- A() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the homogeneous part, the coefficient matrix, of the linear system.
- a() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
Gets the width (x-axis) of the rectangular region.
- a() - Method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision
-
Get a as in the remainder (b * (x + u) + a).
- A(int, double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
-
Compute an.
- a() - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
-
Get a.
- A() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
Get the constraint coefficients.
- A - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
This is either [A] or
[ A]
[-C]
- A(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Gets Ai.
- A(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
-
Gets Ai.
- A(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets Ai.
- A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
\[
A = [A_1, A_2, ...
- A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets A.
- A() - Method in interface dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- A() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- A() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- a() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- A() - Method in class dev.nm.stat.markovchain.SimpleMC
-
Gets the state transition probabilities.
- A() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the estimated sequence of A.
- A() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Gets the regressor matrix.
- A - Variable in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix
-
a random matrix constructed by AS159
- A() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets A
as in eq.
- a0() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Gets the constant coefficient.
- a0() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the constant term.
- a1() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
-
Gets the ARCH coefficient.
- AbelianGroup<G> - Interface in dev.nm.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 dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector pair.
- ABMPredictorCorrector1 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 1.
- ABMPredictorCorrector1() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
-
- ABMPredictorCorrector2 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 2.
- ABMPredictorCorrector2() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
-
- ABMPredictorCorrector3 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 3.
- ABMPredictorCorrector3() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
-
- ABMPredictorCorrector4 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 4.
- ABMPredictorCorrector4() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
-
- ABMPredictorCorrector5 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 5.
- ABMPredictorCorrector5() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
-
- abs(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the absolute values of a vector, element-by-element.
- abs(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the absolute values.
- ABSOLUTE_ZERO_T0 - Static variable in class dev.nm.misc.PhysicalConstants
-
The absolute zero temperature in Celsius (°C).
- absoluteError(double, double) - Static method in class dev.nm.number.DoubleUtils
-
Compute the absolute difference between x1
and x0
.
- AbsoluteErrorPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the absolute error penalties.
- AbsoluteErrorPenalty(EqualityConstraints, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
-
Construct an absolute error penalty function from a collection of equality constraints.
- AbsoluteErrorPenalty(EqualityConstraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
-
Construct an absolute error penalty function from a collection of equality constraints.
- AbsoluteErrorPenalty(EqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
-
Construct an absolute error penalty function from a collection of equality constraints.
- AbsoluteTolerance - Class in dev.nm.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 dev.nm.misc.algorithm.iterative.tolerance.AbsoluteTolerance
-
- AbsoluteTolerance(double) - Constructor for class dev.nm.misc.algorithm.iterative.tolerance.AbsoluteTolerance
-
Construct an instance with specified tolerance
.
- AbstractBivariateEVD - Class in dev.nm.stat.evt.evd.bivariate
-
- AbstractBivariateEVD() - Constructor for class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
-
- AbstractBivariateProbabilityDistribution - Class in dev.nm.stat.distribution.multivariate
-
- AbstractBivariateProbabilityDistribution() - Constructor for class dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
-
- AbstractBivariateRealFunction - Class in dev.nm.analysis.function.rn2r1
-
A bivariate real function takes two real arguments and outputs one real value.
- AbstractBivariateRealFunction() - Constructor for class dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction
-
- AbstractHybridMCMC - Class in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
-
Constructs a new instance with the given parameters.
- AbstractMetropolis - Class in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
Constructs a new instance with the given parameters.
- AbstractR1RnFunction - Class in dev.nm.analysis.function.rn2rm
-
This is a function that takes one real argument and outputs one vector value.
- AbstractR1RnFunction(int) - Constructor for class dev.nm.analysis.function.rn2rm.AbstractR1RnFunction
-
- AbstractRealScalarFunction - Class in dev.nm.analysis.function.rn2r1
-
- AbstractRealScalarFunction(int) - Constructor for class dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction
-
Construct an instance with the dimension of the domain.
- AbstractRealVectorFunction - Class in dev.nm.analysis.function.rn2rm
-
- AbstractRealVectorFunction(int, int) - Constructor for class dev.nm.analysis.function.rn2rm.AbstractRealVectorFunction
-
- AbstractTrivariateRealFunction - Class in dev.nm.analysis.function.rn2r1
-
A trivariate real function takes three real arguments and outputs one real value.
- AbstractTrivariateRealFunction() - Constructor for class dev.nm.analysis.function.rn2r1.AbstractTrivariateRealFunction
-
- AbstractUnivariateRealFunction - Class in dev.nm.analysis.function.rn2r1.univariate
-
A univariate real function takes one real argument and outputs one real value.
- AbstractUnivariateRealFunction() - Constructor for class dev.nm.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction
-
- acceptanceProbability(Vector, double, Vector, double, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.BoxGSAAcceptanceProbabilityFunction
-
- acceptanceProbability(Vector, double, Vector, double, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.GSAAcceptanceProbabilityFunction
-
- acceptanceProbability(Vector, double, Vector, double, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.MetropolisAcceptanceProbabilityFunction
-
- acceptanceProbability(Vector, double, Vector, double, double) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.TemperedAcceptanceProbabilityFunction
-
Computes the probability that the next state transition will be accepted.
- acceptanceRate() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
Gets the acceptance rate, i.e.
- acceptanceTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction
-
- acceptanceTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
-
- acceptanceTemperature(int) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.TemperatureFunction
-
Gets the acceptance temperature \(T^A_t\) at time t.
- ACERAnalysis - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Create an instance with the default values.
- ACERAnalysis(int, int, double, boolean, boolean) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Create an instance with various options listed below.
- ACERAnalysis.Result - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
-
- ACERByCounting - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
-
Estimate epsilons by counting conditional exceedances from the observations.
- ACERByCounting(double[], int) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERByCounting
-
Create an instance for estimating epsilon for each of the given barrier levels.
- ACERConfidenceInterval - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- ACERFunction - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction
-
- ACERFunction.ACERParameter - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
-
- ACERInverseFunction - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
-
The inverse of the ACER function.
- ACERInverseFunction(ACERFunction.ACERParameter) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERInverseFunction
-
- ACERLogFunction - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.ACERLogFunction
-
Create an instance with the ACER function parameter.
- ACERParameter(double[]) - Constructor for class dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- ACERReturnLevel - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.ACERReturnLevel
-
Create an instance with the (estimated) ACER function parameter and the total number of
events.
- ACERUtils - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
-
Utility functions used in ACER empirical analysis.
- ACERUtils() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERUtils
-
- acos(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Inverse of cosine.
- acosh(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the arc hyperbolic cosine of a value; the returned hyperbolic angle is positive.
- acot(double) - Static method in class dev.nm.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 dev.nm.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 dev.nm.geometry.TrigMath
-
Returns the arc hyperbolic cotangent of a value.
- acovers(double) - Static method in class dev.nm.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 dev.nm.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 dev.nm.geometry.TrigMath
-
Returns the arc hyperbolic cosecant of a value.
- actions() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the the sequence of actions taken.
- ActiveList - Interface in dev.nm.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 dev.nm.misc.algorithm
-
This class keeps track of the active and inactive indices.
- ActiveSet(boolean) - Constructor for class dev.nm.misc.algorithm.ActiveSet
-
Construct a working set of active/inactive indices.
- ActiveSet(boolean, Collection<Integer>) - Constructor for class dev.nm.misc.algorithm.ActiveSet
-
Construct a working set of active/inactive indices.
- ActiveSet(boolean, int[]) - Constructor for class dev.nm.misc.algorithm.ActiveSet
-
Construct a working set of active/inactive indices.
- activeSize() - Method in class dev.nm.misc.algorithm.ActiveSet
-
Get the number of active indices.
- AdamsBashforthMoulton - Class in dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- add(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
-
this + that
- add(DenseData) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Add up the elements in this
and that
, element-by-element.
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Computes the sum of two diagonal matrices.
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- add(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- add(MatrixAccess, MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A1 + A2
- add(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- add(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- add(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- add(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- add(ComplexMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- add(GenericFieldMatrix<F>) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- add(RealMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- add(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- add(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- add(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- add(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Adds two vectors, element-by-element.
- add(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Adds a constant to a vector, element-by-element.
- add(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- add(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- add(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- add(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
\(this + that\)
- add(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Add a constant to all entries in this vector.
- add(G) - Method in interface dev.nm.algebra.structure.AbelianGroup
-
+ : G × G → G
- add(Polynomial) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
- add(OrderedPairs) - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
-
Dynamically add points to the step function.
- add(double) - Method in class dev.nm.combinatorics.Counter
-
Add a number to the counter.
- add(double...) - Method in class dev.nm.combinatorics.Counter
-
Add numbers to the counter.
- add(Interval<T>) - Method in class dev.nm.interval.Intervals
-
Add an interval to the set.
- add(Interval<T>...) - Method in class dev.nm.interval.Intervals
-
Add intervals to the set.
- add(BBNode) - Method in interface dev.nm.misc.algorithm.bb.ActiveList
-
Add a node to the active list.
- add(double, T) - Method in class dev.nm.misc.algorithm.Bins
-
Add a valued item to the bin.
- add(T) - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- add(Complex) - Method in class dev.nm.number.complex.Complex
-
- add(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
-
- add(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Add a double value to each element in an array.
- add(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
-
Add two double
arrays, entry-by-entry.
- add(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
-
- add(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
-
- add(Real) - Method in class dev.nm.number.Real
-
- add(SOCPGeneralConstraint) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Add an SOCP constraint.
- addActive(Collection<Integer>) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add active indices.
- addActive(int[]) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add active indices.
- addActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add an active constraint by index.
- addAll(Collection<? extends T>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- addCheck(PairingCheck) - Method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
-
- addColAt(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Adds a column at i.
- addColAt(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Adds a column at i.
- addColumn(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Column addition:
A[, j1] = A[, j1] + c * A[, j2]
- addData(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.LinearInterpolator
-
- addData(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
- addData(OrderedPairs) - Method in interface dev.nm.analysis.curvefit.interpolation.OnlineInterpolator
-
Add more points for interpolation.
- addData(double...) - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
-
Update the statistic with more data.
- addData(double[], double[]) - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
-
Add the given two samples.
- addData(double...) - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
-
Update the statistic with more data.
- addData(double[], double[]) - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
-
Add the given two samples.
- addData(double...) - Method in class dev.nm.stat.descriptive.covariance.Covariance
-
Update the covariance statistic with more data.
- addData(double[], double[]) - Method in class dev.nm.stat.descriptive.covariance.Covariance
-
Update the covariance statistic with more data.
- addData(double...) - Method in class dev.nm.stat.descriptive.moment.Kurtosis
-
- addData(double...) - Method in class dev.nm.stat.descriptive.moment.Mean
-
- addData(double...) - Method in class dev.nm.stat.descriptive.moment.Moments
-
- addData(double...) - Method in class dev.nm.stat.descriptive.moment.Skewness
-
- addData(double...) - Method in class dev.nm.stat.descriptive.moment.Variance
-
- addData(double...) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
-
- addData(double[], double[]) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
-
- addData(double...) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
-
- addData(double[], double[]) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
-
- addData(double...) - Method in class dev.nm.stat.descriptive.rank.Max
-
- addData(double...) - Method in class dev.nm.stat.descriptive.rank.Min
-
- addData(double...) - Method in class dev.nm.stat.descriptive.rank.Quantile
-
- addData(double...) - Method in interface dev.nm.stat.descriptive.Statistic
-
Recompute the statistic with more data, incrementally if possible.
- addData(double...) - Method in class dev.nm.stat.descriptive.SynchronizedStatistic
-
- addEdge(E) - Method in interface dev.nm.graph.Graph
-
Adds an edge to this graph.
- addEdge(E) - Method in class dev.nm.graph.type.SparseDAGraph
-
- addEdge(E) - Method in class dev.nm.graph.type.SparseGraph
-
- addEdge(Arc<V>) - Method in class dev.nm.graph.type.SparseTree
-
Add an edge to the tree, connecting v1, the parent and v2..., the children.
- addEdge(Arc<VertexTree<T>>) - Method in class dev.nm.graph.type.VertexTree
-
- addEdges(Graph<V, E>, E...) - Static method in class dev.nm.graph.GraphUtils
-
Add a set of edges to a graph.
- addFactor(int) - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
-
Adds the indexed factor.
- addInactive(Collection<Integer>) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add inactive indices.
- addInactive(int[]) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add inactive indices.
- addInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add an inactive constraint by index.
- addIterate(S) - Method in class dev.nm.misc.algorithm.iterative.monitor.CountMonitor
-
- addIterate(S) - Method in class dev.nm.misc.algorithm.iterative.monitor.IteratesMonitor
-
- addIterate(S) - Method in interface dev.nm.misc.algorithm.iterative.monitor.IterationMonitor
-
Record a new iteration state.
- addIterate(S) - Method in class dev.nm.misc.algorithm.iterative.monitor.NullMonitor
-
- addIterate(Vector) - Method in class dev.nm.misc.algorithm.iterative.monitor.VectorMonitor
-
- AdditiveModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.AdditiveModel
-
Construct a univariate time series by adding up the components.
- AdditiveModel(double[], double[], RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.AdditiveModel
-
Construct a univariate time series by adding up the components.
- addRow(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Row addition:
A[i1, ] = A[i1, ] + c * A[i2, ]
- addRow(double, double[]) - Method in class dev.nm.misc.datastructure.MathTable
-
Adds a row to the table.
- addRow(S, String, double...) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Inserts a row of data into the panel.
- addRow(PanelData<S>.Row) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Inserts a row of data into the panel.
- addRowAt(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Adds a row at i.
- addRowAt(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Adds a row at i.
- addRows(double[][]) - Method in class dev.nm.misc.datastructure.MathTable
-
Adds rows by a double[][]
.
- addVertex(V) - Method in interface dev.nm.graph.Graph
-
Adds a vertex to this graph.
- addVertex(V) - Method in class dev.nm.graph.type.SparseGraph
-
- addVertex(V) - Method in class dev.nm.graph.type.SparseTree
-
- addVertex(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- addVertices(Graph<V, ?>, V...) - Static method in class dev.nm.graph.GraphUtils
-
Add a set of vertices to a graph.
- ADFAsymptoticDistribution - Class in dev.nm.stat.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 dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution
-
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
- ADFAsymptoticDistribution(TrendType) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution
-
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
- ADFAsymptoticDistribution1 - Class in dev.nm.stat.test.timeseries.adf
-
- ADFAsymptoticDistribution1(ADFAsymptoticDistribution1.Type) - Constructor for class dev.nm.stat.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 dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1
-
Deprecated.
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
- ADFAsymptoticDistribution1.Type - Enum in dev.nm.stat.test.timeseries.adf
-
Deprecated.
the available types of Dickey-Fuller tests
- ADFDistribution - Class in dev.nm.stat.test.timeseries.adf
-
This represents an Augmented Dickey Fuller distribution.
- ADFDistributionTable - Class in dev.nm.stat.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 dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable
-
- ADFDistributionTable_CONSTANT_lag0 - Class in dev.nm.stat.test.timeseries.adf.table
-
- ADFDistributionTable_CONSTANT_lag0() - Constructor for class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable_CONSTANT_lag0
-
- ADFDistributionTable_CONSTANT_TIME_lag0 - Class in dev.nm.stat.test.timeseries.adf.table
-
- ADFDistributionTable_CONSTANT_TIME_lag0() - Constructor for class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable_CONSTANT_TIME_lag0
-
- ADFDistributionTable_NO_CONSTANT_lag0 - Class in dev.nm.stat.test.timeseries.adf.table
-
- ADFDistributionTable_NO_CONSTANT_lag0() - Constructor for class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable_NO_CONSTANT_lag0
-
- ADFFiniteSampleDistribution - Class in dev.nm.stat.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 dev.nm.stat.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
- ADFFiniteSampleDistribution(int, TrendType, boolean, int) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
- ADFFiniteSampleDistribution(int, TrendType) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct a finite sample distribution for the original Dickey-Fuller test
statistic.
- ADFFiniteSampleDistribution(int) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFFiniteSampleDistribution
-
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
- Aeq() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the coefficients, Aeq, of the equality constraints Aeq * x ≥ beq.
- Aeq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- Aeq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- Aeq() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- aexsec(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the arc exsecant of a value; the returned angle is in the range 0.0 through
pi.
- AfterIterations - Class in dev.nm.misc.algorithm.stopcondition
-
Stops after a given number of iterations.
- AfterIterations(int) - Constructor for class dev.nm.misc.algorithm.stopcondition.AfterIterations
-
Stops after a given number of iterations.
- AfterNoImprovement - Class in dev.nm.misc.algorithm.stopcondition
-
- AfterNoImprovement(int) - Constructor for class dev.nm.misc.algorithm.stopcondition.AfterNoImprovement
-
- AhatEstimation - Class in tech.nmfin.meanreversion.daspremont2008
-
Estimates the coefficient of a VAR(1) model by penalized maximum likelihood.
- AhatEstimation(Matrix, Matrix, double) - Constructor for class tech.nmfin.meanreversion.daspremont2008.AhatEstimation
-
Estimates the coefficient matrix of a vector autoregressive process of
order 1.
- ahav(double) - Static method in class dev.nm.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 dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
-
AIC = 2 * #param - 2 * log-likelihood
- AIC(Vector, Vector, Vector, double, double, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
-
- AIC(Vector, Vector, Vector, double, double, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
-
- AIC() - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
-
Gets the Akaike information criterion (AIC).
- AIC() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
-
Gets the AIC.
- AIC() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMInformationCriteria
-
Gets the Akaike information criterion.
- AIC() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Compute the AIC of fitted model.
- AIC() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Compute the AIC, a model selection criterion.
- AICC() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Compute the AICC of fitted model.
- AICC() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Compute the AICC, a model selection criterion.
- ak - Variable in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
the increment in the search direction
- algebraicMultiplicity() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get the multiplicity of the eigenvalue (a root) of the characteristic
polynomial.
- allForecasts() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Gets all the predictions of the next h steps in one vector.
- allForecasts() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Gets all the predictions of the next h steps in one vector.
- AllIntegers() - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers
-
- allMSEs() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Gets all the mean squared errors (MSE) of the h-step ahead predictions.
- allMSEs() - Method in class dev.nm.stat.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
-
Get the percentage increment along the minimizer increment direction.
- alpha(Vector, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- alpha(Vector, Vector, Vector, Vector) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation
-
Get the percentage increment along the minimizer increment direction.
- alpha(Vector, Vector, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
-
Get the percentage increment along the minimizer increment direction.
- alpha() - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the set of adjusting coefficients, by columns.
- alpha - Variable in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
-
α: the shape parameter
- alpha() - Method in class dev.nm.stat.regression.linear.panel.FixedEffectsModel
-
Gets the individual/subject specific terms.
- alpha(double) - Method in class dev.nm.stat.test.distribution.AndersonDarlingPValue
-
Gets the p-value for a test statistic.
- alpha() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Gets the ARCH coefficient.
- alpha() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the ARCH coefficients.
- alpha0 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
- alpha1 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
- AlternatingDirectionImplicitMethod - Class in dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod
-
Create an ADI method with the given precision parameter.
- AlternatingDirectionImplicitMethod(double, boolean) - Constructor for class dev.nm.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 dev.nm.stat.test.distribution
-
This algorithm calculates the Anderson-Darling k-sample test statistics and p-values.
- AndersonDarling(double[]...) - Constructor for class dev.nm.stat.test.distribution.AndersonDarling
-
Runs the Anderson-Darling test.
- AndersonDarlingPValue - Class in dev.nm.stat.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 dev.nm.stat.test.distribution.AndersonDarlingPValue
-
Construct the Anderson-Darling distribution for a particular number of samples.
- AndStopConditions - Class in dev.nm.misc.algorithm.stopcondition
-
Combines an arbitrary number of stop conditions, terminating when all conditions are met.
- AndStopConditions(StopCondition...) - Constructor for class dev.nm.misc.algorithm.stopcondition.AndStopConditions
-
- angle(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- angle(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- angle(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the angle between two vectors.
- angle(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- angle(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Measure the angle, \(\theta\), between this
and that
.
- angle(H) - Method in interface dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.solver.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 dev.nm.stat.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 dev.nm.stat.random.variancereduction.AntitheticVariates
-
Estimate \(E(f(X_1))\), where f is a function of a random variable.
- AntitheticVariates(UnivariateRealFunction, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.variancereduction.AntitheticVariates
-
- AntoniouLu2007 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
-
This implementation is based on Algorithm 14.5 in the reference.
- APERY - Static variable in class dev.nm.misc.Constants
-
the Apery's constant
- AR() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
-
Get the AR coefficients.
- AR(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the i-th AR coefficient; AR(0) = 1.
- AR(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the i-th AR coefficient; AR(0) = 1.
- AR1GARCH11Model - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch
-
An AR1-GARCH11 model takes this form.
- AR1GARCH11Model(double, double, double, double, double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
- AR2() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: adjusted R-squared
- Arc<V> - Interface in dev.nm.graph
-
An arc is an ordered pair of vertices.
- areAllConstraintsSatisfied(Vector) - Method in class dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Checks whether the constraints are satisfied with a solution vector x.
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
-
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
- areAllSparse(Matrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
- areAllSparse(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
- areEqual(Matrix, Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks the equality of two matrices up to a precision.
- areEqual(Vector, Vector, double) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if two vectors are orthogonal, i.e., v1 ∙ v2 == 0.
- areOrthogonal(Vector[], double) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if two vectors are orthogonormal.
- areOrthogonormal(Vector[], double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a set of vectors are orthogonormal.
- arg() - Method in class dev.nm.number.complex.Complex
-
Get the θ of the complex number in polar representation.
- ArgumentAssertion - Class in dev.nm.misc
-
Utility class for checking numerical arguments.
- ARIMAForecast - Class in dev.nm.stat.timeseries.linear.univariate.arima
-
Forecasts an ARIMA time series using the innovative algorithm.
- ARIMAForecast(IntTimeTimeSeries, ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast
-
Constructs a forecaster for a time series assuming ARIMA model.
- ARIMAForecast(IntTimeTimeSeries, int, int, int, double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast
-
Constructs a forecaster for a time series assuming ARIMA model.
- ARIMAForecast.Forecast - Class in dev.nm.stat.timeseries.linear.univariate.arima
-
The forecast value and variance.
- ARIMAForecastMultiStep - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARIMA model.
- ARIMAModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model.
- ARIMAModel(double, double[], int, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model with unit variance.
- ARIMAModel(double[], int, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model with zero-intercept (mu).
