanshu.number.Real
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
- toString() - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
- toString() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
- toString() - Method in class com.numericalmethod.suanshu.stats.evt.cluster.Clusters.Cluster
-
- toString() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- toString() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- toString() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData.Row
-
- toString() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
-
- toString() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159.RandomMatrix
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
- toString() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
- toVector(UnivariateTimeSeries<?, ?>) - Static method in class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.UnivariateTimeSeriesUtils
-
Cast a time series into a vector, discarding the timestamps.
- tr(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the sum of the diagonal elements, i.e., the trace of a matrix.
- track(V, int) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS
-
- track(V, int) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS
-
- track(V, int) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.TraversalFromRoots
-
Runs the traversal algorithm on a graph from a designated root.
- trades() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.HConstruction
-
- TradingPair - Class in com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration
-
- TradingPair(String, String, Vector, Vector, double) - Constructor for class com.numericalmethod.suanshu.finance.trading.meanreversion.cointegration.TradingPair
-
Constructs a related pair for trading, e.g., cointegrated pair.
- train(int[], DiscreteHMM) - Static method in class com.numericalmethod.suanshu.stats.hmm.discrete.BaumWelch
-
Constructs a trained (discrete) hidden Markov model, one iteration.
- train(MixtureHMM, double[]) - Static method in class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHMMEM
-
Constructs a trained mixture hidden Markov model, one iteration.
- transform(double[]) - Method in interface com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.Filter
-
Transforms the input signal into the output signal.
- transform(double[]) - Method in class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage
-
- transform(double[]) - Method in class com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverageByExtension
-
- transform(QPProblem) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer
-
- transpose(MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- transpose(MatrixAccess) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
Get the transpose of A.
- transpose(MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- transposeSolve(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
-
Return the input vector x.
- transposeSolve(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
-
Pt = P-1 for Jacobi preconditioner.
- transposeSolve(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner
-
Solve Mtv = x, where M is the preconditioner
matrix.
- transposeSolve(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
-
Mtx = M-1x as M is symmetric.
- Trapezoidal - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes
-
The Trapezoidal rule is a closed type Newton-Cotes formula, where the integral interval is evenly divided into N sub-intervals.
- Trapezoidal(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.Trapezoidal
-
Construct an integrator that implements the Trapezoidal rule.
- TraversalFromRoots<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
-
A graph traversal is the problem of visiting all the nodes in a graph in a particular manner.
- TraversalFromRoots(Graph<? extends V, ? extends Edge<V>>) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.TraversalFromRoots
-
Constructs a traversal order of a graph.
- traverse(V, int) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.TraversalFromRoots
-
Runs the traversal algorithm on a graph from a designated root.
- Tree<V,E extends HyperEdge<V>> - Interface in com.numericalmethod.suanshu.graph
-
A tree is an undirected graph in which any two vertices are connected by exactly one simple path.
- trees() - Method in interface com.numericalmethod.suanshu.graph.Forest
-
Get the disjoint set of trees.
- trend() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.KagiModel
-
- TrendType - Enum in com.numericalmethod.suanshu.stats.test.timeseries.adf
-
These are the three versions of the Augmented Dickey-Fuller (ADF) test.
- TriangularDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The triangular distribution is a continuous probability distribution with lower limit a, upper
limit b and mode c, where a < b and a ≤ c ≤ b.
- TriangularDistribution(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution
-
Constructs an instance of a Triangular distribution.
- TridiagonalDeflationSearch - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
-
This class locates deflation in a tridiagonal matrix.
- TridiagonalDeflationSearch(boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
-
- TridiagonalDeflationSearch(DeflationCriterion, boolean, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
-
- TriDiagonalization - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization
-
A tri-diagonal matrix A is a matrix such that
it has non-zero elements only in the main diagonal, the first diagonal below, and the first
diagonal above.
- TriDiagonalization(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization
-
Runs the tri-diagonalization process for a symmetric matrix.
- TridiagonalMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
-
A tri-diagonal matrix has non-zero entries only on the super, main and sub diagonals.
- TridiagonalMatrix(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
Constructs a 0 tri-diagonal matrix of dimension dim * dim.
- TridiagonalMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
Constructs a tri-diagonal matrix from a 3-row 2D double[][] array such that:
the first row is the super diagonal with (dim - 1) entries;
the second row is the main diagonal with dim entries;
the third row is the sub diagonal with (dim - 1) entries.
For example,
- TridiagonalMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
Casts a matrix to tridiagonal by copying the 3 diagonals (ignoring all other entries).
- TridiagonalMatrix(TridiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
Copy constructor performing a deep copy.
- Trigamma - Class in com.numericalmethod.suanshu.analysis.function.special.gamma
-
The trigamma function is defined as the logarithmic derivative of the digamma function.
- Trigamma() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gamma.Trigamma
-
- TrigMath - Class in com.numericalmethod.suanshu.geometry
-
A collection of trigonometric functions complementary to those in Java's
Math class.
- Triple - Class in com.numericalmethod.suanshu.analysis.function.tuple
-
A triple is a tuple of length three.
- Triple(double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.tuple.Triple
-
Creates a new triple with the given values.
- TrivariateRealFunction - Interface in com.numericalmethod.suanshu.analysis.function.rn2r1
-
A trivariate real function takes three real arguments and outputs one real value.
- TruncatedNormalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
-
The truncated Normal distribution is the probability distribution of a normally distributed
random variable whose value is either bounded below or above (or both).
- TruncatedNormalDistribution(double, double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
Construct a truncated Normal distribution.
- TruncatedNormalDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
Construct a truncated standard Normal distribution.
- TSS() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: total sum of squares, \(\sum (y-y_mean)^2 \).
- Twiddle<T> - Class in com.numericalmethod.suanshu.combinatorics
-
Generates all combinations of M elements drawn without replacement from a set of N
elements.
- Twiddle(Collection<T>, int) - Constructor for class com.numericalmethod.suanshu.combinatorics.Twiddle
-
- type - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
the label type
- V() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization
-
Gets V, where
V' = Vk * ...
- V() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
-
- V() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
-
Gets V, where
V' = Vk * ...
- V() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
-
- V() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
- V() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
-
- V() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD
-
- V() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the V matrix as in SVD decomposition.
- V() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Returns the matrix V as in A=UDV'.
- v - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
The defining vector which is perpendicular to the Householder hyperplane.
- v() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.QuadraticMonomial
-
Get v as in (x2 + ux + v).
- V - Variable in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.optimizer.MVOptimizer.Result
-
- V() - Method in class com.numericalmethod.suanshu.finance.trading.returns.moments.ReturnsMoments
-
Gets the second moment matrix.
- v() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
When the problem is unbounded, the direction of arbitrarily negative can be computed by
adjusting λ.
- v() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
-
- V(int) - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateObservationEquation
-
Gets V(t), the covariance matrix of vt.
- V(int) - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.ObservationEquation
-
Get V(t), the variance of vt.
- V() - Method in class com.numericalmethod.suanshu.stats.factor.pca.PCAbyEigen
-
Gets the correlation (or covariance) matrix used by the PCA.
- validate(String) - Static method in class com.numericalmethod.suanshu.misc.license.Package
-
- validateAll(String...) - Static method in class com.numericalmethod.suanshu.misc.license.Package
-
- validateAny(String...) - Static method in class com.numericalmethod.suanshu.misc.license.Package
-
- validateVersion(String, String) - Static method in class com.numericalmethod.suanshu.misc.license.Package
-
Check if a package is licensed up to a specified version.
- value - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
the entry value
- value() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
Gets the value of this entry.
- value() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.InnerProduct
-
Get the value of the inner product.
- value() - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.Lebesgue
-
Get the integral value.
- value(double[]) - Method in interface com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification.DiversificationMeasure
-
Evaluates the level of portfolio diversification given portfolio weights.
- value(double[]) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification.ProductOfWeights
-
- value(double[]) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification.SumOfPoweredWeights
-
- value(double[]) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification.SumOfSquaredWeights
-
- value(double[]) - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.corvalan2005.diversification.SumOfWLogW
-
- value() - Method in class com.numericalmethod.suanshu.finance.trading.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
-
- value(UndirectedEdge<V>) - Method in class com.numericalmethod.suanshu.graph.community.EdgeBetweeness
-
Gets the edge-betweeness of an edge.