- ARIMAModel(double[], int, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model with unit variance and zero-intercept (mu).
- ARIMAModel(ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
-
Copy constructor.
- ARIMASim - Class in dev.nm.stat.timeseries.linear.univariate.arima
-
This class simulates an ARIMA (AutoRegressive Integrated Moving Average) process.
- ARIMASim(ARIMAModel, double[], double[], RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
-
Construct an ARIMA model.
- ARIMASim(ARIMAModel, RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
-
Construct an ARIMA model.
- ARIMASim(ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
-
Construct an ARIMA model, using random standard Gaussian innovations.
- ARIMAXModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model.
- ARIMAXModel(double, double[], int, double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model with unit variance.
- ARIMAXModel(double[], int, double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model with zero-intercept (mu).
- ARIMAXModel(double[], int, double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model with unit variance and zero-intercept (mu).
- ARIMAXModel(ARIMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Copy constructor.
- ARMAFit - Interface in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
-
This interface represents a fitting method for estimating φ, θ, μ, σ2 in an ARMA model.
- ARMAForecast - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
-
Forecasts an ARMA time series using the innovative algorithm.
- ARMAForecast(IntTimeTimeSeries, ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Constructs a forecaster for a time series assuming ARMA model.
- ARMAForecast(IntTimeTimeSeries, int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Constructs a forecaster for a time series assuming ARMA model.
- ARMAForecastMultiStep - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARMA model.
- ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int, InnovationsAlgorithm) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARMA model.
- ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARMA model.
- ARMAForecastMultiStep(double[], ARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the h-step ahead prediction for an ARMA model.
- ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the one-step ahead prediction for an ARMA model.
- ARMAForecastOneStep - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Makes the one-step ahead prediction for an ARMA model.
- ARMAForecastOneStep(IntTimeTimeSeries, ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Makes the one-step ahead prediction for an ARMA model.
- ARMAForecastOneStep(double[], ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Makes the one-step ahead prediction for an ARMA model.
- ARMAGARCHFit - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
Constructs a model.
- ARMAGARCHFit(double[], int, int, int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- ARMAGARCHModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
-
Construct a univariate ARMA-GARCH model.
- ARMAModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
-
A univariate ARMA model, Xt, takes this form.
- ARMAModel(double, double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model.
- ARMAModel(double, double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model with unit variance.
- ARMAModel(double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model with zero-intercept (mu).
- ARMAModel(double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model with unit variance and zero-intercept
(mu).
- ARMAModel(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Copy constructor.
- armaxMean(Matrix, Matrix, Vector) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Compute the multivariate ARMAX conditional mean.
- armaxMean(double[], double[], double[]) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Compute the univariate ARMAX conditional mean.
- ARMAXModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model.
- ARMAXModel(double, double[], double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model with unit variance.
- ARMAXModel(double[], double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model with zero-intercept (mu).
- ARMAXModel(double[], double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model with unit variance and zero-intercept (mu).
- ARMAXModel(ARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Copy constructor.
- ARModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
-
This class represents an AR model.
- ARModel(double, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model.
- ARModel(double, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model with unit variance.
- ARModel(double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model with zero-intercept (mu).
- ARModel(double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model with unit variance and zero-intercept (mu).
- ARModel(ARModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Copy constructor.
- ArrayUtils - Class in dev.nm.misc
-
- ARResamplerFactory - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
-
- ARResamplerFactory(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
-
- ARResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
-
- ARTIFICIAL - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- ARTIFICIAL_COST - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- AS159 - Class in dev.nm.stat.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 dev.nm.stat.test.distribution.pearson.AS159
-
Constructs a random table generator according to the row and column totals.
- AS159(int[], int[], RandomLongGenerator) - Constructor for class dev.nm.stat.test.distribution.pearson.AS159
-
Constructs a random table generator according to the row and column totals.
- AS159.RandomMatrix - Class in dev.nm.stat.test.distribution.pearson
-
a random matrix generated by AS159 and its probability
- asArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Cast this data structure as a double[]
.
- asec(double) - Static method in class dev.nm.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 dev.nm.geometry.TrigMath
-
Returns the arc hyperbolic secant of a value.
- asin(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Inverse of sine.
- asinh(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the arc hyperbolic sine of a value.
- assertEqual(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test if
Number
x
equal to
bound
.
- assertEqual(T, T, String, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test if two
Number
s
x1
and
x2
are equal.
- assertFalse(boolean, String, Object...) - Static method in class dev.nm.misc.ArgumentAssertion
-
Check if an argument condition
is false.
- assertGreaterThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test if
Number
x
is greater than
bound
.
- assertLessThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test if
Number
x
is less than
bound
.
- assertNegative(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
- assertNonNegative(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test if
Number
x
is non-negative.
- assertNonPositive(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test if
Number
x
is non-positive.
- assertNormalDouble(double, String) - Static method in class dev.nm.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 dev.nm.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 dev.nm.misc.ArgumentAssertion
-
Test if
Number
x
is not greater than
bound
.
- assertNotInfinity(double, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
- assertNotInfinity(float, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
- assertNotLessThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test if
Number
x
is not less than
bound
.
- assertNotNaN(double, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
- assertNotNaN(float, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
- assertNotNull(Object, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Check if obj
is not null
.
- assertNull(Object, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Check if obj
is null
.
- assertPositive(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
- assertRange(T, T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test whether the specified
Number
occurs within the range [
low
,
high
]
(both inclusive).
- assertRangeLeftOpen(T, T, T, String) - Static method in class dev.nm.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 dev.nm.misc.ArgumentAssertion
-
Test whether the specified
Number
occurs within the range (
low
,
high
)
(both exclusive).
- assertRangeRightOpen(T, T, T, String) - Static method in class dev.nm.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 dev.nm.misc.ArgumentAssertion
-
Check if an argument condition
is true.
- asymptoticCDF(double) - Static method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
This is the asymptotic distribution of the Kolmogorov distribution.
- asymptoticCDF(double, double) - Static method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
This is the asymptotic distribution of the one-sided Kolmogorov distribution.
- atan(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Inverse of tangent.
- atanh(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the arc hyperbolic tangent of a value.
- ATOMIC_MASS_MU - Static variable in class dev.nm.misc.PhysicalConstants
-
The atomic mass constant \(m_u\) in kilograms (kg).
- AtThreshold - Class in dev.nm.misc.algorithm.stopcondition
-
Stops when the value reaches a given value with a given precision.
- AtThreshold(double, double) - Constructor for class dev.nm.misc.algorithm.stopcondition.AtThreshold
-
Stops when the value reaches a given value with a given precision.
- AugmentedDickeyFuller - Class in dev.nm.stat.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 dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
-
Performs the Augmented Dickey-Fuller test to test for the existence of unit root.
- AugmentedDickeyFuller(double[]) - Constructor for class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
-
Performs the Augmented Dickey-Fuller test to test for the existence of unit root.
- AutoARIMAFit - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
Automatically selects and estimates the ARIMA model using custom parameters.
- AutoARIMAFit(double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
Automatically selects and estimates the ARIMA model using default parameters.
- AutoCorrelation - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
Compute the auto-correlation function for an ARMA model.
- AutoCorrelationFunction - Class in dev.nm.stat.timeseries.linear.univariate
-
This is the auto-correlation function of a univariate time series {xt}.
- AutoCorrelationFunction() - Constructor for class dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction
-
- AutoCovariance - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
Computes the auto-covariance function for an ARMA model.
- AutoCovarianceFunction - Class in dev.nm.stat.timeseries.linear.univariate
-
This is the auto-covariance function of a univariate time series {xt}.
- AutoCovarianceFunction() - Constructor for class dev.nm.stat.timeseries.linear.univariate.AutoCovarianceFunction
-
- autoEpsilon(double...) - Static method in class dev.nm.misc.PrecisionUtils
-
Guess a reasonable precision parameter.
- autoEpsilon(double[]...) - Static method in class dev.nm.misc.PrecisionUtils
-
Guess a reasonable precision parameter.
- autoEpsilon(MatrixTable) - Static method in class dev.nm.misc.PrecisionUtils
-
Guess a reasonable precision parameter.
- AutoParallelMatrixMathOperation - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- AutoParallelMatrixMathOperation(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- AverageImplicitModelPCA - Class in dev.nm.stat.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 dev.nm.stat.factor.implicitmodelpca.AverageImplicitModelPCA
-
Constructs an explicit-implicit model for a time series of vectored
observations
- AverageImplicitModelPCA(Matrix, double) - Constructor for class dev.nm.stat.factor.implicitmodelpca.AverageImplicitModelPCA
-
Constructs an explicit-implicit model for a time series of vectored
observations
- avers(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the arc versine of a value; the returned angle is in the range zero through
pi.
- avg_duration1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
-
- avg_duration2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
-
- AVOGADRO_NA - Static variable in class dev.nm.misc.PhysicalConstants
-
The Avogadro constant \(N_A\), \(L\) in units per mole (mol-1).
- B() - Method in interface dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
-
- B() - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the non-homogeneous part, the right-hand side vector, of the linear system.
- B() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow
-
Get the double precision matrix.
- b() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
Gets the height (y-axis) of the rectangular region.
- b() - Method in class dev.nm.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 dev.nm.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 dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision
-
Get b as in the remainder (b * (x + u) + a).
- B(int, double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
-
Compute bn.
- b() - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
-
Get b.
- b() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
Get the constraint values.
- b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Gets b.
- b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Gets the objective vector, b, in the compact form.
- b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets b.
- b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets b.
- b() - Method in interface dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- B - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- b() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- b() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- B() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets B, the implicit factor loading matrix.
- b(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets the implicit factor loading for the n-th subject.
- B() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets B, the factor loading matrix.
- b(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets the factor loading for the n-th subject.
- B() - Method in class dev.nm.stat.hmm.discrete.DiscreteHMM
-
Gets the conditional probabilities of the observation symbols: rows
correspond to state; columns corresponds symbols.
- b() - Method in interface dev.nm.stat.random.variancereduction.ControlVariates.Estimator
-
Gets the optimal b.
- B(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the Brownian motion value at the i-th time point.
- B() - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal
-
- B() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets B
as in eq.
- b1() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
-
Gets the GARCH coefficient.
- backSearch(Matrix) - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
-
- backSearch(Matrix, int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
-
- backward(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
-
Perform a backward sweep.
- BackwardElimination - Class in dev.nm.stat.regression.linear.glm.modelselection
-
Constructs a GLM model for a set of observations using the backward elimination method.
- BackwardElimination(GLMProblem) - Constructor for class dev.nm.stat.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 dev.nm.stat.regression.linear.glm.modelselection.BackwardElimination
-
Constructs a GLM model using the backward elimination method.
- BackwardElimination.Step - Interface in dev.nm.stat.regression.linear.glm.modelselection
-
- BackwardSubstitution - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
- BanachSpace<B,F extends Field<F> & Comparable<F>> - Interface in dev.nm.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 dev.nm.stat.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 dev.nm.stat.test.variance.Bartlett
-
Perform the Bartlett test to test for equal variances across the groups.
- base() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow
-
Get the radix or base of the coefficient.
- basis() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the kernel basis.
- Basis - Class in dev.nm.algebra.linear.vector.doubles.operation
-
A basis is a set of linearly independent vectors spanning a vector space.
- Basis(int, int) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Basis
-
Construct a vector that corresponds to the i-th dimension in Rn.
- basisAndFreeVars() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the kernel basis and the associated free variables for each basis/column.
- BaumWelch - Class in dev.nm.stat.hmm.discrete
-
This implementation trains an HMM model by observations using the Baum–Welch
algorithm.
- BaumWelch(int[], DiscreteHMM, int) - Constructor for class dev.nm.stat.hmm.discrete.BaumWelch
-
Constructs an HMM model by training an initial model using the Baum–Welch
algorithm.
- BBNode - Interface in dev.nm.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 dev.nm.interval.Interval
-
Get the beginning of this interval.
- BEGINNING_OF_TIME - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
This represents a time before all (representable) times.
- BEGINNING_OF_TIME_LONG - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
This represents a time before all (representable) times, in long
representation.
- beq() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the values, beq, of the equality constraints Aeq * x ≥ beq.
- beq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- beq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- beq() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- BernoulliTrial - Class in dev.nm.stat.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 dev.nm.stat.random.rng.univariate.BernoulliTrial
-
- Best1Bin - Class in dev.nm.solver.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 dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best1Bin
-
Construct an instance of Best1Bin
.
- Best2Bin - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
-
The Best-1-Bin rule always picks the best chromosome as the base.
- Best2Bin(double, double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin
-
Construct an instance of Best2Bin
.
- Best2Bin.DeBest2BinCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
-
- beta - Variable in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
β = 2 / v'v.
- beta() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
-
Gets the value of the wave coefficient β
- beta() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
-
Get the value of the wave coefficient β
- beta() - Method in class dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
-
Gets the coefficient in the PDE (thermal diffusivity in case of the heat equation).
- Beta - Class in dev.nm.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 dev.nm.analysis.function.special.beta.Beta
-
- beta() - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the set of cointegrating factors, by columns.
- beta(int) - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the r-th cointegrating factor, counting from 1.
- beta - Variable in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
-
β: the shape parameter
- beta() - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
-
Gets the GLM coefficients estimator, β^.
- beta() - Method in class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
Gets the GLM coefficient estimator, β^.
- beta() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
-
- beta() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP
-
- beta() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
-
- beta() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP
-
- beta() - Method in interface dev.nm.stat.regression.linear.LinearModel
-
Gets \(\hat{\beta}\) and statistics.
- beta() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
-
- beta() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
-
- beta() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Gets the GARCH coefficient.
- beta() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the GARCH coefficients.
- beta - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- beta() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- BetaDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.BetaDistribution
-
Construct a Beta distribution.
- betaHat() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
-
Gets the estimates of β, β^, as in
E(Y) = μ = g-1(Xβ)
- betaHat() - Method in class dev.nm.stat.regression.linear.glm.IWLS
-
- betaHat() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
- betaHat() - Method in class dev.nm.stat.regression.linear.LMBeta
-
Gets the coefficient estimates, β^.
- BetaMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
-
The HMM states use the Beta distribution to model the observations.
- BetaMixtureDistribution(BetaMixtureDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
-
Constructs a Beta distribution for each state in the HMM model.
- BetaMixtureDistribution(BetaMixtureDistribution.Lambda[], int) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
-
Constructs a Beta distribution for each state in the HMM model.
- BetaMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
-
the Beta distribution parameters
- BetaRegularized - Class in dev.nm.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 dev.nm.analysis.function.special.beta.BetaRegularized
-
Construct an instance of Ix(p,q) with the parameters p and q.
- BetaRegularizedInverse - Class in dev.nm.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 dev.nm.analysis.function.special.beta.BetaRegularizedInverse
-
Construct an instance of \(I^{-1}_{(p,q)}(u)\) with parameters p and p.
- betas() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the sequence of betas.
- BFGSImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer.BFGSImpl
-
- BFGSMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
-
Construct a multivariate minimizer using the BFGS method.
- BFGSMinimizer.BFGSImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
-
an implementation of the BFGS algorithm
- BFS<V> - Class in dev.nm.graph.algorithm.traversal
-
This class implements the breadth-first-search using iteration.
- BFS(Graph<V, ? extends Edge<V>>) - Constructor for class dev.nm.graph.algorithm.traversal.BFS
-
Constructs a BFS tree of a graph.
- BFS(Graph<W, ? extends Edge<V>>, V, int) - Static method in class dev.nm.graph.algorithm.traversal.BFS
-
Runs the breadth-first-search on a graph from a designated root.
- BFS.Node<V> - Class in dev.nm.graph.algorithm.traversal
-
This is a node in a BFS-spanning tree.
- bias(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
Computes the amount of deviation from neutrality, hence bias.
- bias(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
Computes the amount of deviation from self financing, hence bias.
- bias(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
-
Computes the amount of deviation from zero value, hence bias.
- BIC() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMInformationCriteria
-
Gets the Bayesian information criterion.
- BiconjugateGradientSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
Construct a Biconjugate Gradient (BiCG) solver.
- BiconjugateGradientSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
Construct a Biconjugate Gradient (BiCG) solver.
- BiconjugateGradientStabilizedSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
- BiconjugateGradientStabilizedSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
- BicubicInterpolation - Class in dev.nm.analysis.curvefit.interpolation.bivariate
-
Bicubic interpolation is the two-dimensional equivalent of cubic Hermite spline interpolation.
- BicubicInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
-
- BicubicInterpolation(BicubicInterpolation.PartialDerivatives) - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
-
Create a new instance which uses the given derivatives to interpolate.
- BicubicInterpolation.PartialDerivatives - Interface in dev.nm.analysis.curvefit.interpolation.bivariate
-
Specify the partial derivatives defined at points on a
BivariateGrid
.
- BicubicSpline - Class in dev.nm.analysis.curvefit.interpolation.bivariate
-
Bicubic splines are the two-dimensional equivalent of cubic splines.
- BicubicSpline() - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicSpline
-
- BiDiagonalization - Interface in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization
-
This implementation uses Golub-Kahan-Lanczos algorithm with reorthogonalization.
- BiDiagonalizationByGolubKahanLanczos(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
-
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
- BiDiagonalizationByGolubKahanLanczos(Matrix, RandomLongGenerator) - Constructor for class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
-
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
- BiDiagonalizationByHouseholder - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
-
Runs the Householder bi-diagonalization for a tall matrix.
- BidiagonalMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
-
A bi-diagonal matrix is either upper or lower diagonal.
- BidiagonalMatrix(double[][]) - Constructor for class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Constructs a 0 bi-diagonal matrix of dimension dim * dim.
- BidiagonalMatrix(BidiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Copy constructor.
- BidiagonalMatrix.BidiagonalMatrixType - Enum in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
-
the available types of bi-diagonal matrices
- BidiagonalSVDbyMR3 - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
Creates a singular value decomposition for a bidiagonal matrix A.
- BigDecimalUtils - Class in dev.nm.number.big
-
These are the utility functions to manipulate
BigDecimal
.
- bigDecimalValue() - Method in class dev.nm.number.ScientificNotation
-
- BigIntegerUtils - Class in dev.nm.number.big
-
These are the utility functions to manipulate
BigInteger
.
- BilinearInterpolation - Class in dev.nm.analysis.curvefit.interpolation.bivariate
-
Bilinear interpolation is the 2-dimensional equivalent of linear interpolation.
- BilinearInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BilinearInterpolation
-
- bin(MultinomialRVG) - Static method in class dev.nm.stat.markovchain.SimpleMC
-
Picks the first non-empty bin.
- BinomialDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.BinomialDistribution
-
Construct a Binomial distribution.
- BinomialMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
-
The HMM states use the Binomial distribution to model the observations.
- BinomialMixtureDistribution(BinomialMixtureDistribution.Lambda[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
-
Constructs a Binomial distribution for each state in the HMM model.
- BinomialMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
-
the Binomial distribution parameters
- BinomialRNG - Class in dev.nm.stat.random.rng.univariate
-
This random number generator samples from the binomial distribution.
- BinomialRNG(int, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.BinomialRNG
-
Construct a random number generator to sample from the binomial distribution.
- BinomialRNG(int, double) - Constructor for class dev.nm.stat.random.rng.univariate.BinomialRNG
-
Construct a random number generator to sample from the binomial distribution.
- Bins<T> - Class in dev.nm.misc.algorithm
-
This class divides the items based on their keys into a number of bins.
- Bins(int) - Constructor for class dev.nm.misc.algorithm.Bins
-
Constructs an empty bin of valued items.
- Bins(int, Map<Double, T>) - Constructor for class dev.nm.misc.algorithm.Bins
-
Constructs a bin with valued items.
- bins - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
bin allocation; the j(kappa) defined between equations 33 and 34 in
paper 2016
- BisectionRoot - Class in dev.nm.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 dev.nm.analysis.root.univariate.BisectionRoot
-
- BisectionRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.BisectionRoot
-
Create an instance with the tolerance for convergence and the maximum number of iterations.
- BivariateArrayGrid - Class in dev.nm.analysis.curvefit.interpolation.bivariate
-
- BivariateArrayGrid(double[][], double[], double[]) - Constructor for class dev.nm.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 dev.nm.stat.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 dev.nm.stat.evt.evd.bivariate
-
The bivariate asymmetric logistic model.
- BivariateEVDAsymmetricLogistic(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- BivariateEVDAsymmetricLogistic(double, double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- BivariateEVDAsymmetricLogistic(double, double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- BivariateEVDAsymmetricLogistic(double, double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- BivariateEVDAsymmetricMixed - Class in dev.nm.stat.evt.evd.bivariate
-
The asymmetric mixed model.
- BivariateEVDAsymmetricMixed(double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- BivariateEVDAsymmetricMixed(double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- BivariateEVDAsymmetricMixed(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- BivariateEVDAsymmetricNegativeLogistic - Class in dev.nm.stat.evt.evd.bivariate
-
The bivariate asymmetric negative logistic model.
- BivariateEVDAsymmetricNegativeLogistic(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- BivariateEVDAsymmetricNegativeLogistic(double, double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- BivariateEVDAsymmetricNegativeLogistic(double, double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- BivariateEVDAsymmetricNegativeLogistic(double, double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- BivariateEVDBilogistic - Class in dev.nm.stat.evt.evd.bivariate
-
The bilogistic model.
- BivariateEVDBilogistic(double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- BivariateEVDBilogistic(double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- BivariateEVDBilogistic(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- BivariateEVDColesTawn - Class in dev.nm.stat.evt.evd.bivariate
-
The Coles-Tawn model.
- BivariateEVDColesTawn(double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- BivariateEVDColesTawn(double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- BivariateEVDColesTawn(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- BivariateEVDHuslerReiss - Class in dev.nm.stat.evt.evd.bivariate
-
The Husler-Reiss model.
- BivariateEVDHuslerReiss(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- BivariateEVDHuslerReiss(double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- BivariateEVDHuslerReiss(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- BivariateEVDLogistic - Class in dev.nm.stat.evt.evd.bivariate
-
The bivariate logistic model.
- BivariateEVDLogistic(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- BivariateEVDLogistic(double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- BivariateEVDLogistic(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- BivariateEVDNegativeBilogistic - Class in dev.nm.stat.evt.evd.bivariate
-
The negative bilogistic model.
- BivariateEVDNegativeBilogistic(double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- BivariateEVDNegativeBilogistic(double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- BivariateEVDNegativeBilogistic(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- BivariateEVDNegativeLogistic - Class in dev.nm.stat.evt.evd.bivariate
-
The bivariate negative logistic model.