- value(UndirectedEdge<V>) - Method in class com.numericalmethod.suanshu.graph.community.GirvanNewman
-
Get the edge-betweeness of an edge.
- value() - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.BBNode
-
the value of this node
- value() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.KendallRankCorrelation
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.SpearmanRankCorrelation
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedMean
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedVariance
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max
-
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min
-
- value(double) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile
-
Compute the sample value corresponding to a quantile.
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile
-
- value() - Method in interface com.numericalmethod.suanshu.stats.descriptive.Statistic
-
Get the value of the statistic.
- value() - Method in class com.numericalmethod.suanshu.stats.descriptive.SynchronizedStatistic
-
- value(double[]) - Method in class com.numericalmethod.suanshu.stats.evt.exi.ExtremalIndexByClusterSizeReciprocal
-
- value(double[]) - Method in class com.numericalmethod.suanshu.stats.evt.exi.ExtremalIndexByFerroSeegers
-
- value(double[]) - Method in interface com.numericalmethod.suanshu.stats.evt.exi.ExtremalIndexEstimation
-
Estimate the extremal index with the given observations from a distribution.
- value() - Method in enum com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
-
Gets the value of this Mersenne exponent.
- value() - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.BootstrapEstimator
-
Gets the estimator value (the mean).
- value(double[], double[], double[]) - Method in class com.numericalmethod.suanshu.stats.regression.WeightedRSS
-
Computes the weighted RSS for a set of observations.
- value(Filtration) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integral
-
Integrate the function with respect to a given filtration.
- value() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Get the expectation (the mean).
- ValueArray(int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
-
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry.TopLeftFirstComparator
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry.Comparator
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage.Side
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010Regime.Regime
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.Kagi.Trend
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.finance.trading.returns.ReturnsCalculators
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node.Color
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.interval.IntervalRelation
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.number.DoubleUtils.RoundingScheme
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.Test
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.TrendType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.descriptive.rank.Quantile.QuantileType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.descriptive.rank.Rank.TiesMethod
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.evt.markovchain.ExtremeValueMC.MarginalDistributionType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis.ScoringRule
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.UniformRNG.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov.Side
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1.Type
-
Deprecated.
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.timeseries.adf.TrendType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.test.variance.Levene.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCovariance.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
-
Returns the enum constant of this type with the specified name.
- values() - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry.TopLeftFirstComparator
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
-
- values() - Static method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry.Comparator
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.function.special.gamma.LogGamma.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Method in class com.numericalmethod.suanshu.combinatorics.Ties
-
Get the numbers of occurrences of the objects.
- values() - Static method in enum com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles.MovingAverage.Side
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.finance.trading.infantino2010.Infantino2010Regime.Regime
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.Kagi.Trend
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.finance.trading.returns.ReturnsCalculators
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node.Color
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.interval.IntervalRelation
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.number.DoubleUtils.RoundingScheme
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.Test
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.TrendType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.descriptive.rank.Quantile.QuantileType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.descriptive.rank.Rank.TiesMethod
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.evt.markovchain.ExtremeValueMC.MarginalDistributionType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.factor.factoranalysis.FactorAnalysis.ScoringRule
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.UniformRNG.Method
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData.Row
-
Gets the values of the row.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov.Side
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.timeseries.adf.ADFAsymptoticDistribution1.Type
-
Deprecated.
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.timeseries.adf.TrendType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.test.variance.Levene.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.SampleAutoCovariance.Type
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- VanDerWaerden - Class in com.numericalmethod.suanshu.stats.test.rank
-
The Van der Waerden test tests for the equality of all population distribution functions.
- VanDerWaerden(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden
-
Perform the Van Der Waerden test to test for the equality of all population distribution functions.
- VanDerWaerden1969 - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.beta
-
- VanDerWaerden1969(RandomGammaGenerator, RandomGammaGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.VanDerWaerden1969
-
Deprecated.
Construct a random number generator to sample from the beta distribution.
- VanDerWaerden1969(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.VanDerWaerden1969
-
Deprecated.
Construct a random number generator to sample from the beta distribution.
- var - Variable in class com.numericalmethod.suanshu.finance.trading.trend.dai2011.Dai2011HMM.CalibrationParam
-
- var() - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.CorrelationMatrix
-
Gets the variance of the elements.
- var(int) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.CorrelationMatrix
-
Gets the variance of the i-th element.
- var() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
Gets the variance of the prediction.
- var() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Gets the mean squared error of the h-step ahead prediction.
- var() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Get the variance of the white noise.
- var() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Gets the mean squared error of the h-step ahead prediction.
- var(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Gets the mean squared error of the prediction at time n for \(\hat{x}_{n+1}\), i.e.,
\(E(x_{n+1} - \hat{x}_{n+1})^2\).
- var() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Gets the mean squared error of the one-step ahead prediction.
- var() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
- var() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Compute the unconditional variance of the GARCH model.
- var(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
-
Gets the mean squared error for prediction errors at time n for \(\hat{x}_{n+1}\),
i.e.,
\(E(x_{n+1} - \hat{x}_{n+1})^2\).
- VARFit - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class construct a VAR model by estimating the coefficients using OLS regression.
- VARFit(MultivariateIntTimeTimeSeries, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARFit
-
Estimate a VAR model from a multivariate time series.
- Variable(String, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
Constructs a named variable.
- variance() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
-
Get the (unbiased) variance.
- variance() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
-
Get the (unbiased) variance.
- Variance - Class in com.numericalmethod.suanshu.stats.descriptive.moment
-
The variance of a sample is the average squared deviations from the sample mean.
- Variance() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Construct an empty Variance calculator.
- Variance(double[], boolean) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Construct a Variance calculator,
initialized with a sample.
- Variance(double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Construct an unbiased Variance calculator.
- Variance(Variance) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
-
Copy constructor.
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
-
Gets the variance of this distribution.
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
-
- variance() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
-
Gets the variance of this distribution.
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
-
Gets the variance of this distribution.
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
-
- variance() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
-
\[
\frac{\sigma^2}{(1-\xi)^2(1-2\xi)}
\]
for \(\xi < 1/2\).
- variance() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution
-
- variance() - Method in interface com.numericalmethod.suanshu.stats.random.Estimator
-
Gets the variance of the estimator.
- variance() - Method in class com.numericalmethod.suanshu.stats.random.sampler.resampler.BootstrapEstimator
-
Gets the estimator variance, of which the convergence limit is decided by
sample size, not B.
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial
-
- variance(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMExponentialDistribution
-
The variance function of the distribution in terms of the mean μ.
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma
-
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian
-
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian
-
- variance(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson
-
- variance() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralExpectation
-
Compute the variance of the integral.
- variance() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Get the variance.
- variance() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- variance() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- variance() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- variance() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- variance() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- variance() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- VariancebtX - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
-
Computes \(b'Xb\).
- VariancebtX(Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.VariancebtX
-
Computes \(b'Xb\).
- variation() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.HConstruction
-
Gets the H variation/fluctuation.
- VARIMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima
-
An ARIMA(p, d, q) process, Yt, is such that
\[
X_t = (1 - L)^d Y_t
\]
where
L is the lag operator, d the order of difference,
Xt an ARMA(p, q) process, for which
\[
X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,
\]
Xt, μ and εt are n-dimensional
vectors.
- VARIMAModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model.
- VARIMAModel(Vector, Matrix[], int, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model with unit variance.
- VARIMAModel(Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model with zero-intercept (mu).
- VARIMAModel(Matrix[], int, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model with unit variance and zero-intercept (mu).
- VARIMAModel(ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate model from a univariate ARIMA model.
- VARIMAModel(VARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAModel
-
Copy constructor.
- VARIMASim - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima
-
This class simulates a multivariate ARIMA process.
- VARIMASim(VARIMAModel, Vector[], Vector[], RandomVectorGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMASim
-
Construct a multivariate ARIMA model.
- VARIMASim(VARIMAModel, RandomVectorGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMASim
-
Construct a multivariate ARIMA model.
- VARIMASim(VARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMASim
-
Construct a multivariate ARIMA model, using random standard Gaussian innovations.
- VARIMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima
-
The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by
incorporating exogenous variables.
- VARIMAXModel(Vector, Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model.
- VARIMAXModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model with unit variance.
- VARIMAXModel(Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model with zero-intercept (mu).
- VARIMAXModel(Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model with unit variance and zero-intercept (mu).