- BivariateEVDNegativeLogistic(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- BivariateEVDNegativeLogistic(double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- BivariateEVDNegativeLogistic(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- BivariateGrid - Interface in dev.nm.analysis.curvefit.interpolation.bivariate
-
A rectilinear (meaning that grid lines are not necessarily equally-spaced) bivariate
grid of double
values.
- BivariateGridInterpolation - Interface in dev.nm.analysis.curvefit.interpolation.bivariate
-
A bivariate interpolation, which requires the input to form a rectilinear grid.
- BivariateProbabilityDistribution - Interface in dev.nm.stat.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 dev.nm.analysis.function.rn2r1
-
A bivariate real function takes two real arguments and outputs one real value.
- BivariateRegularGrid - Class in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Computes the splitting points with the given threshold.
- BlockSplitPointSearch(double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.BlockSplitPointSearch
-
- BlockWinogradAlgorithm - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.BlockWinogradAlgorithm
-
- BMSDE - Class in dev.nm.stat.stochasticprocess.univariate.sde.discrete
-
A Brownian motion is a stochastic process with the following properties.
- BMSDE(double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
-
Construct a univariate Brownian motion.
- BMSDE() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
-
Construct a univariate standard Brownian motion.
- BoltzAnnealingFunction - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
-
Constructs a new instance where the RVG is created from a given RLG.
- BoltzAnnealingFunction(RandomVectorGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
-
Constructs a new instance that uses a given RVG.
- BOLTZMANN_K - Static variable in class dev.nm.misc.PhysicalConstants
-
The Boltzmann constant \(k\) in joule per kelvin (J K-1).
- BoltzTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
-
\(T_k = T_0 / ln(k)\).
- BoltzTemperatureFunction(double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.BoltzTemperatureFunction
-
Constructs a new instance with an initial temperature.
- BootstrapEstimator - Class in dev.nm.stat.random.sampler.resampler
-
This class estimates the statistic of a sample using a bootstrap method.
- BootstrapEstimator(Resampler, StatisticFactory, int) - Constructor for class dev.nm.stat.random.sampler.resampler.BootstrapEstimator
-
Constructs a bootstrap estimator.
- BootstrapEstimator(Resampler, StatisticFactory, int, boolean) - Constructor for class dev.nm.stat.random.sampler.resampler.BootstrapEstimator
-
Constructs a bootstrap estimator.
- BorderedHessian - Class in dev.nm.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 dev.nm.analysis.differentiation.multivariate.BorderedHessian
-
Construct the bordered Hessian matrix for multivariate functions f and g at
point x.
- BottomUp<V> - Class in dev.nm.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 dev.nm.graph.algorithm.traversal.BottomUp
-
Constructs a BottomUp traversal instance.
- bound(double, double, double) - Static method in class dev.nm.number.DoubleUtils
-
Bounds a given value by a given range.
- Bound(int, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
Construct a bound constraint for a variable.
- bounds() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
Get a deep copy of the bounds.
- BoxConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
-
This represents the lower and upper bounds for a variable.
- BoxConstraints(int, BoxConstraints.Bound...) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
Construct a set of bound constraints.
- BoxConstraints(Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
Construct a set of bound constraints.
- BoxConstraints.Bound - Class in dev.nm.solver.multivariate.constrained.constraint.linear
-
A bound constraint for a variable.
- BoxGeneralizedSimulatedAnnealingMinimizer - Class in dev.nm.solver.multivariate.constrained.general.box
-
- BoxGeneralizedSimulatedAnnealingMinimizer(int, double, double, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
- BoxGeneralizedSimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
- BoxGeneralizedSimulatedAnnealingMinimizer(int, StopCondition) - Constructor for class dev.nm.solver.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
- BoxGSAAcceptanceProbabilityFunction - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.BoxGSAAcceptanceProbabilityFunction
-
Constructs a boxed acceptance probability function.
- BoxGSAAnnealingFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
-
- BoxGSAAnnealingFunction(Vector, Vector, double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoxGSAAnnealingFunction
-
Constructs a boxed annealing function.
- BoxMinimizer<P extends BoxOptimProblem,S extends MinimizationSolution<?>> - Interface in dev.nm.solver.multivariate.constrained
-
- BoxMuller - Class in dev.nm.stat.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 dev.nm.stat.random.rng.univariate.normal.BoxMuller
-
Construct a random number generator to sample from the standard Normal distribution.
- BoxMuller() - Constructor for class dev.nm.stat.random.rng.univariate.normal.BoxMuller
-
Construct a random number generator to sample from the standard Normal distribution.
- BoxOptimProblem - Class in dev.nm.solver.multivariate.constrained.problem
-
A box constrained optimization problem, for which a solution must be within fixed bounds.
- BoxOptimProblem(RealScalarFunction, BoxConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
Constructs an optimization problem with box constraints.
- BoxOptimProblem(RealScalarFunction, Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
Constructs an optimization problem with box constraints.
- BoxOptimProblem(BoxOptimProblem) - Constructor for class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
Copy constructor.
- BoxPierce - Class in dev.nm.stat.test.timeseries.portmanteau
-
- BoxPierce(double[], int, int) - Constructor for class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
Perform the Box-Pierce test to check auto-correlation in a time series.
- BracketSearchMinimizer - Class in dev.nm.solver.univariate.bracketsearch
-
This class provides implementation support for those univariate optimization algorithms that are based on bracketing.
- BracketSearchMinimizer(double, int) - Constructor for class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer
-
Construct a univariate minimizer using a bracket search method.
- BracketSearchMinimizer.Solution - Class in dev.nm.solver.univariate.bracketsearch
-
- BranchAndBound - Class in dev.nm.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 dev.nm.misc.algorithm.bb.BranchAndBound
-
Solve a minimization problem using a branch-and-bound algorithm.
- BranchAndBound(BBNode) - Constructor for class dev.nm.misc.algorithm.bb.BranchAndBound
-
Solve a minimization problem using a branch-and-bound algorithm using depth-first search.
- branching() - Method in interface dev.nm.misc.algorithm.bb.BBNode
-
Get the children of this node by using the branching operation.
- branching() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
Get the children of this node by using the branching operation.
- BrentCetaMaximizer - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
-
- BrentCetaMaximizer(double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
Constructs a maximizer with a given ε (for the Brent's search
algorithm).
- BrentCetaMaximizer() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
- BrentMinimizer - Class in dev.nm.solver.univariate.bracketsearch
-
Brent's algorithm is the preferred method for finding the minimum of a univariate function.
- BrentMinimizer(double, int) - Constructor for class dev.nm.solver.univariate.bracketsearch.BrentMinimizer
-
Construct a univariate minimizer using Brent's algorithm.
- BrentMinimizer.Solution - Class in dev.nm.solver.univariate.bracketsearch
-
This is the solution to a Brent's univariate optimization.
- BrentRoot - Class in dev.nm.analysis.root.univariate
-
Brent's root-finding algorithm combines super-linear convergence with reliability of bisection.
- BrentRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.BrentRoot
-
Construct an instance of Brent's root finding algorithm.
- BreuschPagan - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
-
The Breusch-Pagan test tests for conditional heteroskedasticity.
- BreuschPagan(LMResiduals, boolean) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.BreuschPagan
-
Perform the Breusch-Pagan test to test for heteroskedasticity in a linear regression model.
- BrownForsythe - Class in dev.nm.stat.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 dev.nm.stat.test.variance.BrownForsythe
-
Perform the Brown-Forsythe test to test for equal variances across the groups.
- BruteForce<D,R> - Class in dev.nm.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 dev.nm.misc.algorithm.BruteForce
-
Constructs a brute force search for a function.
- BruteForceIPMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
-
Constructs a brute force minimizer to solve integral constrained
minimization problems.
- BruteForceIPMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
-
Constructs a brute force minimizer to solve integral constrained
minimization problems.
- BruteForceIPMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.integer.bruteforce
-
This is the solution to an integral constrained minimization using the
brute-force search.
- BruteForceIPProblem - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
-
Construct an integral constrained minimization problem with explicit integral domains.
- BruteForceIPProblem(RealScalarFunction, BruteForceIPProblem.IntegerDomain[], double) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
-
Construct an integral constrained minimization problem with explicit integral domains.
- BruteForceIPProblem.IntegerDomain - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained
-
This implementation solves an unconstrained minimization problem by
brute force search for all given possible values.
- BruteForceMinimizer(boolean) - Constructor for class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer
-
- BruteForceMinimizer.Solution - Class in dev.nm.solver.multivariate.unconstrained
-
This is the solution to solving an optimization using the brute force
algorithm.
- Bt - Class in dev.nm.stat.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 dev.nm.stat.stochasticprocess.univariate.filtration.Bt
-
- Bt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the entire Brownian path.
- build(double) - Method in class tech.nmfin.meanreversion.hvolatility.Kagi
-
Makes a KAGI construction for the given random process.
- build() - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
-
- Builder(Dai2011HMM, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
-
- BurlischStoerExtrapolation - Class in dev.nm.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 dev.nm.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 dev.nm.stat.random.rng.univariate
-
A burn-in random number generator discards the first M samples.
- BurnInRNG(RandomNumberGenerator, int) - Constructor for class dev.nm.stat.random.rng.univariate.BurnInRNG
-
Construct a burn-in RNG.
- BurnInRVG - Class in dev.nm.stat.random.rng.multivariate
-
A burn-in random number generator discards the first M samples.
- BurnInRVG(RandomVectorGenerator, int) - Constructor for class dev.nm.stat.random.rng.multivariate.BurnInRVG
-
Construct a burn-in RVG.
- c() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets the value of c.
- c() - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
-
Get c.
- C() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Gets C.
- C() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
-
Gets C.
- c(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets ci.
- c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets c.
- c() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
-
Get the objective function.
- c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- c() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- c - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
n=c/p
- c() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- c(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
-
- C() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets C
as in eq.
- C0() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
-
Get the covariance matrix of x0.
- C0() - Method in class dev.nm.stat.dlm.univariate.DLM
-
Get the variance of x0.
- c1() - Method in class dev.nm.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 dev.nm.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 dev.nm.analysis.differentiation.differentiability
-
A function, f, is said to be of class C1 if the derivative f' exists.
- c2() - Method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.solver.problem
-
This is an optimization problem of a real valued function that is twice differentiable.
- C2OptimProblemImpl - Class in dev.nm.solver.problem
-
This is an optimization problem of a real valued function: \(\max_x f(x)\).
- C2OptimProblemImpl(RealScalarFunction, RealVectorFunction, RntoMatrix) - Constructor for class dev.nm.solver.problem.C2OptimProblemImpl
-
Construct an optimization problem with an objective function.
- C2OptimProblemImpl(RealScalarFunction, RealVectorFunction) - Constructor for class dev.nm.solver.problem.C2OptimProblemImpl
-
Construct an optimization problem with an objective function.
- C2OptimProblemImpl(RealScalarFunction) - Constructor for class dev.nm.solver.problem.C2OptimProblemImpl
-
Construct an optimization problem with an objective function.
- C2OptimProblemImpl(C2OptimProblemImpl) - Constructor for class dev.nm.solver.problem.C2OptimProblemImpl
-
Copy Ctor.
- CalibrationParam(double, double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
-
- CartesianProduct<T> - Class in dev.nm.misc.algorithm
-
The Cartesian product can be generalized to the n-ary Cartesian product over
n sets X1, ..., Xn.
- CartesianProduct(T[]...) - Constructor for class dev.nm.misc.algorithm.CartesianProduct
-
Construct an
Iterable
of all combinations of arrays, taking one
element from each array.
- CaseResamplingReplacement - Class in dev.nm.stat.random.sampler.resampler.bootstrap
-
This is the classical bootstrap method described in the reference.
- CaseResamplingReplacement(double[]) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacement(double[], RandomLongGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacement(double[], ConcurrentCachedRLG) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacementForObject<X> - Class in dev.nm.stat.random.sampler.resampler.bootstrap
-
This is the classical bootstrap method described in the reference.
- CaseResamplingReplacementForObject(X[], Class<X>) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacementForObject(X[], Class<X>, RandomLongGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
Constructs a bootstrap sample generator.
- CaseResamplingReplacementForObject(X[], Class<X>, ConcurrentCachedRLG) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
Constructs a bootstrap sample generator.
- CauchyPolynomial - Class in dev.nm.analysis.function.polynomial
-
The Cauchy's polynomial of a polynomial takes this form:
- CauchyPolynomial(Polynomial) - Constructor for class dev.nm.analysis.function.polynomial.CauchyPolynomial
-
- cbind(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of vectors by columns.
- cbind(SparseVector...) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines a list of vectors by columns.
- cbind(Matrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of matrices by columns.
- cbind(SparseMatrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of sparse matrices by columns.
- ccdf(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
- ccdf(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
-
The complementary cumulative distribution function.
- cdf(Vector) - Method in class dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
-
- cdf(double, double) - Method in interface dev.nm.stat.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 dev.nm.stat.distribution.multivariate.DirichletDistribution
-
- cdf(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
-
- cdf(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
- cdf(Vector) - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the cumulative probability F(x) = Pr(X ≤ x).
- cdf(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
-
Gets the cumulative probability F(x) = Pr(X ≤ x).
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
-
- cdf(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
Gets the cumulative probability F(x) = Pr(X ≤ x).
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
- cdf(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
-
- cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
Gets the cumulative probability F(x) = Pr(X ≤ x).
- cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
- cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
-
The cumulative distribution function.
- cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
The cumulative distribution function.
- cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
- cdf(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
- cdf(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
- cdf(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
- cdf(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
- cdf(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- cdf(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- centralMoment(int) - Method in class dev.nm.stat.descriptive.moment.Moments
-
Get the value of the k-th central moment.
- CentralPath - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
-
Construct a central path.
- Ceta - Class in tech.nmfin.portfoliooptimization.lai2010.ceta
-
The function C(η) to be maximized (Eq.
- Ceta(Ceta.PortfolioMomentsEstimator, double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta
-
- Ceta.PortfolioMoments - Class in tech.nmfin.portfoliooptimization.lai2010.ceta
-
- Ceta.PortfolioMomentsEstimator - Interface in tech.nmfin.portfoliooptimization.lai2010.ceta
-
- CetaMaximizer - Interface in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
-
Defines an algorithm to search for the maximal C(η).
- CetaMaximizer.NegCetaFunction - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
-
- CetaMaximizer.Solution - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
-
- ChangeOfVariable - Class in dev.nm.analysis.integration.univariate.riemann
-
Change of variable can easy the computation of some integrals, such as improper integrals.
- ChangeOfVariable(SubstitutionRule, Integrator) - Constructor for class dev.nm.analysis.integration.univariate.riemann.ChangeOfVariable
-
Construct an integrator that uses change of variable to do integration.
- CHARACTERISTIC_IMPEDANCE_Z0 - Static variable in class dev.nm.misc.PhysicalConstants
-
The characteristic impedance of vacuum \(Z_0\) in ohms (Ω).
- CharacteristicPolynomial - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
-
The characteristic polynomial of a square matrix is the function
- CharacteristicPolynomial(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
Construct the characteristic polynomial for a square matrix.
- ChebyshevRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
-
- ChebyshevRule(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
Create a Chebyshev rule of the given order.
- checkInputs() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Checks whether this LMProblem
instance is valid.
- checkInputs() - Method in class dev.nm.stat.regression.linear.logistic.LogisticProblem
-
- Cheng1978 - Class in dev.nm.stat.random.rng.univariate.beta
-
Cheng, 1978, is a new rejection method for generating beta variates.
- Cheng1978(double, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.beta.Cheng1978
-
Construct a random number generator to sample from the beta distribution.
- Cheng1978(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.beta.Cheng1978
-
Construct a random number generator to sample from the beta distribution.
- children(V) - Method in interface dev.nm.graph.DiGraph
-
Gets the set of all children of this vertex.
- children(V) - Method in class dev.nm.graph.type.SparseDiGraph
-
- children(V) - Method in class dev.nm.graph.type.SparseTree
-
- children(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- ChiSquareDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.ChiSquareDistribution
-
Construct a Chi-Square distribution.
- ChiSquareIndependenceTest - Class in dev.nm.stat.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 dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
-
Assess whether the two random variables in the contingency table are independent.
- ChiSquareIndependenceTest(Matrix) - Constructor for class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
-
Assess whether the two random variables in the contingency table are independent.
- ChiSquareIndependenceTest.Type - Enum in dev.nm.stat.test.distribution.pearson
-
the available distributions used for the test
- Chol - Class in dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
- CholeskyBanachiewiczParallelized(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized
-
- CholeskySparse - Class in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
Cholesky decomposition works only for a positive definite matrix.
- CholeskyWang2006(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyWang2006
-
Constructs the Cholesky decomposition of a matrix.
- Chromosome - Interface in dev.nm.solver.multivariate.geneticalgorithm
-
A chromosome is a representation of a solution to an optimization problem.
- clamped() - Static method in class dev.nm.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 dev.nm.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 dev.nm.misc.algorithm.bb.ActiveList
-
Removes all of the elements from this collection.
- clear() - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- Cluster(int, int) - Constructor for class dev.nm.stat.evt.cluster.Clusters.Cluster
-
Create a cluster with the beginning and ending indices of the cluster.
- ClusterAnalyzer - Class in dev.nm.stat.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 dev.nm.stat.evt.cluster.ClusterAnalyzer
-
Create an instance with the given threshold value and default interval length value of 1.
- ClusterAnalyzer(double, int) - Constructor for class dev.nm.stat.evt.cluster.ClusterAnalyzer
-
Create an instance with the given threshold and clustering interval length.
- clusters() - Method in class dev.nm.graph.community.GirvanNewman
-
Gets all the clusters, each of which is connected.
- Clusters - Class in dev.nm.stat.evt.cluster
-
Store cluster information obtained by cluster analysis.
- Clusters(double[], List<Clusters.Cluster>, int) - Constructor for class dev.nm.stat.evt.cluster.Clusters
-
- Clusters.Cluster - Class in dev.nm.stat.evt.cluster
-
Define the beginning and ending indices (inclusively) of a cluster.
- Coefficients(ConvectionDiffusionEquation1D, int, int, double[]) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D.Coefficients
-
Constructs the coefficient computation
- Coefficients(HeatEquation1D, int, double, double) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
-
Constructs the coefficient computation
- CointegrationMLE - Class in dev.nm.stat.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 dev.nm.stat.cointegration.CointegrationMLE
-
Perform the Johansen MLE procedure on a multivariate time series.
- CointegrationMLE(MultivariateSimpleTimeSeries, boolean, int) - Constructor for class dev.nm.stat.cointegration.CointegrationMLE
-
Perform the Johansen MLE procedure on a multivariate time series, using
the EIGEN test.
- CointegrationMLE(MultivariateSimpleTimeSeries, boolean) - Constructor for class dev.nm.stat.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 dev.nm.number.DoubleUtils
-
Convert a collection of numbers to a double
array.
- collection2IntArray(Collection<Integer>) - Static method in class dev.nm.number.DoubleUtils
-
Convert a collection of Integer
s to an int
array.
- collection2LongArray(Collection<Long>) - Static method in class dev.nm.number.DoubleUtils
-
Convert a collection of Long
s to a long
array.
- colMeans(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the column means.
- colMeanVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the column mean vector of a given matrix.
- color - Variable in class dev.nm.graph.algorithm.traversal.DFS.Node
-
- color() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node
-
Gets the color of this node.
- colSums(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the column sums.
- colSumVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the column sum vector of a given matrix.
- ColumnBindMatrix - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
A fast "cbind" matrix from vectors.
- ColumnBindMatrix(Vector...) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- columnIndices() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
-
- columns(Matrix, int[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the columns of a matrix.
- columns(Matrix, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the columns of a matrix.
- combination(int, int) - Static method in class dev.nm.analysis.function.FunctionOps
-
Compute the combination function or binomial coefficient.
- combination(int, int) - Static method in class dev.nm.number.big.BigIntegerUtils
-
Compute the combination function or the binomial coefficient.
- CombinedCetaMaximizer - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
-
Searches the maximum C(η) by an array of given maximizers, being
tried in sequence.
- CombinedCetaMaximizer(CetaMaximizer[]) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
-
Constructs a combined maximizer.
- CombinedCetaMaximizer() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
-
Constructs a combined maximizer.
- CombinedVectorByRef - Class in dev.nm.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 dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
-
- commission(double) - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
-
- commission(double, double) - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
-
- CommonRandomNumbers - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry.TopLeftFirstComparator
-
- compare(SparseVector.Entry, SparseVector.Entry) - Method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry.Comparator
-
- compare(Pair, Pair) - Method in class dev.nm.analysis.function.tuple.PairComparatorByAbscissaFirst
-
- compare(Pair, Pair) - Method in class dev.nm.analysis.function.tuple.PairComparatorByAbscissaOnly
-
- compare(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compare two BigDecimal
s up to a precision.
- compare(Number, double) - Method in class dev.nm.number.complex.Complex
-
- compare(double, double, double) - Static method in class dev.nm.number.DoubleUtils
-
Compares two double
s up to a precision.
- compare(Number, double) - Method in interface dev.nm.number.NumberUtils.Comparable
-
Compare this
and that
numbers up to a precision.
- compare(Number, Number, double) - Static method in class dev.nm.number.NumberUtils
-
Compare two numbers.
- compareTo(Pair) - Method in class dev.nm.analysis.function.tuple.Pair
-
- compareTo(GraphTraversal.Node<V>) - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
-
- compareTo(SortableArray) - Method in class dev.nm.misc.datastructure.SortableArray
-
- compareTo(Real) - Method in class dev.nm.number.Real
-
- compareTo(BoxConstraints.Bound) - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
- compareTo(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
- compareTo(PanelData<S>.Row) - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
-
- Complex - Class in dev.nm.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 dev.nm.number.complex.Complex
-
Construct a complex number from the real and imaginary parts.
- Complex(double) - Constructor for class dev.nm.number.complex.Complex
-
Construct a complex number from a real number.
- ComplexMatrix - Class in dev.nm.algebra.linear.matrix.generic.matrixtype
-
- ComplexMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- ComplexMatrix(Complex[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- ComplexMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- CompositeDoubleArrayOperation - Class in dev.nm.number.doublearray
-
It is desirable to have multiple implementations and switch between them for, e.g., performance
reason.
- CompositeDoubleArrayOperation(CompositeDoubleArrayOperation.ImplementationChooser) - Constructor for class dev.nm.number.doublearray.CompositeDoubleArrayOperation
-
Construct a CompositeDoubleArrayOperation
by supplying the multiplexing criterion and
the multiple DoubleArrayOperation
s.