- VARIMAXModel(VARIMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Copy constructor.
- VARIMAXModel(ARIMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model from a univariate ARIMAX model.
- VARLinearRepresentation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated)
infinite sum of AR terms.
- VARLinearRepresentation(VARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
-
Construct the linear representation of an ARMA model.
- VARLinearRepresentation(VARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
-
- VARMAAutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that
EXt = 0.
- VARMAAutoCorrelation(VARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
-
Compute the auto-correlation function for a vector ARMA model.
- VARMAAutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that
EXt = 0.
- VARMAAutoCovariance(VARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
-
Compute the auto-covariance function for a vector ARMA model.
- VARMAForecastOneStep - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This is an implementation, adapted for an ARMA process, of the innovation algorithm,
which is an efficient way of obtaining a one step least square linear predictor.
- VARMAForecastOneStep(MultivariateIntTimeTimeSeries, VARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
-
Construct an instance of InnovationAlgorithm for a multivariate ARMA time series.
- VARMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
A multivariate ARMA model, Xt, takes this form.
- VARMAModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model.
- VARMAModel(Vector, Matrix[], Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model with unit variance.
- VARMAModel(Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model with zero-intercept (mu).
- VARMAModel(Matrix[], Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model with unit variance and zero-intercept (mu).
- VARMAModel(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate model from a univariate ARMA model.
- VARMAModel(VARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Copy constructor.
- VARMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
The VARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by
incorporating exogenous variables.
- VARMAXModel(Vector, Matrix[], Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate ARMAX model.
- VARMAXModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate ARMAX model with unit variance.
- VARMAXModel(Matrix[], Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate ARMAX model with zero-intercept (mu).
- VARMAXModel(Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate ARMAX model with unit variance and zero-intercept (mu).
- VARMAXModel(ARMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate model from a univariate ARMAX model.
- VARMAXModel(VARMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Copy constructor.
- VARModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a VAR model.
- VARModel(Vector, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model.
- VARModel(Vector, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model with unit variance.
- VARModel(Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model with zero-intercept (mu).
- VARModel(Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model with unit variance and zero-intercept (mu).
- VARModel(ARModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a multivariate model from a univariate AR model.
- VARModel(VARModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Copy constructor.
- VARXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
A VARX (Vector AutoRegressive model with eXogeneous inputs) model, Xt, takes
this form.
- VARXModel(Vector, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model.
- VARXModel(Vector, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model with unit variance.
- VARXModel(Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model with zero-mean.
- VARXModel(Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model with unit variance and zero-mean.
- VARXModel(VECMTransitory) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX(p) from a transitory VECM(p).
- VARXModel(VECMLongrun) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX(p) from a long-run VECM(p).
- VARXModel(VARXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Copy constructor.
- VECM - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
A Vector Error Correction Model (VECM(p)) has one of the following specifications:
Transitory:
\[
\Delta Y_t = \mu + \Pi Y_{t-1} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1
\]
or
Long-run:
\[
\Delta Y_t = \mu + \Pi Y_{t-p} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1
\]
Yt,
μ and
εt are n-dimensional vectors.
- VECM(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Construct a VECM(p) model.
- VECM(VECM) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Copy constructor.
- VECMLongrun - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
The long-run Vector Error Correction Model (VECM(p)) takes this form.
- VECMLongrun(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Construct a long-run VECM(p) model.
- VECMLongrun(Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Construct a long-run VECM(p) model with zero-intercept (mu).
- VECMLongrun(VARXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Construct a long-run VECM(p) from a VARX(p).
- VECMLongrun(VECMLongrun) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Copy constructor.
- VECMTransitory - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
A transitory Vector Error Correction Model (VECM(p)) takes this form.
- VECMTransitory(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a transitory VECM(p) model.
- VECMTransitory(Matrix, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a transitory VECM(p) model with zero-intercept (mu).
- VECMTransitory(VARXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a transitory VECM(p) from a VARX(p).
- VECMTransitory(VECMTransitory) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Copy constructor.
- Vector - Interface in com.numericalmethod.suanshu.algebra.linear.vector.doubles
-
An Euclidean vector is a geometric object that has both a magnitude/length and a direction.