- CompositeDoubleArrayOperation(int, DoubleArrayOperation, DoubleArrayOperation) - Constructor for class dev.nm.number.doublearray.CompositeDoubleArrayOperation
-
Construct a CompositeDoubleArrayOperation
that chooses an implementation by array
length.
- CompositeDoubleArrayOperation.ImplementationChooser - Interface in dev.nm.number.doublearray
-
Specify which implementation to use.
- CompositeLinearCongruentialGenerator - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.BlockSplitPointSearch
-
Searches splitting points in the symmetric tridiagonal matrix.
- compute(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion.MatrixNorm
-
- compute(Matrix) - Method in class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EpsilonStatisticsCalculator
-
Compute the statistics.
- compute() - Method in class tech.nmfin.meanreversion.daspremont2008.AhatEstimation
-
- computeCointegratingBeta(Matrix) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
-
- computeGershgorinIntervals(Vector, Vector) - Static method in class dev.nm.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 tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
-
- computeOptimalPositions(int) - Method in class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
-
Computes the solution to the problem described in Section 3.2 in reference.
- computeOptimalPositions(int) - Method in interface tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueSolver
-
Computes the solution to the problem described in Section 3.2 in reference.
- computePrice(double, double) - Method in interface tech.nmfin.returns.ReturnsCalculator
-
Computes the next price after a return.
- computePrice(double, double) - Method in enum tech.nmfin.returns.ReturnsCalculators
-
- computeReturn(double, double) - Method in interface tech.nmfin.returns.ReturnsCalculator
-
Computes the portfolio return.
- computeReturn(double, double) - Method in enum tech.nmfin.returns.ReturnsCalculators
-
- computeRobustPair(String, String, Vector, Vector) - Method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
-
- computeShortTermCointegratingBeta(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
-
- computeWeightedRSS(ACERFunction.ACERParameter, double[], double[], double[]) - Static method in class dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
-
Compute weights from epsilon values and their corresponding confidence interval half-width.
- computeWeightsByPeriodLength(double[][]) - Static method in class dev.nm.stat.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 dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Concatenates an array of vectors into one vector.
- concat(Collection<Vector>) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Concatenates an array of vectors into one vector.
- concat(SparseVector...) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Concatenates an array of sparse vectors into one sparse vector.
- concat(double[]...) - Static method in class dev.nm.number.DoubleUtils
-
Concatenate an array of arrays into one array.
- concat(LinearConstraints...) - Static method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
Concatenate collections of linear constraints into one collection.
- CONCURRENT_RNORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
-
- ConcurrentCachedGenerator<T> - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.random.rng.concurrent.cache
-
Defines a generic generator of type T
.
- ConcurrentCachedRLG - Class in dev.nm.stat.random.rng.concurrent.cache
-
This is a fast thread-safe wrapper for random long generators.
- ConcurrentCachedRLG(RandomLongGenerator, int) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.random.rng.concurrent.cache
-
This is a fast thread-safe wrapper for random number generators.
- ConcurrentCachedRNG(RandomNumberGenerator, int) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.random.rng.concurrent.cache
-
This is a fast thread-safe wrapper for random vector generators.
- ConcurrentCachedRVG(RandomVectorGenerator, int) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.random.rng.univariate.normal
-
- ConcurrentStandardNormalRNG(RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
-
- ConcurrentStandardNormalRNG() - Constructor for class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
-
- conditionalCopula(double, double) - Method in interface dev.nm.stat.evt.evd.bivariate.BivariateEVD
-
The conditional copula function conditioning on either margin.
- conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- conditionalForEach(boolean, Iterable<T>, IterationBody<T>) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Calls
forEach
only if
conditionToParallelize
is
true
.
- conditionalForLoop(boolean, int, int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Runs a parallel for-loop only if conditionToParallelize
is true
.
- conditionalForLoop(boolean, int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
- conditionalMean(Matrix, Matrix) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Compute the multivariate ARMA conditional mean, given all the lags.
- conditionalMean(double[], double[]) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Compute the univariate ARMA conditional mean, given all the lags.
- conditionalMean(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Compute the univariate AR1 conditional mean, given the last lag.
- ConditionalSumOfSquares - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Fit an ARIMA model for the observations using CSS.
- ConditionalSumOfSquares(double[], int, int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Fit an ARIMA model for the observations using CSS.
- conditionNumber(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Computes the condition number of a given matrix A.
- CONDUCTANCE_QUANTUM_G0 - Static variable in class dev.nm.misc.PhysicalConstants
-
The conductance quantum \(G_0\) in siemens (s).
- ConfidenceInterval - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
-
Create an instance with the confidence interval information.
- confidenceInterval(double) - Method in class dev.nm.stat.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 dev.nm.stat.test.mean.T
-
Get the confidence interval.
- confidenceInterval(double) - Method in class dev.nm.stat.test.variance.F
-
Compute the confidence interval.
- CongruentMatrix - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.CongruentMatrix
-
Constructs the congruent matrix B = P'AP.
- conjugate() - Method in class dev.nm.number.complex.Complex
-
Get the conjugate of the complex number, namely, (a - bi).
- ConjugateGradientMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ConjugateGradientMinimizer
-
Construct a multivariate minimizer using the Conjugate-Gradient method.
- ConjugateGradientNormalErrorSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
Construct a Conjugate Gradient Normal Error (CGNE) solver.
- ConjugateGradientNormalErrorSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
Construct a Conjugate Gradient Normal Error (CGNE) solver.
- ConjugateGradientNormalResidualSolver - Class in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
Construct a Conjugate Gradient Normal Residual method (CGNR) solver.
- ConjugateGradientSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
Construct a Conjugate Gradient (CG) solver.
- ConjugateGradientSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
Construct a Conjugate Gradient (CG) solver.
- ConjugateGradientSquaredSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
Construct a Conjugate Gradient Squared (CGS) solver.
- ConjugateGradientSquaredSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
Construct a Conjugate Gradient Squared (CGS) solver.
- ConstantDriftVector - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
-
The class represents a constant drift function.
- ConstantDriftVector(Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantDriftVector
-
Construct a constant drift function.
- Constants - Class in dev.nm.misc
-
This class lists the global parameters and constants in this nmdev library.
- ConstantSeeder<T extends Seedable> - Class in dev.nm.stat.random.rng
-
A wrapper that seeds each given seedable random number generator with the given seed(s).
- ConstantSeeder(Iterable<T>, long...) - Constructor for class dev.nm.stat.random.rng.ConstantSeeder
-
Constructs a wrapper with the underlying RNGs and the seeds for seeding each RNG.
- ConstantSigma1 - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
-
The class represents a constant diffusion coefficient function.
- ConstantSigma1(Matrix) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
Construct a constant diffusion coefficient function.
- ConstantSigma2 - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
-
- ConstantSigma2(Matrix) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
Construct a constant diffusion coefficient function.
- ConstrainedCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
-
- ConstrainedCellFactory - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
This defines a Differential Evolution operator that takes in account constraints.
- ConstrainedCellFactory(DEOptimCellFactory) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
Construct an instance of a ConstrainedCellFactory
that define the constrained Differential Evolution operators.
- ConstrainedCellFactory.ConstrainedCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
A ConstrainedCell
is a chromosome for a constrained optimization problem.
- ConstrainedLASSObyLARS - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
-
Solves a constrained LASSO problem by modified least angle regression (LARS) and linear
interpolation.
- ConstrainedLASSObyLARS(ConstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
-
Solves a constrained LASSO problem by modified least angle regression (LARS) and linear
interpolation.
- ConstrainedLASSObyQP - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP
-
Solves a constrained LASSO problem by transforming it into a single quadratic programming
problem.
- ConstrainedLASSOProblem - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.lasso.ConstrainedLASSOProblem
-
Constructs a LASSO problem in the constrained form.
- ConstrainedLASSOProblem(ConstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.ConstrainedLASSOProblem
-
Copy constructor.
- ConstrainedMinimizer<P extends ConstrainedOptimProblem,S extends MinimizationSolution<?>> - Interface in dev.nm.solver.multivariate.constrained
-
- ConstrainedOptimProblem - Interface in dev.nm.solver.multivariate.constrained.problem
-
A constrained optimization problem takes this form.
- ConstrainedOptimProblemImpl1 - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
Constructs a constrained optimization problem.
- ConstrainedOptimProblemImpl1(ConstrainedOptimProblemImpl1) - Constructor for class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
Copy constructor.
- ConstrainedOptimSubProblem - Interface in dev.nm.solver.multivariate.constrained
-
A constrained optimization sub-problem takes this form.
- Constraints - Interface in dev.nm.solver.multivariate.constrained.constraint
-
A set of constraints for a (real-valued) optimization problem is a set of functions.
- constraints - Variable in class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
the constraint/cost functions
- constraints() - Method in class tech.nmfin.portfoliooptimization.corvalan2005.constraint.MinimumWeights
-
- constraints() - Method in class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoConstraints
-
- constraints() - Method in class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoShortSelling
-
- constraints() - Method in interface tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005.WeightsConstraint
-
Gets the less-than constraints on weights.
- ConstraintsUtils - Class in dev.nm.solver.multivariate.constrained.constraint
-
These are the utility functions for manipulating
Constraints.
- ConstraintsUtils() - Constructor for class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
-
- ConstraintViolationException() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.ConstraintViolationException
-
Constructs a ConstraintViolationException
.
- ConstraintViolationException(String) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.ConstraintViolationException
-
Constructs a ConstraintViolationException
with an error
message.
- containActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Check if the active set contains a certain index.
- containInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Check if the inactive set contains a certain index.
- contains(UndirectedEdge<V>) - Method in class dev.nm.graph.community.EdgeBetweeness
-
Checks if the graph contains an edge.
- contains(V) - Method in class dev.nm.graph.type.SparseGraph
-
Check if this graph contains a vertex.
- contains(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- containsAll(Collection<?>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- containsEdge(Graph<V, ?>, HyperEdge<V>) - Static method in class dev.nm.graph.GraphUtils
-
Returns true if this graph's edge collection contains e
- containsVertex(Graph<V, ?>, V) - Static method in class dev.nm.graph.GraphUtils
-
Returns true if this graph's vertex collection contains v
- ContextRNG<T> - Class in dev.nm.stat.random.rng.concurrent.context
-
This uniform number generator generates independent sequences of random numbers per context.
- ContextRNG() - Constructor for class dev.nm.stat.random.rng.concurrent.context.ContextRNG
-
- ContinuedFraction - Class in dev.nm.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 dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- ContinuedFraction(ContinuedFraction.Partials, int, int) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- ContinuedFraction(ContinuedFraction.Partials) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- ContinuedFraction.MaxIterationsExceededException - Exception in dev.nm.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 dev.nm.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 dev.nm.stat.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 dev.nm.stat.random.variancereduction.ControlVariates
-
- ControlVariates.Estimator - Interface in dev.nm.stat.random.variancereduction
-
- ConvectionDiffusionEquation1D - Class in dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Constructs a convection-diffusion equation problem.
- ConvergenceFailure - Exception in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
-
- ConvergenceFailure(ConvergenceFailure.Reason) - Constructor for exception dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
-
Construct an exception with reason.
- ConvergenceFailure(ConvergenceFailure.Reason, String) - Constructor for exception dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
-
Construct an exception with reason and error message.
- ConvergenceFailure.Reason - Enum in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
-
the reasons for the convergence failure
- convertToDerivativeFunction(RealVectorFunction, int) - Static method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Converts the given vector function to a first order derivative function.
- cookDistances() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics
-
Cook distances.
- coordinates - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
the coordinates of this entry
- copy2D(double[][]) - Static method in class dev.nm.number.DoubleUtils
-
Copies a 2D array.
- cor(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
-
- cor(double) - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
-
- correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
-
- correlation() - Method in class dev.nm.stat.descriptive.covariance.Covariance
-
Get the correlation, i.e., Pearson's correlation coefficient.
- CorrelationCheck - Class in tech.nmfin.meanreversion.cointegration.check
-
- CorrelationCheck(Matrix, double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
-
- CorrelationCheck(Matrix, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
-
- CorrelationMatrix - Class in dev.nm.stat.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 dev.nm.stat.descriptive.correlation.CorrelationMatrix
-
Construct a correlation matrix from a covariance matrix.
- Corvalan2005 - Class in tech.nmfin.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 tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005
-
Constructs an instance of the Corvalan model.
- Corvalan2005(Minimizer<? super ConstrainedOptimProblem, IterativeSolution<Vector>>, DiversificationMeasure, double, double) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005
-
Constructs an instance of the Corvalan model.
- Corvalan2005.WeightsConstraint - Interface in tech.nmfin.portfoliooptimization.corvalan2005
-
Constraints on weights which are defined by a set of less-than constraints.
- cos(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the cosine of a vector, element-by-element.
- cos(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Cosine of a complex number.
- cosec(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the cosecant of an angle.
- cosh(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Hyperbolic cosine of a complex number.
- cost() - Method in class dev.nm.graph.type.SimpleArc
-
- cost() - Method in class dev.nm.graph.type.SimpleEdge
-
- cost() - Method in interface dev.nm.graph.WeightedEdge
-
Gets the cost or weight of this edge.
- COST - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- cot(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the cotangent of an angle.
- coth(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the hyperbolic cotangent of a hyperbolic angle.
- count(double) - Method in class dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SturmCount
-
Computes the Sturm count.
- count(double) - Method in class dev.nm.combinatorics.Counter
-
Get the count, i.e., the number of occurrences, of a particular number.
- countEntriesInEachColumn(List<SparseMatrix.Entry>, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Counts the number of entries in each column.
- countEntriesInEachRow(List<SparseMatrix.Entry>, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Counts the number of entries in each row.
- Counter - Class in dev.nm.combinatorics
-
A counter keeps track of the number of occurrences of numbers.
- Counter() - Constructor for class dev.nm.combinatorics.Counter
-
Construct a counter with no rounding.
- Counter(int) - Constructor for class dev.nm.combinatorics.Counter
-
Construct a counter.
- CountMonitor<S> - Class in dev.nm.misc.algorithm.iterative.monitor
-
This
IterationMonitor
counts the number of iterates generated, hence
the number of iterations.
- CountMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.CountMonitor
-
- CourantPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the squared error penalties.
- CourantPenalty(EqualityConstraints, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.CourantPenalty
-
Construct a CourantPenalty penalty function from a collection of equality constraints.
- CourantPenalty(EqualityConstraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.CourantPenalty
-
Construct a CourantPenalty penalty function from a collection of equality constraints.
- CourantPenalty(EqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.CourantPenalty
-
Construct a CourantPenalty penalty function from a collection of equality constraints.
- cov() - Method in class dev.nm.stat.random.variancereduction.AntitheticVariates
-
- cov() - Method in class dev.nm.stat.random.variancereduction.CommonRandomNumbers
-
Gets the covariance between f and g.
- cov() - Method in class dev.nm.stat.random.variancereduction.ControlVariates
-
- covariance() - Method in interface dev.nm.stat.covariance.covarianceselection.CovarianceSelectionSolver
-
Get the estimated Covariance matrix of the selection problem.
- covariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
-
Gets the estimated covariance matrix.
- covariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Get the estimated covariance matrix.
- Covariance - Class in dev.nm.stat.descriptive.covariance
-
Covariance is a measure of how much two variables change together.
- Covariance() - Constructor for class dev.nm.stat.descriptive.covariance.Covariance
-
Construct an empty Covariance
calculator.
- Covariance(double[], double[]) - Constructor for class dev.nm.stat.descriptive.covariance.Covariance
-
Construct a Covariance
calculator, initialized with two samples.
- Covariance(Covariance) - Constructor for class dev.nm.stat.descriptive.covariance.Covariance
-
Copy constructor.
- covariance() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
-
- covariance() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
-
- covariance() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
- covariance() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the covariance matrix of this distribution.
- covariance() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
- covariance() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
-
- covariance() - Method in class dev.nm.stat.regression.linear.glm.GLMBeta
-
- covariance() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMBeta
-
- covariance() - Method in class dev.nm.stat.regression.linear.LMBeta
-
Gets the covariance matrix of the coefficient estimates, β^.
- covariance() - Method in class dev.nm.stat.regression.linear.logistic.LogisticBeta
-
- covariance() - Method in class dev.nm.stat.regression.linear.ols.OLSBeta
-
- covariance(int) - Method in class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
-
Get the covariance matrix for prediction errors at time t for x^t+1.
- covariance() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Get the asymptotic covariance matrix of the estimators.
- covariance() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the asymptotic covariance matrix of the estimated parameters,
φ and θ.
- covariance() - Method in class tech.nmfin.meanreversion.daspremont2008.CovarianceEstimation
-
- CovarianceEstimation - Class in tech.nmfin.meanreversion.daspremont2008
-
Estimates the covariance matrix by maximum likelihood.
- CovarianceEstimation(Matrix, double) - Constructor for class tech.nmfin.meanreversion.daspremont2008.CovarianceEstimation
-
Solves the maximum likelihood problem for covariance selection.
- covarianceMatrix() - Method in class dev.nm.stat.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 dev.nm.stat.covariance.covarianceselection.lasso
-
GLASSOFAST is the Graphical LASSO algorithm to solve the covariance selection
problem.
- CovarianceSelectionGLASSOFAST(CovarianceSelectionProblem) - Constructor for class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
-
Solves the maximum likelihood problem for covariance selection.
- CovarianceSelectionLASSO - Class in dev.nm.stat.covariance.covarianceselection.lasso
-
The LASSO approach of covariance selection.
- CovarianceSelectionLASSO(CovarianceSelectionProblem, double) - Constructor for class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Estimate the covariance matrix directly by using LASSO.
- CovarianceSelectionLASSO(CovarianceSelectionProblem) - Constructor for class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Estimate the covariance matrix directly by using LASSO.
- CovarianceSelectionProblem - Class in dev.nm.stat.covariance.covarianceselection
-
This class defines the covariance selection problem outlined in d'Aspremont
(2008).
- CovarianceSelectionProblem(Matrix, double) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Constructs a covariance selection problem.
- CovarianceSelectionProblem(MultivariateTimeSeries, double, boolean) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Constructs a covariance selection problem from a multivariate time
series.
- CovarianceSelectionProblem(MultivariateTimeSeries, double) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Constructs a covariance selection problem from a multivariate time
series.
- CovarianceSelectionProblem(CovarianceSelectionProblem) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Copy constructor.
- CovarianceSelectionSolver - Interface in dev.nm.stat.covariance.covarianceselection
-
- covers(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the coversed sine or coversine of an angle.
- Cr - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
the crossover probability
- CramerVonMises2Samples - Class in dev.nm.stat.test.distribution
-
This algorithm calculates the two sample Cramer-Von Mises test statistic and p-value.
- CramerVonMises2Samples(double[], double[]) - Constructor for class dev.nm.stat.test.distribution.CramerVonMises2Samples
-
Calculate the statistics and p-value of two sample Cramer-Von Mises test.
- CrankNicolsonConvectionDiffusionEquation1D - Class in dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D
-
Constructs a Crank-Nicolson solver for a 1 dimensional
convection-diffusion PDE.
- CrankNicolsonConvectionDiffusionEquation1D.Coefficients - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
-
Gets the coefficients of a discretized 1D convection-diffusion equation
for each time step.
- CrankNicolsonHeatEquation1D - Class in dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D
-
- CrankNicolsonHeatEquation1D.Coefficients - Class in dev.nm.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 dev.nm.solver.multivariate.geneticalgorithm.Chromosome
-
Construct a Chromosome
by crossing over a pair of chromosomes.
- crossover(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
-
- crossover(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
-
- crossover(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
-
- crossover(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
-
Crossover by taking the midpoint.
- csch(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the hyperbolic cosecant of a hyperbolic angle.
- CSDPMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
-
Implements the CSDP algorithm for semidefinite programming problem with equality constraints.
- CSDPMinimizer(double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
-
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming
problems.
- CSDPMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
-
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming
problems.
- CSDPMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
-
- CSRSparseMatrix - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Constructs a sparse matrix in CSR format.
- CSRSparseMatrix(int, int, int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Constructs a sparse matrix in CSR format.
- CSRSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Constructs a sparse matrix from a matrix.
- CSRSparseMatrix(CSRSparseMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
Copy constructor.
- CSV_SEPARATOR - Static variable in class dev.nm.number.DoubleUtils
-
The default separator for CSV file parsing.
- Ctor2x2(double, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Same as new GivensMatrix(2, 1, 2, c, s)
.
- CtorFromRho(int, int, int, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Constructs a Givens matrix from ρ.
- CtorToRotateColumns(int, int, int, double, double) - Static method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Constructs a Givens matrix such that Gt * A has 0 in the [i,j]
entry.
- CubicHermite - Class in dev.nm.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 dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite
-
- CubicHermite(CubicHermite.Tangent) - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite
-
Construct an instance with the given method to compute tangents.
- CubicHermite.Tangent - Interface in dev.nm.analysis.curvefit.interpolation.univariate
-
The method for computing the control tangent at a given index.
- CubicHermite.Tangents - Enum in dev.nm.analysis.curvefit.interpolation.univariate
-
- CubicRoot - Class in dev.nm.analysis.function.polynomial.root
-
This is a cubic equation solver.
- CubicRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.CubicRoot
-
- CubicSpline - Class in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the cumulative sums.
- cumsum(double[]) - Static method in class dev.nm.number.DoubleUtils
-
Gets the cumulative sums of the elements in an array.
- cumsum(int[]) - Static method in class dev.nm.number.DoubleUtils
-
Gets the cumulative sums of the elements in an array.
- cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
-
- cumulant(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
-
The cumulant function of the exponential distribution.
- cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
-
- cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
-
- cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
-
- cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
-
- CumulativeNormalHastings - Class in dev.nm.analysis.function.special.gaussian
-
Hastings algorithm is faster but less accurate way to compute the cumulative standard Normal.
- CumulativeNormalHastings() - Constructor for class dev.nm.analysis.function.special.gaussian.CumulativeNormalHastings
-
- CumulativeNormalInverse - Class in dev.nm.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 dev.nm.analysis.function.special.gaussian.CumulativeNormalInverse
-
- CumulativeNormalMarsaglia - Class in dev.nm.analysis.function.special.gaussian
-
Marsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal.
- CumulativeNormalMarsaglia() - Constructor for class dev.nm.analysis.function.special.gaussian.CumulativeNormalMarsaglia
-
- cumulativeProportionVar() - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the cumulative proportion of overall variance explained by the
principal components
- CurveFitting - Interface in dev.nm.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 dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer.MyCutter
-
- cut(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer.MyCutter
-
- cut(SimplexTable) - Method in interface dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer.CutterFactory.Cutter
-
Cut a simplex table.
- D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
-
Get the diagonal matrix D as in Q * D * Q' = A.
- D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
The diagonal entries of the diagonal matrix D.
- D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot
-
- D() - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
-
- D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
- D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
-
- D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
-
- D() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the D matrix as in SVD decomposition.
- D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Returns the matrix D as in A=UDV'.
- D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Get D the the diagonal matrix in the LDL decomposition.
- D() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Gets the diagonal matrix D in the LDL decomposition.
- d() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets d.
- D() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
Computes D.
- d() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the order of integration.
- d() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the order of integration.
- D() - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal
-
- D() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets D
as in eq.
- DAgostino - Class in dev.nm.stat.test.distribution.normality
-
D'Agostino's K2 test is a goodness-of-fit measure of departure from normality.
- DAgostino(double[]) - Constructor for class dev.nm.stat.test.distribution.normality.DAgostino
-
Perform D'Agostino's test to test for the departure from normality.
- DAGraph<V,E extends Arc<V>> - Interface in dev.nm.graph
-
A directed acyclic graph (DAG), is a directed graph with no directed cycles.
- Dai2011HMM - Class in tech.nmfin.trend.dai2011
-
Creates a two-state Geometric Brownian Motion with a constant volatility.
- Dai2011HMM(double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM
-
Constructs a two-state Markov switching Geometric Brownian Motion.
- Dai2011HMM(Dai2011HMM.ModelParam) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM
-
- Dai2011HMM(Dai2011HMM) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM
-
Copy constructor.
- Dai2011HMM.CalibrationParam - Class in tech.nmfin.trend.dai2011
-
- Dai2011HMM.ModelParam - Class in tech.nmfin.trend.dai2011
-
- Dai2011Solver - Class in tech.nmfin.trend.dai2011
-
Solves the stochastic control problem in the referenced paper to get the two
thresholds.
- Dai2011Solver.Boundaries - Class in tech.nmfin.trend.dai2011
-
- Dai2011Solver.Builder - Class in tech.nmfin.trend.dai2011
-
- dampedBFGSHessianUpdate(Matrix, Vector, Vector) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
-
Damped BFGS Hessian update.
- data() - Method in class dev.nm.graph.type.VertexTree
-
- DATE_FORMAT_STRING - Static variable in class dev.nm.misc.license.License
-
Date format for all kinds of dates in a license file.
- DateTimeGenericTimeSeries<V> - Class in dev.nm.stat.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 dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries
-
Construct a time series.
- DateTimeGenericTimeSeries.Entry<V> - Class in dev.nm.stat.timeseries.datastructure
-
This is the TimeSeries.Entry
for a DateTime
-indexed time series.
- DateTimeTimeSeries - Class in dev.nm.stat.timeseries.datastructure.univariate
-
This is a time series has its double
values indexed by DateTime
.
- DateTimeTimeSeries(DateTime[], double[]) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.DateTimeTimeSeries
-
Construct a time series from DateTime
and double
.
- DateTimeTimeSeries(ArrayList<DateTime>, ArrayList<Double>) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.DateTimeTimeSeries
-
Construct a time series from DateTime
and double
.
- dB(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get a Brownian motion increment.
- dB(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the Brownian increment at the i-th time point.
- dB(double) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
-
Get a Brownian motion increment.
- db(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE
-
\[
\frac{d\sigma}{dt}
\]
- DBeta - Class in dev.nm.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 dev.nm.analysis.differentiation.univariate.DBeta
-
- DBetaRegularized - Class in dev.nm.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 dev.nm.analysis.differentiation.univariate.DBetaRegularized
-
Construct the derivative function of the Regularized Incomplete Beta function,
BetaRegularized
.
- dBt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get all the Brownian increments.
- ddy() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative
-
Gets y'' = F(x,y).
- DeBest2BinCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell
-
- deColumnMean(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the de-mean (column means) matrix of a given matrix.
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
Make a deep copy of the underlying matrix.
- deepCopy() - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
Return
this
as this
Matrix
is immutable.
- deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
Returns this
as the reference is immutable.
- deepCopy() - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
-
- deepCopy() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- deepCopy() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- deepCopy() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- deepCopy() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
- deepCopy() - Method in interface dev.nm.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 dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
- deepCopy() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
-
- deepCopy() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
- deepCopy() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
-
- DeepCopyable - Interface in dev.nm.misc
-
This interface provides a way to do polymorphic copying.
- DEFAULT_CACHE_SIZE - Static variable in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
The default cache size = the number of available processors ×
1000.
- DEFAULT_CACHE_SIZE - Static variable in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
The default cache size = the number of available processors × 1000.
- DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
- DEFAULT_GRID_SIZE - Static variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
- DEFAULT_INITIAL_TEMPERATURE - Static variable in class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
the default initial temperature
- DEFAULT_LICENSE_FILES - Static variable in class dev.nm.misc.license.License
-
Default license file names.
- DEFAULT_MATRIX_SIZE_THRESHOLD - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
The default matrix size threshold.
- DEFAULT_MAXIMUM_ITERATIONS - Static variable in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- DEFAULT_MERSENNE_EXPONENT - Static variable in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
-
- DEFAULT_METHOD - Static variable in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
-
The default algorithm for computing SVD.
- DEFAULT_MIN_RELATIVE_GAP - Static variable in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Default value for the minimum relative gap threshold.
- DEFAULT_NLAGS - Static variable in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
the default number of lags
- DEFAULT_NUMBER_OF_LAGS - Static variable in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
-
the default number of lags
- DEFAULT_NUMBER_OF_LAGS - Static variable in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
the default number of lags
- DEFAULT_PENALTY_FUNCTION_FACTORY - Static variable in class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
the default penalty function factory
- DEFAULT_QA - Static variable in class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
the default acceptance parameter
- DEFAULT_QV - Static variable in class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
the default visiting parameter
- DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
-
Default value for the safety factor γ.
- DEFAULT_SIGMA - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.AntoniouLu2007
-
the default value of the centering parameter
- DEFAULT_STABLE_ITERATION_COUNT - Static variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- DEFAULT_THRESHOLD - Static variable in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
-
The default tolerance parameter tol.
- DEFAULT_TOLERANCE - Static variable in class dev.nm.misc.algorithm.iterative.tolerance.AbsoluteTolerance
-
default tolerance
- DEFAULT_TOLERANCE - Static variable in class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance
-
default tolerance
- DEFAULT_TOLERANCE - Static variable in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- DefaultDeflationCriterion - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
The default deflation criterion is to use eq.
- DefaultDeflationCriterion() - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
-
Constructs the default deflation criterion.
- DefaultDeflationCriterion(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
-
Constructs the default deflation criterion.
- DefaultDeflationCriterion(DefaultDeflationCriterion.MatrixNorm) - Constructor for class dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
Computes the norm of a given matrix.
- DefaultMatrixStorage - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Construct a DefaultMatrixStorage
to wrap a storage for access.
- DefaultRoot() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.DefaultRoot
-
- DefaultSimplex - Class in dev.nm.solver.multivariate.initialization
-
A simplex optimization algorithm, e.g., Nelder-Mead, requires an initial simplex to start the search.
- DefaultSimplex(double) - Constructor for class dev.nm.solver.multivariate.initialization.DefaultSimplex
-
Construct a simplex builder.
- DefaultSimplex() - Constructor for class dev.nm.solver.multivariate.initialization.DefaultSimplex
-
Construct a simplex builder.
- definingVector() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
-
- definingVector() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Get the Householder defining vector which is orthogonal to the Householder hyperplane.
- Deflation - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
A deflation found in a Hessenberg (or tridiagonal in symmetric case) matrix.
- DeflationCriterion - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
Determines whether a sub-diagonal entry is sufficiently small to be neglected.
- deflationCriterion - Variable in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
- degree() - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Get the degree of this polynomial.
- deleteCol(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Deletes column i.
- deleteRow(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Deletes row i.
- delta - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- delta() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
-
Gets the shrinkage parameter δ.
- deltaX() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
Return the distance between two adjacent points along the x-axis.
- deltaX(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
-
Get the distance between two adjacent points along the axis with the given index.
- deltaY() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
Return the distance between two adjacent points along the y-axis.
- DenseData - Class in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Construct a storage.
- DenseMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense
-
This class implements the standard, dense, double
based matrix
representation.
- DenseMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a 0 matrix of dimension nRows * nCols.
- DenseMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a matrix from a 2D double[][]
array.
- DenseMatrix(double[], int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a matrix from a 1D double[]
.
- DenseMatrix(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a column matrix from a vector.
- DenseMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Converts any matrix to the standard matrix representation.
- DenseMatrix(DenseMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Copy constructor performing a deep copy.
- DenseMatrix(DenseMatrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
This constructor is useful for subclass to pass in computed value.
- DenseMatrixMultiplication - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
Matrix operation that multiplies two matrices.
- DenseMatrixMultiplicationByBlock - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
- DenseMatrixMultiplicationByBlock() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock
-
- DenseMatrixMultiplicationByBlock(DenseMatrixMultiplicationByBlock.BlockAlgorithm) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock
-
- DenseMatrixMultiplicationByBlock.BlockAlgorithm - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
- DenseMatrixMultiplicationByIjk - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
Implements the naive IJK algorithm.
- DenseMatrixMultiplicationByIjk() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByIjk
-
- DenseVector - Class in dev.nm.algebra.linear.vector.doubles.dense
-
This class implements the standard, dense, double
based vector
representation.
- DenseVector(int) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector.
- DenseVector(int, double) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by repeating a value.
- DenseVector(double...) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a double[]
.
- DenseVector(Double[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a Double[]
.
- DenseVector(List<Double>) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a List<Double>
.
- DenseVector(Collection<? extends Number>) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a collection, with order defined by
its iterator.
- DenseVector(int[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a int[]
.
- DenseVector(Matrix) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector from a column or row matrix.
- DenseVector(Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Casts any vector to a DenseVector
.
- DenseVector(DenseVector) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Copy constructor.
- Densifiable - Interface in dev.nm.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 dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
-
- density(double, double) - Method in interface dev.nm.stat.distribution.multivariate.BivariateProbabilityDistribution
-
The joint distribution density \(f_{X_1,X_2}(x_1,x_2)\).
- density(Vector) - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
-
- density(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
-
- density(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
- density(Vector) - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
-
The density function, which, if exists, is the derivative of F.
- density(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
-
This is the probability mass function.
- density(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
This is the probability mass function for the discrete sample.
- density(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
-
- density(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
The density function, which, if exists, is the derivative of F.
- density(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
- density(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
-
- density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- density(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
The density function, which, if exists, is the derivative of F.
- density(double) - Method in class dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.MaximaDistribution
-
The probability density function.
- density(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
The probability density function.
- density(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
- density(int, double) - Method in class dev.nm.stat.hmm.discrete.DiscreteHMM
-
Gets the (conditional) probability mass of making an observation in a
particular state.
- density(int, double) - Method in class dev.nm.stat.hmm.HiddenMarkovModel
-
Gets the (conditional) probability density/mass of making an observation
in a particular state.
- density(int, double) - Method in class dev.nm.stat.hmm.mixture.MixtureHMM
-
- density(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- density(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- density(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- density(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- density(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- density(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- DEOptim - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
-
Differential Evolution (DE) is a global optimization method.
- DEOptim(DEOptim.NewCellFactory, RandomLongGenerator, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
-
Construct a DEOptim
to solve unconstrained minimization problems.
- DEOptim(double, double, RandomLongGenerator, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
-
Construct a DEOptim
to solve unconstrained minimization problems.
- DEOptim(double, double, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
-
Construct a DEOptim
to solve unconstrained minimization problems.
- DEOptim.NewCellFactory - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
-
This factory constructs a new DEOptimCellFactory
for each minimization problem.
- DEOptim.Solution - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
-
This is the solution to a minimization problem using DEOptim
.
- DeOptimCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
-
- DEOptimCellFactory - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
-
- DEOptimCellFactory(double, double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Construct an instance of a DEOptimCellFactory
.
- DEOptimCellFactory(DEOptimCellFactory) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Copy constructor.
- DEOptimCellFactory.DeOptimCell - Class in dev.nm.solver.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 dev.nm.stat.evt.evd.bivariate.BivariateEVD
-
The dependence function \(A\) for the parametric bivariate extreme value model.
- dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- depth() - Method in class dev.nm.graph.algorithm.traversal.BFS.Node
-
Gets the depth of this node.
- depth(V) - Method in interface dev.nm.graph.RootedTree
-
Gets the (unweighted) distance of a vertex from the root of the vertex.
- depth(V) - Method in class dev.nm.graph.type.SparseTree
-
- depth(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- DeRand1BinCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
-
- DErf - Class in dev.nm.analysis.differentiation.univariate
-
This is the first order derivative function of the Error function,
Erf
.
- DErf() - Constructor for class dev.nm.analysis.differentiation.univariate.DErf
-
- derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkCloglog
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.link.LinkFunction
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkIdentity
-
- derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverse
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverseSquared
-
- derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLog
-
- derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLogit
-
Derivative of the link function, i.e., g'(x).
- derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkProbit
-
- derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkSqrt
-
Derivative of the link function, i.e., g'(x).
- DerivativeFunction - Interface in dev.nm.analysis.differentialequation.ode.ivp.problem
-
Defines the derivative function F(x, y) for ODE problems.
- deRowMean(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the de-mean (row means) matrix of a given matrix.
- det() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix
-
The determinant of a Hilbert matrix is the reciprocal of an integer.
- det(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the determinant of a matrix.
- dev.nm.algebra.linear.matrix - package dev.nm.algebra.linear.matrix
-
- dev.nm.algebra.linear.matrix.doubles - package dev.nm.algebra.linear.matrix.doubles
-
- dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization - package dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization
-
- dev.nm.algebra.linear.matrix.doubles.factorization.eigen - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen
-
- dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds
-
- dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3 - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
- dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec
-
- dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
- dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination - package dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination
-
- dev.nm.algebra.linear.matrix.doubles.factorization.qr - package dev.nm.algebra.linear.matrix.doubles.factorization.qr
-
- dev.nm.algebra.linear.matrix.doubles.factorization.svd - package dev.nm.algebra.linear.matrix.doubles.factorization.svd
-
- dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3 - package dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3
-
- dev.nm.algebra.linear.matrix.doubles.factorization.triangle - package dev.nm.algebra.linear.matrix.doubles.factorization.triangle
-
- dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky - package dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
-
- dev.nm.algebra.linear.matrix.doubles.linearsystem - package dev.nm.algebra.linear.matrix.doubles.linearsystem
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype - package dev.nm.algebra.linear.matrix.doubles.matrixtype
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.dense - package dev.nm.algebra.linear.matrix.doubles.matrixtype.dense
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal - package dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle - package dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation - package dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication - package dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
- dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
- dev.nm.algebra.linear.matrix.doubles.operation - package dev.nm.algebra.linear.matrix.doubles.operation
-
- dev.nm.algebra.linear.matrix.doubles.operation.householder - package dev.nm.algebra.linear.matrix.doubles.operation.householder
-
- dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite - package dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite
-
- dev.nm.algebra.linear.matrix.generic - package dev.nm.algebra.linear.matrix.generic
-
- dev.nm.algebra.linear.matrix.generic.matrixtype - package dev.nm.algebra.linear.matrix.generic.matrixtype
-
- dev.nm.algebra.linear.vector - package dev.nm.algebra.linear.vector
-
- dev.nm.algebra.linear.vector.doubles - package dev.nm.algebra.linear.vector.doubles
-
- dev.nm.algebra.linear.vector.doubles.dense - package dev.nm.algebra.linear.vector.doubles.dense
-
- dev.nm.algebra.linear.vector.doubles.operation - package dev.nm.algebra.linear.vector.doubles.operation
-
- dev.nm.algebra.structure - package dev.nm.algebra.structure
-
- dev.nm.analysis.curvefit - package dev.nm.analysis.curvefit
-
- dev.nm.analysis.curvefit.interpolation - package dev.nm.analysis.curvefit.interpolation
-
- dev.nm.analysis.curvefit.interpolation.bivariate - package dev.nm.analysis.curvefit.interpolation.bivariate
-
- dev.nm.analysis.curvefit.interpolation.multivariate - package dev.nm.analysis.curvefit.interpolation.multivariate
-
- dev.nm.analysis.curvefit.interpolation.univariate - package dev.nm.analysis.curvefit.interpolation.univariate
-
- dev.nm.analysis.differentialequation - package dev.nm.analysis.differentialequation
-
- dev.nm.analysis.differentialequation.ode.ivp.problem - package dev.nm.analysis.differentialequation.ode.ivp.problem
-
- dev.nm.analysis.differentialequation.ode.ivp.solver - package dev.nm.analysis.differentialequation.ode.ivp.solver
-
- dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation - package dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation
-
- dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton - package dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
-
- dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta - package dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
-
- dev.nm.analysis.differentialequation.pde - package dev.nm.analysis.differentialequation.pde
-
- dev.nm.analysis.differentialequation.pde.finitedifference - package dev.nm.analysis.differentialequation.pde.finitedifference
-
- dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2 - package dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2
-
- dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1 - package dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1
-
- dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2 - package dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2
-
- dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation - package dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
-
- dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation - package dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
-
- dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2 - package dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2
-
- dev.nm.analysis.differentiation - package dev.nm.analysis.differentiation
-
- dev.nm.analysis.differentiation.differentiability - package dev.nm.analysis.differentiation.differentiability
-
- dev.nm.analysis.differentiation.multivariate - package dev.nm.analysis.differentiation.multivariate
-
- dev.nm.analysis.differentiation.univariate - package dev.nm.analysis.differentiation.univariate
-
- dev.nm.analysis.function - package dev.nm.analysis.function
-
- dev.nm.analysis.function.matrix - package dev.nm.analysis.function.matrix
-
- dev.nm.analysis.function.polynomial - package dev.nm.analysis.function.polynomial
-
- dev.nm.analysis.function.polynomial.root - package dev.nm.analysis.function.polynomial.root
-
- dev.nm.analysis.function.polynomial.root.jenkinstraub - package dev.nm.analysis.function.polynomial.root.jenkinstraub
-
- dev.nm.analysis.function.rn2r1 - package dev.nm.analysis.function.rn2r1
-
- dev.nm.analysis.function.rn2r1.univariate - package dev.nm.analysis.function.rn2r1.univariate
-
- dev.nm.analysis.function.rn2rm - package dev.nm.analysis.function.rn2rm
-
- dev.nm.analysis.function.special - package dev.nm.analysis.function.special
-
- dev.nm.analysis.function.special.beta - package dev.nm.analysis.function.special.beta
-
- dev.nm.analysis.function.special.gamma - package dev.nm.analysis.function.special.gamma