- VectorAccessException - Exception in com.numericalmethod.suanshu.algebra.linear.vector
-
This is the exception thrown when any invalid access to a
Vector instance is
detected, e.g., out-of-range index.
- VectorAccessException(int, int) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.vector.VectorAccessException
-
Constructs an instance of VectorAccessException for out-of-range access.
- VectorAccessException(String) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.vector.VectorAccessException
-
Constructs an instance of VectorAccessException.
- VectorFactory - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation
-
These are the utility functions that create new instances of vectors from existing ones.
- VectorMathOperation - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense
-
This is a generic implementation of the math operations of
double based
Vector.
- VectorMathOperation() - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
-
- VectorMonitor - Class in com.numericalmethod.suanshu.misc.algorithm.iterative.monitor
-
- VectorMonitor() - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.VectorMonitor
-
- VectorSizeMismatch - Exception in com.numericalmethod.suanshu.algebra.linear.vector
-
This is the exception thrown when an operation is performed on two vectors with different
sizes.
- VectorSizeMismatch(int, int) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.vector.VectorSizeMismatch
-
Constructs an instance of SizeMismatch.
- VectorSpace<V,F extends Field<F>> - Interface in com.numericalmethod.suanshu.algebra.structure
-
A vector space is a set V together with two binary operations that combine two entities to yield a third,
called vector addition and scalar multiplication.
- vers(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
-
Returns the versed sine or versine of an angle.
- vertex() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal.Node
-
Gets the node.
- VertexTree<T> - Class in com.numericalmethod.suanshu.graph.type
-
A
VertexTree is both a tree and a vertex/node.This implementation builds a tree
incrementally and recursively (combining trees).
- VertexTree(T) - Constructor for class com.numericalmethod.suanshu.graph.type.VertexTree
-
- vertices() - Method in interface com.numericalmethod.suanshu.geometry.polyline.PolygonalChain
-
Get a list of the vertices defining the chain.
- vertices() - Method in class com.numericalmethod.suanshu.geometry.polyline.PolygonalChainByArray
-
- vertices() - Method in interface com.numericalmethod.suanshu.graph.Graph
-
Gets the set of all vertices in this graph.
- vertices() - Method in interface com.numericalmethod.suanshu.graph.HyperEdge
-
Gets the set of vertices associated with the edge.
- vertices() - Method in class com.numericalmethod.suanshu.graph.type.SimpleArc
-
- vertices() - Method in class com.numericalmethod.suanshu.graph.type.SimpleEdge
-
- vertices() - Method in class com.numericalmethod.suanshu.graph.type.SparseDiGraph
-
Gets the set of all vertices in this graph, sorted by the number of parents.
- vertices() - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
-
- vertices() - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
-
- vertices() - Method in class com.numericalmethod.suanshu.graph.type.VertexTree
-
- visitingTemperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction
-
- visitingTemperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
-
- visitingTemperature(int) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.TemperatureFunction
-
Gets the visiting temperature \(T^V_t\) at time t.
- visitTime() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal.Node
-
Gets the first visit time of this node.
- Viterbi - Class in com.numericalmethod.suanshu.stats.hmm
-
The Viterbi algorithm is a dynamic programming algorithm for finding the most
likely sequence of hidden states - called the Viterbi path - that results in
a sequence of observed events, especially in the context of Markov
information sources and hidden Markov models.
- Viterbi(HiddenMarkovModel) - Constructor for class com.numericalmethod.suanshu.stats.hmm.Viterbi
-
Constructs an Viterbi algorithm for an HMM.
- VMAInvertibility - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
The inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages.
- VMAInvertibility(VARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
Construct the inverse representation of an ARMA model.
- VMAInvertibility(VARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
- VMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a multivariate MA model.
- VMAModel(Vector, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model.
- VMAModel(Vector, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model with unit variance.
- VMAModel(Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model with zero-mean.
- VMAModel(Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model with unit variance and zero-mean.
- VMAModel(MAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model from a univariate MA model.
- VMAModel(VMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Copy constructor.
- volatility() - Method in class com.numericalmethod.suanshu.finance.trading.meanreversion.hvolatility.HConstruction
-
Gets the H volatility.
- Vt() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
- Vt() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
-
- Vt() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets the inverse (or transpose) of accumulated Householder right-reflections applied to A.