-
- dev.nm.analysis.function.special.gaussian - package dev.nm.analysis.function.special.gaussian
-
- dev.nm.analysis.function.tuple - package dev.nm.analysis.function.tuple
-
- dev.nm.analysis.integration.univariate - package dev.nm.analysis.integration.univariate
-
- dev.nm.analysis.integration.univariate.riemann - package dev.nm.analysis.integration.univariate.riemann
-
- dev.nm.analysis.integration.univariate.riemann.gaussian - package dev.nm.analysis.integration.univariate.riemann.gaussian
-
- dev.nm.analysis.integration.univariate.riemann.gaussian.rule - package dev.nm.analysis.integration.univariate.riemann.gaussian.rule
-
- dev.nm.analysis.integration.univariate.riemann.newtoncotes - package dev.nm.analysis.integration.univariate.riemann.newtoncotes
-
- dev.nm.analysis.integration.univariate.riemann.substitution - package dev.nm.analysis.integration.univariate.riemann.substitution
-
- dev.nm.analysis.root.multivariate - package dev.nm.analysis.root.multivariate
-
- dev.nm.analysis.root.univariate - package dev.nm.analysis.root.univariate
-
- dev.nm.analysis.sequence - package dev.nm.analysis.sequence
-
- dev.nm.combinatorics - package dev.nm.combinatorics
-
- dev.nm.dsp.univariate.operation.system.doubles - package dev.nm.dsp.univariate.operation.system.doubles
-
- dev.nm.geometry - package dev.nm.geometry
-
- dev.nm.geometry.polyline - package dev.nm.geometry.polyline
-
- dev.nm.graph - package dev.nm.graph
-
- dev.nm.graph.algorithm.shortestpath - package dev.nm.graph.algorithm.shortestpath
-
- dev.nm.graph.algorithm.traversal - package dev.nm.graph.algorithm.traversal
-
- dev.nm.graph.community - package dev.nm.graph.community
-
- dev.nm.graph.type - package dev.nm.graph.type
-
- dev.nm.interval - package dev.nm.interval
-
- dev.nm.misc - package dev.nm.misc
-
- dev.nm.misc.algorithm - package dev.nm.misc.algorithm
-
- dev.nm.misc.algorithm.bb - package dev.nm.misc.algorithm.bb
-
- dev.nm.misc.algorithm.iterative - package dev.nm.misc.algorithm.iterative
-
- dev.nm.misc.algorithm.iterative.monitor - package dev.nm.misc.algorithm.iterative.monitor
-
- dev.nm.misc.algorithm.iterative.tolerance - package dev.nm.misc.algorithm.iterative.tolerance
-
- dev.nm.misc.algorithm.stopcondition - package dev.nm.misc.algorithm.stopcondition
-
- dev.nm.misc.datastructure - package dev.nm.misc.datastructure
-
- dev.nm.misc.datastructure.time - package dev.nm.misc.datastructure.time
-
- dev.nm.misc.license - package dev.nm.misc.license
-
- dev.nm.misc.parallel - package dev.nm.misc.parallel
-
- dev.nm.number - package dev.nm.number
-
- dev.nm.number.big - package dev.nm.number.big
-
- dev.nm.number.complex - package dev.nm.number.complex
-
- dev.nm.number.doublearray - package dev.nm.number.doublearray
-
- dev.nm.solver - package dev.nm.solver
-
- dev.nm.solver.multivariate.constrained - package dev.nm.solver.multivariate.constrained
-
- dev.nm.solver.multivariate.constrained.constraint - package dev.nm.solver.multivariate.constrained.constraint
-
- dev.nm.solver.multivariate.constrained.constraint.general - package dev.nm.solver.multivariate.constrained.constraint.general
-
- dev.nm.solver.multivariate.constrained.constraint.linear - package dev.nm.solver.multivariate.constrained.constraint.linear
-
- dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing - package dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
-
- dev.nm.solver.multivariate.constrained.convex.sdp.problem - package dev.nm.solver.multivariate.constrained.convex.sdp.problem
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
-
- dev.nm.solver.multivariate.constrained.general.box - package dev.nm.solver.multivariate.constrained.general.box
-
- dev.nm.solver.multivariate.constrained.general.penaltymethod - package dev.nm.solver.multivariate.constrained.general.penaltymethod
-
- dev.nm.solver.multivariate.constrained.general.sqp.activeset - package dev.nm.solver.multivariate.constrained.general.sqp.activeset
-
- dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint - package dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
-
- dev.nm.solver.multivariate.constrained.integer - package dev.nm.solver.multivariate.constrained.integer
-
- dev.nm.solver.multivariate.constrained.integer.bruteforce - package dev.nm.solver.multivariate.constrained.integer.bruteforce
-
- dev.nm.solver.multivariate.constrained.integer.linear.bb - package dev.nm.solver.multivariate.constrained.integer.linear.bb
-
- dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane - package dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
-
- dev.nm.solver.multivariate.constrained.integer.linear.problem - package dev.nm.solver.multivariate.constrained.integer.linear.problem
-
- dev.nm.solver.multivariate.constrained.problem - package dev.nm.solver.multivariate.constrained.problem
-
- dev.nm.solver.multivariate.geneticalgorithm - package dev.nm.solver.multivariate.geneticalgorithm
-
- dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
-
- dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
- dev.nm.solver.multivariate.geneticalgorithm.minimizer.local - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
-
- dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
-
- dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
-
- dev.nm.solver.multivariate.initialization - package dev.nm.solver.multivariate.initialization
-
- dev.nm.solver.multivariate.minmax - package dev.nm.solver.multivariate.minmax
-
- dev.nm.solver.multivariate.unconstrained - package dev.nm.solver.multivariate.unconstrained
-
- dev.nm.solver.multivariate.unconstrained.annealing - package dev.nm.solver.multivariate.unconstrained.annealing
-
- dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction - package dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
-
- dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction - package dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
-
- dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction - package dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
-
- dev.nm.solver.multivariate.unconstrained.c2 - package dev.nm.solver.multivariate.unconstrained.c2
-
- dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection - package dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
-
- dev.nm.solver.multivariate.unconstrained.c2.linesearch - package dev.nm.solver.multivariate.unconstrained.c2.linesearch
-
- dev.nm.solver.multivariate.unconstrained.c2.quasinewton - package dev.nm.solver.multivariate.unconstrained.c2.quasinewton
-
- dev.nm.solver.multivariate.unconstrained.c2.steepestdescent - package dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
-
- dev.nm.solver.problem - package dev.nm.solver.problem
-
- dev.nm.solver.univariate - package dev.nm.solver.univariate
-
- dev.nm.solver.univariate.bracketsearch - package dev.nm.solver.univariate.bracketsearch
-
- dev.nm.stat.cointegration - package dev.nm.stat.cointegration
-
- dev.nm.stat.covariance - package dev.nm.stat.covariance
-
- dev.nm.stat.covariance.covarianceselection - package dev.nm.stat.covariance.covarianceselection
-
- dev.nm.stat.covariance.covarianceselection.lasso - package dev.nm.stat.covariance.covarianceselection.lasso
-
- dev.nm.stat.covariance.nlshrink - package dev.nm.stat.covariance.nlshrink
-
- dev.nm.stat.covariance.nlshrink.quest - package dev.nm.stat.covariance.nlshrink.quest
-
- dev.nm.stat.descriptive - package dev.nm.stat.descriptive
-
- dev.nm.stat.descriptive.correlation - package dev.nm.stat.descriptive.correlation
-
- dev.nm.stat.descriptive.covariance - package dev.nm.stat.descriptive.covariance
-
- dev.nm.stat.descriptive.moment - package dev.nm.stat.descriptive.moment
-
- dev.nm.stat.descriptive.moment.weighted - package dev.nm.stat.descriptive.moment.weighted
-
- dev.nm.stat.descriptive.rank - package dev.nm.stat.descriptive.rank
-
- dev.nm.stat.distribution.discrete - package dev.nm.stat.distribution.discrete
-
- dev.nm.stat.distribution.multivariate - package dev.nm.stat.distribution.multivariate
-
- dev.nm.stat.distribution.multivariate.exponentialfamily - package dev.nm.stat.distribution.multivariate.exponentialfamily
-
- dev.nm.stat.distribution.univariate - package dev.nm.stat.distribution.univariate
-
- dev.nm.stat.distribution.univariate.exponentialfamily - package dev.nm.stat.distribution.univariate.exponentialfamily
-
- dev.nm.stat.dlm.multivariate - package dev.nm.stat.dlm.multivariate
-
- dev.nm.stat.dlm.univariate - package dev.nm.stat.dlm.univariate
-
- dev.nm.stat.evt.cluster - package dev.nm.stat.evt.cluster
-
- dev.nm.stat.evt.evd.bivariate - package dev.nm.stat.evt.evd.bivariate
-
- dev.nm.stat.evt.evd.univariate - package dev.nm.stat.evt.evd.univariate
-
- dev.nm.stat.evt.evd.univariate.fitting - package dev.nm.stat.evt.evd.univariate.fitting
-
- dev.nm.stat.evt.evd.univariate.fitting.acer - package dev.nm.stat.evt.evd.univariate.fitting.acer
-
- dev.nm.stat.evt.evd.univariate.fitting.acer.empirical - package dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
-
- dev.nm.stat.evt.evd.univariate.fitting.pot - package dev.nm.stat.evt.evd.univariate.fitting.pot
-
- dev.nm.stat.evt.evd.univariate.rng - package dev.nm.stat.evt.evd.univariate.rng
-
- dev.nm.stat.evt.exi - package dev.nm.stat.evt.exi
-
- dev.nm.stat.evt.function - package dev.nm.stat.evt.function
-
- dev.nm.stat.evt.markovchain - package dev.nm.stat.evt.markovchain
-
- dev.nm.stat.evt.timeseries - package dev.nm.stat.evt.timeseries
-
- dev.nm.stat.factor.factoranalysis - package dev.nm.stat.factor.factoranalysis
-
- dev.nm.stat.factor.implicitmodelpca - package dev.nm.stat.factor.implicitmodelpca
-
- dev.nm.stat.factor.pca - package dev.nm.stat.factor.pca
-
- dev.nm.stat.hmm - package dev.nm.stat.hmm
-
- dev.nm.stat.hmm.discrete - package dev.nm.stat.hmm.discrete
-
- dev.nm.stat.hmm.mixture - package dev.nm.stat.hmm.mixture
-
- dev.nm.stat.hmm.mixture.distribution - package dev.nm.stat.hmm.mixture.distribution
-
- dev.nm.stat.markovchain - package dev.nm.stat.markovchain
-
- dev.nm.stat.random - package dev.nm.stat.random
-
- dev.nm.stat.random.rng - package dev.nm.stat.random.rng
-
- dev.nm.stat.random.rng.concurrent.cache - package dev.nm.stat.random.rng.concurrent.cache
-
- dev.nm.stat.random.rng.concurrent.context - package dev.nm.stat.random.rng.concurrent.context
-
- dev.nm.stat.random.rng.multivariate - package dev.nm.stat.random.rng.multivariate
-
- dev.nm.stat.random.rng.multivariate.mcmc.hybrid - package dev.nm.stat.random.rng.multivariate.mcmc.hybrid
-
- dev.nm.stat.random.rng.multivariate.mcmc.metropolis - package dev.nm.stat.random.rng.multivariate.mcmc.metropolis
-
- dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction - package dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
-
- dev.nm.stat.random.rng.univariate - package dev.nm.stat.random.rng.univariate
-
- dev.nm.stat.random.rng.univariate.beta - package dev.nm.stat.random.rng.univariate.beta
-
- dev.nm.stat.random.rng.univariate.exp - package dev.nm.stat.random.rng.univariate.exp
-
- dev.nm.stat.random.rng.univariate.gamma - package dev.nm.stat.random.rng.univariate.gamma
-
- dev.nm.stat.random.rng.univariate.normal - package dev.nm.stat.random.rng.univariate.normal
-
- dev.nm.stat.random.rng.univariate.normal.truncated - package dev.nm.stat.random.rng.univariate.normal.truncated
-
- dev.nm.stat.random.rng.univariate.poisson - package dev.nm.stat.random.rng.univariate.poisson
-
- dev.nm.stat.random.rng.univariate.uniform - package dev.nm.stat.random.rng.univariate.uniform
-
- dev.nm.stat.random.rng.univariate.uniform.linear - package dev.nm.stat.random.rng.univariate.uniform.linear
-
- dev.nm.stat.random.rng.univariate.uniform.mersennetwister - package dev.nm.stat.random.rng.univariate.uniform.mersennetwister
-
- dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation - package dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation
-
- dev.nm.stat.random.sampler.resampler - package dev.nm.stat.random.sampler.resampler
-
- dev.nm.stat.random.sampler.resampler.bootstrap - package dev.nm.stat.random.sampler.resampler.bootstrap
-
- dev.nm.stat.random.sampler.resampler.bootstrap.block - package dev.nm.stat.random.sampler.resampler.bootstrap.block
-
- dev.nm.stat.random.sampler.resampler.multivariate - package dev.nm.stat.random.sampler.resampler.multivariate
-
- dev.nm.stat.random.variancereduction - package dev.nm.stat.random.variancereduction
-
- dev.nm.stat.regression - package dev.nm.stat.regression
-
- dev.nm.stat.regression.linear - package dev.nm.stat.regression.linear
-
- dev.nm.stat.regression.linear.glm - package dev.nm.stat.regression.linear.glm
-
- dev.nm.stat.regression.linear.glm.distribution - package dev.nm.stat.regression.linear.glm.distribution
-
- dev.nm.stat.regression.linear.glm.distribution.link - package dev.nm.stat.regression.linear.glm.distribution.link
-
- dev.nm.stat.regression.linear.glm.modelselection - package dev.nm.stat.regression.linear.glm.modelselection
-
- dev.nm.stat.regression.linear.glm.quasi - package dev.nm.stat.regression.linear.glm.quasi
-
- dev.nm.stat.regression.linear.glm.quasi.family - package dev.nm.stat.regression.linear.glm.quasi.family
-
- dev.nm.stat.regression.linear.lasso - package dev.nm.stat.regression.linear.lasso
-
- dev.nm.stat.regression.linear.lasso.lars - package dev.nm.stat.regression.linear.lasso.lars
-
- dev.nm.stat.regression.linear.logistic - package dev.nm.stat.regression.linear.logistic
-
- dev.nm.stat.regression.linear.ols - package dev.nm.stat.regression.linear.ols
-
- dev.nm.stat.regression.linear.panel - package dev.nm.stat.regression.linear.panel
-
- dev.nm.stat.regression.linear.residualanalysis - package dev.nm.stat.regression.linear.residualanalysis
-
- dev.nm.stat.stochasticprocess.multivariate.random - package dev.nm.stat.stochasticprocess.multivariate.random
-
- dev.nm.stat.stochasticprocess.multivariate.sde - package dev.nm.stat.stochasticprocess.multivariate.sde
-
- dev.nm.stat.stochasticprocess.multivariate.sde.coefficients - package dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
-
- dev.nm.stat.stochasticprocess.multivariate.sde.discrete - package dev.nm.stat.stochasticprocess.multivariate.sde.discrete
-
- dev.nm.stat.stochasticprocess.timegrid - package dev.nm.stat.stochasticprocess.timegrid
-
- dev.nm.stat.stochasticprocess.univariate.filtration - package dev.nm.stat.stochasticprocess.univariate.filtration
-
- dev.nm.stat.stochasticprocess.univariate.integration - package dev.nm.stat.stochasticprocess.univariate.integration
-
- dev.nm.stat.stochasticprocess.univariate.random - package dev.nm.stat.stochasticprocess.univariate.random
-
- dev.nm.stat.stochasticprocess.univariate.sde - package dev.nm.stat.stochasticprocess.univariate.sde
-
- dev.nm.stat.stochasticprocess.univariate.sde.coefficients - package dev.nm.stat.stochasticprocess.univariate.sde.coefficients
-
- dev.nm.stat.stochasticprocess.univariate.sde.discrete - package dev.nm.stat.stochasticprocess.univariate.sde.discrete
-
- dev.nm.stat.stochasticprocess.univariate.sde.process - package dev.nm.stat.stochasticprocess.univariate.sde.process
-
- dev.nm.stat.stochasticprocess.univariate.sde.process.ou - package dev.nm.stat.stochasticprocess.univariate.sde.process.ou
-
- dev.nm.stat.test - package dev.nm.stat.test
-
- dev.nm.stat.test.distribution - package dev.nm.stat.test.distribution
-
- dev.nm.stat.test.distribution.kolmogorov - package dev.nm.stat.test.distribution.kolmogorov
-
- dev.nm.stat.test.distribution.normality - package dev.nm.stat.test.distribution.normality
-
- dev.nm.stat.test.distribution.pearson - package dev.nm.stat.test.distribution.pearson
-
- dev.nm.stat.test.mean - package dev.nm.stat.test.mean
-
- dev.nm.stat.test.rank - package dev.nm.stat.test.rank
-
- dev.nm.stat.test.rank.wilcoxon - package dev.nm.stat.test.rank.wilcoxon
-
- dev.nm.stat.test.regression.linear.heteroskedasticity - package dev.nm.stat.test.regression.linear.heteroskedasticity
-
- dev.nm.stat.test.timeseries.adf - package dev.nm.stat.test.timeseries.adf
-
- dev.nm.stat.test.timeseries.adf.table - package dev.nm.stat.test.timeseries.adf.table
-
- dev.nm.stat.test.timeseries.portmanteau - package dev.nm.stat.test.timeseries.portmanteau
-
- dev.nm.stat.test.variance - package dev.nm.stat.test.variance
-
- dev.nm.stat.timeseries.datastructure - package dev.nm.stat.timeseries.datastructure
-
- dev.nm.stat.timeseries.datastructure.multivariate - package dev.nm.stat.timeseries.datastructure.multivariate
-
- dev.nm.stat.timeseries.datastructure.multivariate.realtime - package dev.nm.stat.timeseries.datastructure.multivariate.realtime
-
- dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime - package dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
-
- dev.nm.stat.timeseries.datastructure.univariate - package dev.nm.stat.timeseries.datastructure.univariate
-
- dev.nm.stat.timeseries.datastructure.univariate.realtime - package dev.nm.stat.timeseries.datastructure.univariate.realtime
-
- dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime - package dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime
-
- dev.nm.stat.timeseries.linear.multivariate - package dev.nm.stat.timeseries.linear.multivariate
-
- dev.nm.stat.timeseries.linear.multivariate.arima - package dev.nm.stat.timeseries.linear.multivariate.arima
-
- dev.nm.stat.timeseries.linear.multivariate.stationaryprocess - package dev.nm.stat.timeseries.linear.multivariate.stationaryprocess
-
- dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma - package dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
-
- dev.nm.stat.timeseries.linear.univariate - package dev.nm.stat.timeseries.linear.univariate
-
- dev.nm.stat.timeseries.linear.univariate.arima - package dev.nm.stat.timeseries.linear.univariate.arima
-
- dev.nm.stat.timeseries.linear.univariate.sample - package dev.nm.stat.timeseries.linear.univariate.sample
-
- dev.nm.stat.timeseries.linear.univariate.stationaryprocess - package dev.nm.stat.timeseries.linear.univariate.stationaryprocess
-
- dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma - package dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
-
- dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch - package dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch
-
- dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch - package dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
-
- deviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
-
- deviance(double, double) - Method in interface dev.nm.stat.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 dev.nm.stat.regression.linear.glm.distribution.GLMGamma
-
- deviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
-
- deviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
-
- deviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
-
- deviance() - Method in class dev.nm.stat.regression.linear.glm.GLMResiduals
-
Gets the (total) deviance.
- deviance() - Method in class dev.nm.stat.regression.linear.logistic.LogisticResiduals
-
Gets the residual deviance.
- devianceResiduals() - Method in class dev.nm.stat.regression.linear.glm.GLMResiduals
-
Gets the deviances residuals.
- devianceResiduals() - Method in class dev.nm.stat.regression.linear.logistic.LogisticResiduals
-
Gets the residuals, ε.
- deviances() - Method in class dev.nm.stat.regression.linear.glm.GLMResiduals
-
Gets the deviances of the observations.
- deviances() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMResiduals
-
- df(double, double) - Method in class dev.nm.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 dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the degree of freedom.
- df() - Method in class dev.nm.stat.test.mean.T
-
Get the degree of freedom.
- df1() - Method in class dev.nm.stat.test.mean.OneWayANOVA
-
Get the first degree of freedom.
- df2() - Method in class dev.nm.stat.test.mean.OneWayANOVA
-
Get the second degree of freedom.
- Dfdx - Class in dev.nm.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 dev.nm.analysis.differentiation.univariate.Dfdx
-
Construct the first order derivative function of a univariate function f.
- Dfdx(UnivariateRealFunction) - Constructor for class dev.nm.analysis.differentiation.univariate.Dfdx
-
Construct, using the central finite difference, the first order derivative function of a univariate function f.
- Dfdx.Method - Enum in dev.nm.analysis.differentiation.univariate
-
the available methods to compute the numerical derivative
- DFFITS() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics
-
DFFITS, Welsch and Kuh Measure.
- DFPMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.quasinewton.DFPMinimizer
-
Construct a multivariate minimizer using the DFP method.
- DFS<V> - Class in dev.nm.graph.algorithm.traversal
-
This class implements the depth-first-search using iteration.
- DFS(Graph<? extends V, ? extends Edge<V>>) - Constructor for class dev.nm.graph.algorithm.traversal.DFS
-
Constructs a DFS tree of a graph.
- DFS(Graph<W, ? extends Edge<V>>, V, int) - Static method in class dev.nm.graph.algorithm.traversal.DFS
-
Runs the depth-first-search on a graph from a designated root.
- DFS.Node<V> - Class in dev.nm.graph.algorithm.traversal
-
This is a node in a DFS-spanning tree.
- DFS.Node.Color - Enum in dev.nm.graph.algorithm.traversal
-
This is the coloring scheme of visits.
- DGamma - Class in dev.nm.analysis.differentiation.univariate
-
This is the first order derivative function of the Gamma function, \({d \mathrm{\Gamma}(x) \over dx}\).
- DGamma() - Constructor for class dev.nm.analysis.differentiation.univariate.DGamma
-
- DGaussian - Class in dev.nm.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 dev.nm.analysis.differentiation.univariate.DGaussian
-
Construct the derivative function of a Gaussian function.
- Dhat() - Method in class dev.nm.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 dev.nm.graph.GraphUtils
-
Converts a directed graph into an undirected graph by removing the direction of all arcs.
- diagnostics() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
-
Gets the diagnostic measures of an OLS regression.
- diagonal(Matrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the diagonal of a matrix as a vector.
- diagonal(SparseMatrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the diagonal of a sparse matrix as a sparse vector.
- Diagonalization() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.Diagonalization
-
- DiagonalMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
-
A diagonal matrix has non-zero entries only on the main diagonal.
- DiagonalMatrix(double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Constructs a diagonal matrix from a double[]
.
- DiagonalMatrix(double[], int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- DiagonalMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Constructs a 0 diagonal matrix of dimension dim * dim.
- DiagonalMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- DiagonalMatrix(DiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Copy constructor.
- diagonalMatrix(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Gets the diagonal of a matrix.
- DiagonalSum - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
Add diagonal elements to a matrix, an efficient implementation.
- DiagonalSum(Matrix, double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- DiagonalSum(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- DiagonalSum(Matrix, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- diff(double[], int, int) - Static method in class dev.nm.number.DoubleUtils
-
Gets the lagged and iterated differences.
- diff(double[]) - Static method in class dev.nm.number.DoubleUtils
-
Gets the first differences of an array.
- diff(double[][], int, int) - Static method in class dev.nm.number.DoubleUtils
-
Gets the lagged and iterated differences of vectors.
- diff(double[][]) - Static method in class dev.nm.number.DoubleUtils
-
Gets the first differences of an array of vectors.
- diff(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct an instance of MultivariateGenericTimeTimeSeries
by taking the first
difference d
times.
- diff(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries
by taking the first difference
d
times.
- diff(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Construct an instance of GenericTimeTimeSeries
by taking the first difference
d
times.
- diff(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries
by taking the first difference d
times.
- DifferencedIntTimeTimeSeries - Class in dev.nm.stat.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 dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- Diffusion - Interface in dev.nm.stat.stochasticprocess.univariate.sde.coefficients
-
This class represents the diffusion term, σ, of a univariate SDE.
- diffusion() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE
-
Get the diffusion coefficient: \(\sigma(t, X_t, Z_t, ...)\).
- DiffusionMatrix - Interface in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
-
The diffusion term, σ, of an SDE takes this form: σ(dt, Xt, Zt, ...).
- DiffusionSigma - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
-
This class implements the diffusion term in the form of a diffusion matrix.
- DiffusionSigma() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
-
- Digamma - Class in dev.nm.analysis.function.special.gamma
-
The digamma function is defined as the logarithmic derivative of the gamma function.
- Digamma() - Constructor for class dev.nm.analysis.function.special.gamma.Digamma
-
- DiGraph<V,E extends Arc<V>> - Interface in dev.nm.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 dev.nm.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 dev.nm.graph.algorithm.shortestpath.Dijkstra
-
- dim - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
the variable dimension
- dim() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the dimension of the process.
- dimension() - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
-
- dimension() - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
-
Get the total number of dimensions of the grid.
- dimension() - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
-
- dimension() - Method in interface dev.nm.analysis.differentialequation.ode.ivp.problem.DerivativeFunction
-
Gets the dimension of y.
- dimension() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Gets the dimension of y.
- dimension() - Method in class dev.nm.geometry.LineSegment
-
Get the dimension of the coordinate space.
- dimension() - Method in class dev.nm.geometry.Point
-
Get the dimension of the coordinate space.
- dimension() - Method in interface dev.nm.geometry.polyline.PolygonalChain
-
Get the number of dimensions of this polygonal chain.
- dimension() - Method in class dev.nm.geometry.polyline.PolygonalChainByArray
-
- dimension() - Method in class dev.nm.misc.datastructure.MultiDimensionalArray
-
- dimension() - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection
-
Returns the number of dimensions of the collection.
- dimension() - Method in interface dev.nm.solver.multivariate.constrained.constraint.Constraints
-
Get the number of variables.
- dimension() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
- dimension() - Method in class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
- dimension() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
-
- dimension() - Method in interface dev.nm.solver.problem.OptimProblem
-
Get the number of variables.
- dimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the dimension of the system, i.e., the dimension of the state vector.
- dimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Gets the dimension of observation yt.
- dimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Gets the dimension of state xt.
- dimension() - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
-
Get the dimension of observation yt.
- dimension() - Method in class dev.nm.stat.dlm.univariate.StateEquation
-
Get the dimension of state xt.
- dimension() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
- dimension() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
- dimension() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix
-
Get the dimension of the process.
- dimension() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get the dimension of the process.
- dimension() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- dimension() - Method in interface dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries
-
Get the dimension of the multivariate time series.
- dimension() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- dimension() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the dimension of multivariate time series.
- dimension() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the dimension of the multivariate time series.
- DimensionCheck - Class in dev.nm.misc.datastructure
-
These are the utility functions for checking table dimension.
- dimensionOfDomain() - Method in class dev.nm.analysis.differentiation.multivariate.GradientFunction
-
- dimensionOfDomain() - Method in class dev.nm.analysis.differentiation.multivariate.HessianFunction
-
- dimensionOfDomain() - Method in class dev.nm.analysis.differentiation.multivariate.JacobianFunction
-
- dimensionOfDomain() - Method in interface dev.nm.analysis.function.Function
-
Get the number of variables the function has.
- dimensionOfDomain() - Method in class dev.nm.analysis.function.matrix.R1toMatrix
-
- dimensionOfDomain() - Method in class dev.nm.analysis.function.matrix.R2toMatrix
-
- dimensionOfDomain() - Method in class dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction
-
- dimensionOfDomain() - Method in class dev.nm.analysis.function.rn2rm.AbstractRealVectorFunction
-
- dimensionOfDomain() - Method in class dev.nm.analysis.function.SubFunction
-
- dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
-
- dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
- dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Gets the dimension of the original (unstacked) problem.
- dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
- dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.SumOfPenalties
-
- dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.ZeroPenalty
-
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
-
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
- dimensionOfRange() - Method in class dev.nm.analysis.differentiation.multivariate.GradientFunction
-
- dimensionOfRange() - Method in class dev.nm.analysis.differentiation.multivariate.HessianFunction
-
- dimensionOfRange() - Method in class dev.nm.analysis.differentiation.multivariate.JacobianFunction
-
- dimensionOfRange() - Method in interface dev.nm.analysis.function.Function
-
Get the dimension of the range space of the function.
- dimensionOfRange() - Method in class dev.nm.analysis.function.matrix.R1toMatrix
-
- dimensionOfRange() - Method in class dev.nm.analysis.function.matrix.R2toMatrix
-
- dimensionOfRange() - Method in class dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction
-
- dimensionOfRange() - Method in class dev.nm.analysis.function.rn2rm.AbstractRealVectorFunction
-
- dimensionOfRange() - Method in class dev.nm.analysis.function.SubFunction
-
- dimensionOfRange() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
-
- dimensionOfRange() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- dimensionOfRange() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
- dimensionOfRange() - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyFunction
-
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
-
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
- DirichletDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.multivariate.DirichletDistribution
-
Constructs an instance of Dirichlet distribution.
- DirichletDistribution(double[]) - Constructor for class dev.nm.stat.distribution.multivariate.DirichletDistribution
-
Constructs an instance of Dirichlet distribution.
- dis_m_LF_list - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
whole m_LF_zxi_list(including the modified Stieltjes transform of
endpoints)
- DiscreteHMM - Class in dev.nm.stat.hmm.discrete
-
This is the discrete hidden Markov model as defined by Rabiner.
- DiscreteHMM(Vector, Matrix, Matrix) - Constructor for class dev.nm.stat.hmm.discrete.DiscreteHMM
-
Constructs a discrete hidden Markov model.
- DiscreteHMM(DiscreteHMM) - Constructor for class dev.nm.stat.hmm.discrete.DiscreteHMM
-
Copy constructor.
- DiscreteSDE - Interface in dev.nm.stat.stochasticprocess.univariate.sde.discrete
-
This interface represents the discrete approximation of a univariate SDE.
- Discretization(double, double, double) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid.Discretization
-
Constructs a discretization of an interval.
- discretization - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
-
- dispersion(Vector, Vector, int) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
-
Different distribution models have different ways to compute dispersion, Φ.
- dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
-
- dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
-
- dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
-
- dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
-
- dist - Variable in class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
- distance(Point) - Method in class dev.nm.geometry.LineSegment
-
Calculate the shortest distance between a point and this line segment in Euclidean geometry.
- distance(Point) - Method in class dev.nm.geometry.Point
-
Compute the Euclidean distance between this point and the given point.
- distance(V) - Method in class dev.nm.graph.algorithm.shortestpath.Dijkstra
-
- distance(V) - Method in interface dev.nm.graph.algorithm.shortestpath.ShortestPath
-
Gets the shortest distance from the source to a vertex.
- distribution() - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
- distribution() - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
-
- DiversificationMeasure - Interface in tech.nmfin.portfoliooptimization.corvalan2005.diversification
-
Defines the diversification of a portfolio.
- divide(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- divide(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- divide(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- divide(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
A vector is divided by another vector, element-by-element.
- divide(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- divide(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Divide this
by that
, entry-by-entry.
- divide(F) - Method in interface dev.nm.algebra.structure.Field
-
/ : F × F → F
That is the same as
this.multiply(that.inverse())
- divide(Complex) - Method in class dev.nm.number.complex.Complex
-
Compute the quotient of this complex number divided by another complex number.
- divide(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
-
- divide(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
-
Divide one double
array by another, entry-by-entry.
- divide(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
-
- divide(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
-
- divide(Real) - Method in class dev.nm.number.Real
-
- divide(Real, int) - Method in class dev.nm.number.Real
-
/ : R × R → R
Divide this number by another one.
- DividedDifferences - Class in dev.nm.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 dev.nm.analysis.curvefit.interpolation.univariate.DividedDifferences
-
Construct divided differences from a given collection of ordered pairs.
- DividedDifferences(SortedOrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.DividedDifferences
-
Construct divided differences from a given sorted collection of ordered pairs.
- dk - Variable in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
the line search direction at the k-th iteration
- DLM - Class in dev.nm.stat.dlm.univariate
-
This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.
- DLM(double, double, ObservationEquation, StateEquation) - Constructor for class dev.nm.stat.dlm.univariate.DLM
-
Construct a univariate controlled dynamic linear model.
- DLM(DLM) - Constructor for class dev.nm.stat.dlm.univariate.DLM
-
Copy constructor.
- DLMSeries - Class in dev.nm.stat.dlm.univariate
-
This is a simulator for a multivariate controlled dynamic linear model process.
- DLMSeries(int, DLM, double[], RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.DLMSeries
-
Simulate a univariate controlled dynamic linear model process.
- DLMSeries(int, DLM, double[]) - Constructor for class dev.nm.stat.dlm.univariate.DLMSeries
-
Simulate a univariate controlled dynamic linear model process.
- DLMSeries(int, DLM) - Constructor for class dev.nm.stat.dlm.univariate.DLMSeries
-
Simulate a univariate controlled dynamic linear model process.
- DLMSeries.Entry - Class in dev.nm.stat.dlm.univariate
-
This is the TimeSeries.Entry
for a univariate DLM time series.
- DLMSim - Class in dev.nm.stat.dlm.univariate
-
This is a simulator for a univariate controlled dynamic linear model process.
- DLMSim(DLM, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.DLMSim
-
Simulate a univariate controlled dynamic linear model process.
- DLMSim.Innovation - Class in dev.nm.stat.dlm.univariate
-
a simulated innovation
- dof() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
-
Gets the degree of freedom in the factor analysis model.
- DOKSparseMatrix - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Construct a sparse matrix in DOK format.
- DOKSparseMatrix(int, int, int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Construct a sparse matrix in DOK format.
- DOKSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
Copy constructor.
- domain - Variable in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
the integer values the variable can take
- Doolittle - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
Runs Doolittle algorithm on a square matrix for LU decomposition.
- Doolittle(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
Runs Doolittle algorithm on a square matrix for LU decomposition.
- Doolittle(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
Runs Doolittle algorithm on a square matrix for LU decomposition.
- Doolittle(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
Runs Doolittle algorithm on a square matrix for LU decomposition.
- dotProduct(long[], long[]) - Static method in class dev.nm.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 dev.nm.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 dev.nm.number.DoubleUtils
-
Convert a double
array to an int
array, rounding down if
necessary.
- doubleArray2List(double...) - Static method in class dev.nm.number.DoubleUtils
-
Convert a double
array to a list.
- DoubleArrayMath - Class in dev.nm.number.doublearray
-
These are the math functions that operate on double[]
.
- DoubleArrayOperation - Interface in dev.nm.number.doublearray
-
It is possible to provide different implementations for different platforms, hardware, etc.
- DoubleBruteForceMinimizer - Class in dev.nm.solver.multivariate.unconstrained
-
This implementation solves an unconstrained minimization problem by
brute force search for all given possible values.
- DoubleBruteForceMinimizer(boolean) - Constructor for class dev.nm.solver.multivariate.unconstrained.DoubleBruteForceMinimizer
-
- DoubleExponential - Class in dev.nm.analysis.integration.univariate.riemann.substitution
-
This transformation speeds up the convergence of the Trapezoidal Rule exponentially.
- DoubleExponential(UnivariateRealFunction, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
Construct a DoubleExponential
substitution rule by trying to automatically determine the substitution rule.
- DoubleExponential4HalfRealLine - Class in dev.nm.analysis.integration.univariate.riemann.substitution
-
This transformation is good for the region \((0, +\infty)\).
- DoubleExponential4HalfRealLine(UnivariateRealFunction, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential4HalfRealLine
-
Construct a DoubleExponential4HalfRealLine
substitution rule.
- DoubleExponential4RealLine - Class in dev.nm.analysis.integration.univariate.riemann.substitution
-
This transformation is good for the region \((-\infty, +\infty)\).
- DoubleExponential4RealLine(UnivariateRealFunction, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential4RealLine
-
Construct a DoubleExponential4RealLine
substitution rule.
- doubleMod(double, double) - Static method in class dev.nm.number.DoubleUtils
-
Double modulo, analogous to x % y when x and y are ints.
- DoubleUtils - Class in dev.nm.number
-
These are the utility functions to manipulate double
and int
.
- DoubleUtils.ifelse - Interface in dev.nm.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 dev.nm.number
-
the available schemes to round a number
- DoubleUtils.which - Interface in dev.nm.number
-
Decide whether x satisfies the boolean
test.
- doubleValue() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
Construct a
DenseMatrix
equivalent of this Complex matrix (rounded if necessary).
- doubleValue() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
Construct a
DenseMatrix
equivalent of this Real matrix (rounded if necessary).
- doubleValue() - Method in class dev.nm.number.complex.Complex
-
- doubleValue() - Method in class dev.nm.number.Real
-
- doubleValue() - Method in class dev.nm.number.ScientificNotation
-
- DPolynomial - Class in dev.nm.analysis.differentiation.univariate
-
This is the first order derivative function of a
Polynomial
, which, again, is a polynomial.
- DPolynomial(Polynomial) - Constructor for class dev.nm.analysis.differentiation.univariate.DPolynomial
-
Construct the derivative function of a
Polynomial
, which, again, is a polynomial.
- DQDS - Class in dev.nm.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 dev.nm.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 tech.nmfin.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 dev.nm.stat.stochasticprocess.univariate.sde.coefficients
-
This class represents the drift term, μ, of a univariate SDE.
- drift() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE
-
Get the drift: \(\mu(t,X_t,Z_t,...)\).
- drift1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
-
- drift2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
-
- DriftVector - Interface in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
-
The drift term, μ, of an SDE takes this form: μ(dt, Xt, Zt, ...).
- drop(int) - Method in class dev.nm.stat.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 dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries
by dropping the leading
nItems
entries.
- drop(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Construct an instance of GenericTimeTimeSeries
by dropping the leading nItems
entries.
- drop(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries
by dropping the leading nItems
entries.
- dropFactor(int) - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
-
Drops the indexed factor.
- dt - Variable in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
-
- dt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the current time differential.
- dt(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the i-th time increment.
- dt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get all the time increments.
- dt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Get the current time differential.
- dt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
-
Get the time interval \(\delta_t\) of this generator.
- dt - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
-
- du() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
-
Get an array of the measure values.
- du() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDB
-
- du() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDt
-
- dUdx(RealVectorFunction) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
-
Gets the derivative of the potential function, given the derivative of the log density.
- DuplicatedAbscissae - Exception in dev.nm.analysis.function.tuple
-
This exception is thrown when a function has two same x-abscissae, hence ill-defined.
- DuplicatedAbscissae() - Constructor for exception dev.nm.analysis.function.tuple.DuplicatedAbscissae
-
Construct an instance.
- DuplicatedAbscissae(String) - Constructor for exception dev.nm.analysis.function.tuple.DuplicatedAbscissae
-
Construct a DuplicatedAbscissae
runtime exception with an error message.
- DVInv() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
Computes D / V(μ).
- dWt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the increment of the driving Brownian motion during the time differential.
- dWt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Get the increment of the driving Brownian motion during the time differential.
- dx(BivariateGrid, int, int) - Method in interface dev.nm.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 dev.nm.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
-
- dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
- dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
-
- dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
- dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
- dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
- dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
-
- dx() - Method in interface dev.nm.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 dev.nm.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 dev.nm.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
-
- dXt(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
- dXt(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE
-
This is the SDE specification of a stochastic process.
- dXt(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
-
This is the SDE specification of a stochastic process.
- dXt(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
-
- dXt(Ft) - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.discrete.DiscreteSDE
-
This is the SDE specification of a stochastic process.
- dXt(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.EulerSDE
-
This is the SDE specification of a stochastic process.
- dXt(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE
-
This is the SDE specification of a stochastic process.
- dy(BivariateGrid, int, int) - Method in interface dev.nm.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 dev.nm.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
-
- dy() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Gets the first order derivative function.
- DynamicCreator - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation
-
Performs the Dynamic Creation algorithm (DC) to generate parameters for
MersenneTwister
.
- DynamicCreator(MersenneExponent, long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.DynamicCreator
-
Constructs a new instance which aims to generate MT RNGs with the given period.
- DynamicCreatorException - Exception in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState
-
Constructs a new instance with the given position and momentum.
- E() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets E, the residual matrix.
- E() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets E, the residual matrix.
- e_t(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets the residual of all subject at time t.
- e_t(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets the residual of all subject at time t.
- Edge<V> - Interface in dev.nm.graph
-
An edge connects a pair of vertices.
- EdgeBetweeness<V> - Class in dev.nm.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 dev.nm.graph.community.EdgeBetweeness
-
Computes the edge-betweeness-es of all edges in an undirected graph.
- edges() - Method in class dev.nm.graph.community.EdgeBetweeness
-
Gets the set of all edges in the graph.
- edges() - Method in interface dev.nm.graph.Graph
-
Gets the set of all edges in this graph.
- edges(V) - Method in interface dev.nm.graph.Graph
-
Gets the set of all edges associated with a vertex in this graph.
- edges() - Method in class dev.nm.graph.type.SparseGraph
-
- edges(V) - Method in class dev.nm.graph.type.SparseGraph
-
- edges() - Method in class dev.nm.graph.type.SparseTree
-
- edges(V) - Method in class dev.nm.graph.type.SparseTree
-
- edges() - Method in class dev.nm.graph.type.VertexTree
-
- edges(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- Eigen - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Compute the eigenvalues and eigenvectors for a square matrix.
- Eigen(Matrix, Eigen.Method) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Compute the eigenvalues and eigenvectors for a square matrix.
- Eigen(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
- Eigen(Matrix, Eigen.Method, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Compute the eigenvalues and eigenvectors for a square matrix.
- eigen() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
-
Gets the eigenvalue decomposition of the correlation (or covariance)
matrix.
- Eigen.Method - Enum in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
-
the available methods to compute eigenvalues and eigenvectors
- eigenbasis() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get the eigenvectors.
- EigenBoundUtils - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
-
Utility methods for computing bounds of eigenvalues.
- EigenCount - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCount
-
Creates an instance for a symmetric tridiagonal matrix T.
- EigenCountInRange - Class in dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
-
Runs the eigen decomposition on a square matrix.
- EigenDecomposition(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
-
Runs the eigen decomposition on a square matrix.
- EigenProperty - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get the eigenvalue.
- EigenvalueByDQDS - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds
-
Computes all the eigenvalues of a symmetric tridiagonal matrix.
- EigenvalueByDQDS(TridiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.EigenvalueByDQDS
-
Computes all the eigenvalues of a symmetric tridiagonal matrix.
- eigenVector() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get an eigenvector.
- ELECTRIC_EPSILON0 - Static variable in class dev.nm.misc.PhysicalConstants
-
The electric permittivity or electrical constant \(\epsilon_0\) in farads per meter
(F m-1).
- ELECTRON_MASS_ME - Static variable in class dev.nm.misc.PhysicalConstants
-
The electron rest mass \(m_e\) in kilograms (kg).
- ELECTRON_VOLT_EV - Static variable in class dev.nm.misc.PhysicalConstants
-
The electron volt \(eV\) in joules (J).
- ELEMENTARY_CHARGE_E - Static variable in class dev.nm.misc.PhysicalConstants
-
The elementary charge \(e\) in coulombs (C).
- ElementaryFunction - Class in dev.nm.number.complex
-
This class contains some elementary functions for complex number,
Complex
.
- ElementaryFunction() - Constructor for class dev.nm.number.complex.ElementaryFunction
-
- ElementaryOperation - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Construct a transformation matrix of elementary operations.
- ElementaryOperation(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Construct a transformation matrix of elementary operations.
- ElementaryOperation(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Transform A by elementary operations.
- elementDivide(Matrix, Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
- elementMultiply(Matrix, Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
- elementOperation(Matrix, Matrix, BivariateRealFunction) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
- eliminate(GLMProblem, Matrix) - Method in interface dev.nm.stat.regression.linear.glm.modelselection.BackwardElimination.Step
-
- eliminate(GLMProblem, Matrix) - Method in class dev.nm.stat.regression.linear.glm.modelselection.EliminationByAIC
-
- eliminate(GLMProblem, Matrix) - Method in class dev.nm.stat.regression.linear.glm.modelselection.EliminationByZValue
-
- EliminationByAIC - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.glm.modelselection.EliminationByAIC
-
- EliminationByZValue - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.glm.modelselection.EliminationByZValue
-
Creates an instance with the given significance level [0, 1].
- Elliott2005DLM - Class in tech.nmfin.meanreversion.elliott2005
-
This class implements the Kalman filter model as in Elliott's paper.
- Elliott2005DLM(double, double, double, double, double, RandomStandardNormalGenerator) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Constructs an Elliott's Kalman filter model.
- Elliott2005DLM(double, double, double, double, double) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Constructs an Elliott's Kalman filter model.
- Elliott2005DLM(Elliott2005DLM) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Copy constructor.
- ElliottOnlineFilter - Class in tech.nmfin.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 tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter
-
- ElliottOnlineFilter(double[], double, double, double, double, int) - Constructor for class tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter
-
- ElliottOnlineFilter.NoModelFitted - Exception in tech.nmfin.meanreversion.elliott2005
-
- EmpiricalACER - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
- EmpiricalACEREstimation - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
-
This class estimates empirical ACER values from the given observations.
- EmpiricalACEREstimation() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation
-
Create an instance with default values.
- EmpiricalACEREstimation(int, boolean, EpsilonStatisticsCalculator, int) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation
-
Create an instance for counting empirical ACERs.
- EmpiricalACERStatistics - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
-
This class contains the computed statistics of the estimated ACERs.
- EmpiricalACERStatistics(double[], double[]) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
Construct an empirical distribution from a sample.
- EmpiricalDistribution(double[]) - Constructor for class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
- end() - Method in class dev.nm.interval.Interval
-
Get the end of this interval.
- ENDING_OF_TIME - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
This represents a time after all (representable) times.
- ENDING_OF_TIME_LONG - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
This represents a time after all (representable) times, in long
representation.
- energy(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AfterNoImprovement
-
- entropy() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
- entropy() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the entropy of this distribution.
- entropy() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
-
- entropy() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
Gets the entropy of this distribution.
- entropy() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.TDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
- entropy() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
-
- entropy() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
-
- entropy() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
- entropy() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
- entropy() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
-
- entropy() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
- entropy() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
- entropy() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- entropy() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- entropy() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- entropy() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- entropy() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Deprecated.
- entropy() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Deprecated.
- Entry(MatrixCoordinate, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
Construct a sparse entry in a sparse matrix.
- Entry(int, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
- Entry(T, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
-
Construct an instance of TimeSeries.Entry
.
- Entry(int, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateIntTimeTimeSeries.Entry
-
Construct an instance of Entry
.
- Entry(double, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.MultivariateRealization.Entry
-
Construct an instance of TimeSeries.Entry
.
- Entry(int, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.IntTimeTimeSeries.Entry
-
Construct an instance of TimeSeries.Entry
.
- Entry(double, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.Realization.Entry
-
Construct an instance of TimeSeries.Entry
.
- Entry(T, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
-
Construct an instance of Entry
.
- EPSILON - Static variable in class dev.nm.misc.Constants
-
the default epsilon used in this library
- epsilon - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- epsilon() - Method in interface dev.nm.solver.multivariate.constrained.integer.IPProblem
-
Get the threshold to check whether a variable is an integer.
- epsilon() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
- epsilon() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- epsilon - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- epsilon - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
a precision parameter: when a number |x| ≤ ε, it is considered 0
- epsilon - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer
-
the convergence tolerance
- epsilon1 - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
- epsilon2 - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
- EpsilonStatisticsCalculator - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.number.DoubleUtils
-
Check if two double
s are close enough, hence equal.
- equal(double[], double[], double) - Static method in class dev.nm.number.DoubleUtils
-
Check if two double
arrays are close enough, hence equal,
entry-by-entry.
- equal(double[][], double[][], double) - Static method in class dev.nm.number.DoubleUtils
-
Check if two 2D arrays, double[][]
, are close enough, hence
equal, entry-by-entry.
- equal(int[], int[]) - Static method in class dev.nm.number.DoubleUtils
-
Check if two int
arrays, int[]
, are equal,
entry-by-entry.
- equal(Number, Number, double) - Static method in class dev.nm.number.NumberUtils
-
Check the equality of two
Number
s, up to a precision.
- EqualityConstraints - Interface in dev.nm.solver.multivariate.constrained.constraint
-
The domain of an optimization problem may be restricted by equality constraints.
- EqualityConstraints(Vector, SymmetricMatrix, SymmetricMatrix[]) - Constructor for class dev.nm.solver.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 dev.nm.solver.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
- equals(SparseMatrix, SparseMatrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- equals(Object) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- equals(Object) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- equals(Object) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
- equals(Object) - Method in class dev.nm.analysis.function.tuple.Pair
-
- equals(Graph<V, ?>, Graph<V, ?>) - Static method in class dev.nm.graph.GraphUtils
-
Check if two graphs are equal in terms of node values and edges.
- equals(Object) - Method in class dev.nm.interval.Interval
-
- equals(Object) - Method in class dev.nm.interval.Intervals
-
- equals(Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
- equals(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- equals(Object) - Method in class dev.nm.misc.datastructure.SortableArray
-
- equals(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Check if two BigDecimal
s are equal up to a precision.
- equals(Object) - Method in class dev.nm.number.complex.Complex
-
- equals(Object) - Method in class dev.nm.number.Real
-
- equals(Object) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
- equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
-
- equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
-
- equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
-
- Erf - Class in dev.nm.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 dev.nm.analysis.function.special.gaussian.Erf
-
- Erfc - Class in dev.nm.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 dev.nm.analysis.function.special.gaussian.Erfc
-
- ErfInverse - Class in dev.nm.analysis.function.special.gaussian
-
The inverse of the Error function is defined as:
\[
\operatorname{erf}^{-1}(x)
\]
- ErfInverse() - Constructor for class dev.nm.analysis.function.special.gaussian.ErfInverse
-
- ErgodicHybridMCMC - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
-
A simple decorator which will randomly vary dt between each sample.
- ErgodicHybridMCMC(double, UnivariateRealFunction, AbstractHybridMCMC) - Constructor for class dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
-
Constructs a new instance where dt is uniformly drawn from a given range.
- error(T) - Method in interface dev.nm.solver.multivariate.minmax.MinMaxProblem
-
e(x, ω) is the error function, or the minmax objective, for a given ω.
- EST - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
EST
- estimate(Matrix) - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016
-
- estimate(double[][]) - Method in class dev.nm.stat.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 dev.nm.stat.random.MeanEstimator
-
Gets an estimator.
- estimate(int) - Method in class dev.nm.stat.random.variancereduction.AntitheticVariates
-
- estimate(int) - Method in class dev.nm.stat.random.variancereduction.CommonRandomNumbers
-
- estimate(int) - Method in class dev.nm.stat.random.variancereduction.ControlVariates
-
- estimate(int) - Method in class dev.nm.stat.random.variancereduction.ImportanceSampling
-
- estimate(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Moments
-
- estimate(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments1
-
- estimate(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments2
-
- estimate(Matrix) - Method in class tech.nmfin.returns.moments.MomentsEstimatorLedoitWolf
-
- estimate(Matrix) - Method in interface tech.nmfin.returns.moments.ReturnsMoments.Estimator
-
Estimates the moments from a given returns matrix.
- EstimateByLogLikelihood - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
- estimated_lambda - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
estimated lambda
- estimatedPopulationEigenvalues(Vector) - Method in class dev.nm.stat.covariance.nlshrink.TauEstimator
-
Estimates population eigenvalues from given sample eigenvalues.
- estimateForMultiPeriods(double[][]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERByCounting
-
Estimate for multiple periods.
- estimateForOnePeriod(double[]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERByCounting
-
Estimate for a single period.
- Estimator - Interface in dev.nm.stat.random
-
- eta() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
-
- EULER_MASCHERONI - Static variable in class dev.nm.misc.Constants
-
the Euler-Mascheroni constant
- EulerMethod - Class in dev.nm.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 dev.nm.analysis.differentialequation.ode.ivp.solver.EulerMethod
-
Constructs an Euler's method instance, with the given step size.
- EulerMethod(int) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.EulerMethod
-
Constructs an Euler's method instance, with the given number of steps.
- EulerSDE - Class in dev.nm.stat.stochasticprocess.univariate.sde.discrete
-
The Euler scheme is the first order approximation of an SDE.
- EulerSDE(SDE) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.EulerSDE
-
Discretize a continuous-time SDE using the Euler scheme.
- evaluate(double) - Method in class dev.nm.analysis.curvefit.interpolation.LinearInterpolator
-
- evaluate(double) - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
- evaluate(double, Vector) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.problem.DerivativeFunction
-
Computes the derivative at the given point, x.
- evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.GradientFunction
-
- evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.HessianFunction
-
- evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.JacobianFunction
-
- evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.MultivariateFiniteDifference
-
Evaluate numerically the partial derivative of f at point x.
- evaluate(Vector, double) - Method in class dev.nm.analysis.differentiation.multivariate.MultivariateFiniteDifference
-
Evaluate numerically the partial derivative of f at point x with step size h.
- evaluate(Vector) - Method in class dev.nm.analysis.differentiation.Ridders
-
Evaluate the function f at x, where x is from the domain.
- evaluate(double) - Method in class dev.nm.analysis.differentiation.Ridders
-
- evaluate(Vector, double) - Method in class dev.nm.analysis.differentiation.Ridders
-
Evaluate numerically the derivative of f at point x, f'(x), with step size h.
- evaluate(double, double) - Method in class dev.nm.analysis.differentiation.univariate.DBeta
-
Evaluate \({\partial \over \partial x} \mathrm{B}(x, y)\).
- evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DBetaRegularized
-
Evaluate \({\partial \over \partial x} \mathrm{B_x}(p, q)\).
- evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DErf
-
- evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.Dfdx
-
- evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DGamma
-
- evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DGaussian
-
- evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.FiniteDifference
-
- evaluate(double, double) - Method in class dev.nm.analysis.differentiation.univariate.FiniteDifference
-
Evaluate numerically the derivative of f at point x,
f'(x), with step size h.
- evaluate(D) - Method in interface dev.nm.analysis.function.Function
-
Evaluate the function f at x, where x is from the domain.
- evaluate(double) - Method in class dev.nm.analysis.function.matrix.R1toConstantMatrix
-
- evaluate(Vector) - Method in class dev.nm.analysis.function.matrix.R1toMatrix
-
- evaluate(double) - Method in class dev.nm.analysis.function.matrix.R1toMatrix
-
Evaluate f(x) = A.
- evaluate(Vector) - Method in class dev.nm.analysis.function.matrix.R2toMatrix
-
- evaluate(double, double) - Method in class dev.nm.analysis.function.matrix.R2toMatrix
-
Evaluate f(x1, x2) = A.
- evaluate(Number) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(double) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(Complex) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction
-
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.AbstractTrivariateRealFunction
-
- evaluate(double, double) - Method in interface dev.nm.analysis.function.rn2r1.BivariateRealFunction
-
Evaluate y = f(x1,x2).
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction
-
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.R1Projection
-
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.RealScalarSubFunction
-
- evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.RealScalarSubFunction
-
Evaluate the function f at x.
- evaluate(double, double, double) - Method in interface dev.nm.analysis.function.rn2r1.TrivariateRealFunction
-
Evaluate y = f(x1,x2,x3).
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction
-
- evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
-
Evaluate y = f(x).
- evaluate(BigDecimal) - Method in class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
-
Evaluate z.
- evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
-
- evaluate(double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.UnivariateRealFunction
-
Evaluate y = f(x).
- evaluate(double) - Method in class dev.nm.analysis.function.rn2rm.AbstractR1RnFunction
-
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2rm.AbstractR1RnFunction
-
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2rm.RealVectorSubFunction
-
- evaluate(double, double) - Method in class dev.nm.analysis.function.special.beta.Beta
-
Evaluate B(x,y).
- evaluate(double) - Method in class dev.nm.analysis.function.special.beta.BetaRegularized
-
Evaluate Ix(p,q).
- evaluate(double) - Method in class dev.nm.analysis.function.special.beta.BetaRegularizedInverse
-
Evaluate \(I^{-1}_{(p,q)}(u)\).
- evaluate(double, double) - Method in class dev.nm.analysis.function.special.beta.LogBeta
-
Compute log(B(x,y))
.
- evaluate(Vector) - Method in class dev.nm.analysis.function.special.beta.MultinomialBetaFunction
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.Digamma
-
- evaluate(double) - Method in interface dev.nm.analysis.function.special.gamma.Gamma
-
Evaluate \(\Gamma(z) = \int_0^\infty e^{-t} t^{z-1} dt\).
- evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.GammaGergoNemes
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.GammaLanczos
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.GammaLanczosQuick
-
- evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaLowerIncomplete
-
Evaluate \(\gamma(s,x)\).
- evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaRegularizedP
-
Evaluate P(s,x).
- evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaRegularizedPInverse
-
Evaluate x = P-1(s,u).
- evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaRegularizedQ
-
Evaluate Q(s,x).
- evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaUpperIncomplete
-
Compute Γ(s,x).
- evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.LogGamma
-
Evaluate the log of the Gamma function in the positive real domain.
- evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.Trigamma
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.CumulativeNormalHastings
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.CumulativeNormalInverse
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.CumulativeNormalMarsaglia
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.Erf
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.Erfc
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.ErfInverse
-
- evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
-
- evaluate(double) - Method in interface dev.nm.analysis.function.special.gaussian.StandardCumulativeNormal
-
Evaluate \(F(x;\,\mu,\sigma^2)\).
- evaluate(Vector) - Method in class dev.nm.analysis.function.special.Rastrigin
-
- evaluate(double) - Method in interface dev.nm.analysis.sequence.Summation.Term
-
Evaluate the term for an index.
- evaluate(Constraints, Vector) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
-
Evaluates the constraints.
- evaluate(Matrix) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.Hp
-
Computes \(H_p(U) = \frac{1}{2}[PUP^{-1}]+P^{-*}U^*P^*\).
- evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
-
- evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Computes the final objective function value.
- evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
-
- evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.CourantPenalty
-
- evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.FletcherPenalty
-
- evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.SumOfPenalties
-
- evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.ZeroPenalty
-
- evaluate(Realization[]) - Method in interface dev.nm.stat.cointegration.JohansenAsymptoticDistribution.F
-
F(B).
- evaluate() - Method in class dev.nm.stat.covariance.nlshrink.quest.QuEST
-
- evaluate(X) - Method in interface dev.nm.stat.distribution.discrete.ProbabilityMassFunction
-
Compute the probability mass for a discrete realization x.
- evaluate(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction
-
Compute the epsilon value.
- evaluate(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERInverseFunction
-
- evaluate(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERLogFunction
-
Compute the log-scale epsilon value at the given barrier level.
- evaluate(double) - Method in class dev.nm.stat.evt.function.ReturnLevel
-
Compute the return level from the given return period.
- evaluate(double) - Method in class dev.nm.stat.evt.function.ReturnPeriod
-
- evaluate(Vector, Vector) - Method in interface dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings.ProposalDensityFunction
-
Evaluates the density at the given points.
- evaluate(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
-
- evaluate(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.HybridMCMCProposalFunction
-
- evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantDriftVector
-
- evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
- evaluate(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix
-
Evaluate the diffusion matrix, σ(dt, Xt, Zt, ...), with respect to a filtration.
- evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
-
- evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ZeroDriftVector
-
- evaluate(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.FtAdaptedRealFunction
-
Evaluate this function, f, at time t.
- evaluate(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.FtAdaptedVectorFunction
-
Evaluate this function, f, at time t.
- evaluate(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Bt
-
- evaluate(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
-
Compute the function value at the i-th time point, \(f(\mathfrak{F_{t_i}})\).
- evaluate(double) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
-
- evaluate(Ft) - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.FtAdaptedFunction
-
Evaluate this function, f, at time t.
- evaluate(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction
-
Evaluate this function, f, based on only the current value of the stochastic process.
- evaluate(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction
-
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
-
- evaluate(double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
-
Get the i-th auto-correlation matrix.
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
-
- evaluate(double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
-
Get the i-th auto-covariance matrix.
- evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
Compute the auto-correlation for lag k
.
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
- evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
-
Compute the auto-covariance for lag k
.
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
-
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
- evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
Compute the partial auto-correlation for lag k
.
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
- evaluate(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
Get the i-th auto-correlation.
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
- evaluate(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
Gets the i-th auto-covariance.
- evaluate(double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta
-
- evaluate(double, double) - Method in interface tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMomentsEstimator
-
- evaluate(double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.NegCetaFunction
-
- evaluate(double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.NPEBPortfolioMomentsEstimator
-
- evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
- evaluate(Vector) - Method in class tech.nmfin.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 tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
- evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
- evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
-
- evaluate(Vector) - Method in class tech.nmfin.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 tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
- EvaluationException(String) - Constructor for exception dev.nm.analysis.function.Function.EvaluationException
-
Constructs an EvaluationException
with the specified detail message.
- EvenlySpacedGrid - Class in dev.nm.stat.stochasticprocess.timegrid
-
This is an evenly spaced time grid.
- EvenlySpacedGrid(double, double, int) - Constructor for class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid
-
Construct an evenly spaced time grid.
- ExceptionUtils - Class in dev.nm.misc
-
Exception-related utility functions.
- execute(Runnable) - Method in class dev.nm.misc.parallel.Mutex
-
The runnable
is executed under synchronization of this
Mutex
instance.
- executeAll(List<? extends Callable<T>>) - Method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the exponential of a vector, element-by-element.
- exp(double) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute ex.
- exp(double, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute ex.
- exp(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute ex.
- exp(BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute ex.
- exp(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Exponential of a complex number.
- exp(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the exponentials of values.
- ExpectationAtEndTime - Class in dev.nm.stat.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 dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Compute the expectation of a random process at the end time.
- ExpectationAtEndTime(RandomProcess, int, int) - Constructor for class dev.nm.stat.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 dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Compute the expectation of a stochastic SDE at the end time.
- expectedPnL() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
-
Gets the expected PnL when playing the H strategy in mean-reverting
model.
- ExplicitCentralDifference1D - Class in dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.ExplicitCentralDifference1D
-
- ExplicitCentralDifference2D - Class in dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.ExplicitCentralDifference2D
-
- ExplicitImplicitModelPCA - Class in dev.nm.stat.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 dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
-
- ExplicitImplicitModelPCA(Matrix, Matrix, double) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
-
- ExplicitImplicitModelPCA.Result - Class in dev.nm.stat.factor.implicitmodelpca
-
- expm1(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the exponential-minus-one (ex - 1) of values.
- exponent() - Method in class dev.nm.number.ScientificNotation
-
Get the exponent.
- Exponential - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
-
Construct an Exponential
substitution rule.
- ExponentialDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.ExponentialDistribution
-
Construct an instance of the standard exponential distribution, where the rate/lambda is 1.
- ExponentialDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.ExponentialDistribution
-
Construct an exponential distribution.
- ExponentialFamily - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.exponentialfamily.ExponentialFamily
-
Construct a factory to construct probability distribution in the exponential family of this
form.
- ExponentialMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
-
The HMM states use the Exponential distribution to model the observations.
- ExponentialMixtureDistribution(Double[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
-
Constructs an Exponential distribution for each state in the HMM model.
- ExpTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
-
Logarithmic decay, where \(T_k = T_0 * 0.95^k\).
- ExpTemperatureFunction(double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.ExpTemperatureFunction
-
Constructs a new instance with an initial temperature.
- exsec(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the exsecant of an angle.
- extractPeaks(double[]) - Static method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERUtils
-
Extract peaks (values which are preceded and followed by values smaller than itself).
- extrema() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
-
- ExtremalGeneralizedEigenvalueByGreedySearch - Class in tech.nmfin.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 tech.nmfin.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 tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
-
Constructs the problem described in Section 3.2, d'Aspremont (2008).
- ExtremalGeneralizedEigenvalueBySDP - Class in tech.nmfin.meanreversion.daspremont2008
-
Solves the problem described in Section 3.2, d'Aspremont (2008).
- ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix) - Constructor for class tech.nmfin.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 tech.nmfin.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 tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
-
Constructs the problem described in Section 3.2, d'Aspremont (2008).
- ExtremalGeneralizedEigenvalueSolver - Interface in tech.nmfin.meanreversion.daspremont2008
-
- ExtremalIndexByClusterSizeReciprocal - Class in dev.nm.stat.evt.exi
-
This class estimates the extremal index by the reciprocal of the average cluster size.
- ExtremalIndexByClusterSizeReciprocal(double) - Constructor for class dev.nm.stat.evt.exi.ExtremalIndexByClusterSizeReciprocal
-
- ExtremalIndexByClusterSizeReciprocal(double, int) - Constructor for class dev.nm.stat.evt.exi.ExtremalIndexByClusterSizeReciprocal
-
Create an instance with the given threshold and clustering interval length.
- ExtremalIndexByFerroSeegers - Class in dev.nm.stat.evt.exi
-
This class estimates the extremal index from observations by the algorithm proposed by Ferro and
Seegers.
- ExtremalIndexByFerroSeegers(double) - Constructor for class dev.nm.stat.evt.exi.ExtremalIndexByFerroSeegers
-
Create an instance with the given threshold.
- ExtremalIndexEstimation - Interface in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.evt.markovchain
-
Types of marginal distribution of each simulated value.
- Ey(Vector) - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
-
- Ey(Vector) - Method in class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
- Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
-
- Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP
-
- Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
-
- Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP
-
- Ey(Vector) - Method in interface dev.nm.stat.regression.linear.LinearModel
-
Computes the expectation \(E(y(x))\) given an input.
- Ey(Vector) - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
-
Calculates the probability of occurrence (y = 1).
- Ey(Vector) - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
-
- Ey(Vector, Vector, boolean) - Static method in class dev.nm.stat.regression.linear.ols.OLSRegression
-
- F() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
-
Get the differential, \(y^{(n)} = F\).
- f(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
The forcing term.
- f(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
-
Gets the value of the initial condition of u at x.
- f(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
-
Get the initial condition of u at the given point (x,y).
- f(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the initial condition of u at a given position x.
- f(double) - Method in class dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
-
Gets the initial condition of u at the given point (x,y).
- f - Variable in class dev.nm.analysis.function.SubFunction
-
the original, unrestricted function
- f() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- f() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- f() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- f() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- f - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- f() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
- f() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
- f() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- f() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
- f() - Method in class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
- f() - Method in class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
- F - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
the scaling factor
- f() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
Get the objective function.
- f - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- f() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
-
- f() - Method in interface dev.nm.solver.problem.OptimProblem
-
Get the objective function.
- f - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- F() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
-
Gets the sample constant correlation matrix F.
- F - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
unique dis_G_list
- F(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Gets F(t), the coefficient matrix of xt.
- F(int) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
-
Get F(t), the coefficient of xt.
- F() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets F, the implicit factor value matrix.
- f(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets the implicit factor values at time t.
- F() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets F, the factor value matrix.
- f(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets the factor values at time t.
- f() - Method in interface dev.nm.stat.regression.linear.panel.PanelData.Transformation
-
Gets the transformation.
- f - Variable in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
-
the integrand
- F - Class in dev.nm.stat.test.variance
-
The F-test tests whether two normal populations have the same variance.
- F(double[], double[]) - Constructor for class dev.nm.stat.test.variance.F
-
Perform the F-test to test for equal variance of two normal populations.
- F(double[], double[], double) - Constructor for class dev.nm.stat.test.variance.F
-
Perform the F-test to test for equal variance of two normal populations.
- F_idx - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
indices for dis_G_list which is unique
- F_Sum_BtDt - Class in dev.nm.stat.stochasticprocess.univariate.filtration
-
This represents a function of this integral
\[
I = \int_{0}^{1} B(t)dt
\]
- F_Sum_BtDt() - Constructor for class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_BtDt
-
- F_Sum_tBtDt - Class in dev.nm.stat.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 dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt
-
- FactorAnalysis - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.factor.factoranalysis.FactorAnalysis
-
Performs factor analysis on the data set with a user defined scoring
rule.
- FactorAnalysis(Matrix, int) - Constructor for class dev.nm.stat.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 dev.nm.stat.factor.factoranalysis
-
These are the different ways to compute the factor analysis scores.
- factorial(int) - Static method in class dev.nm.analysis.function.FunctionOps
-
n!
- factorial(int) - Static method in class dev.nm.number.big.BigIntegerUtils
-
Compute the n factorial.
- factory - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- factoryCtor - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- FAEstimator - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.glm.quasi.QuasiGLMProblem
-
Gets the quasi-family specification.
- FARADAY_F - Static variable in class dev.nm.misc.PhysicalConstants
-
The Faraday constant \(F\) in coulombs per mole (C mol-1).
- FastAnnealingFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
-
Matlab default: @annealingfast - The step has length temperature, with direction uniformly at
random.
- FastAnnealingFunction(int, RandomStandardNormalGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.FastAnnealingFunction
-
Constructs a new instance where the RVG is created from a given RLG.
- FastAnnealingFunction(RandomVectorGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.FastAnnealingFunction
-
Constructs a new instance that uses a given RVG.
- FastKroneckerProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
This is a fast and memory-saving implementation of computing the Kronecker product.
- FastKroneckerProduct(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
Construct a Kronecker product for read-only.
- FastTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
-
Linear decay, where \(T_k = T_0 / k\).
- FastTemperatureFunction(double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.FastTemperatureFunction
-
Constructs a new instance with an initial temperature.
- FDistribution - Class in dev.nm.stat.distribution.univariate
-
The F distribution is the distribution of the ratio of two independent chi-squared variates.
- FDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.FDistribution
-
Construct an F distribution.
- fdx(UnivariateRealFunction) - Method in class dev.nm.analysis.integration.univariate.riemann.ChangeOfVariable
-
Get the integrand in the "transformed" integral, g(t) = f(x(t)) * x'(t).
- FerrisMangasarianWrightPhase1 - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
The scheme 2 procedure removes equalities and free variables.
- FerrisMangasarianWrightScheme2(SimplexTable) - Constructor for class dev.nm.solver.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 dev.nm.analysis.sequence
-
A Fibonacci sequence starts with 0 and 1 as the first two numbers.
- Fibonacci(int) - Constructor for class dev.nm.analysis.sequence.Fibonacci
-
Construct a Fibonacci sequence.
- FibonaccMinimizer - Class in dev.nm.solver.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 dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer
-
Construct a univariate minimizer using the Fibonacci method.
- FibonaccMinimizer.Solution - Class in dev.nm.solver.univariate.bracketsearch
-
This is the solution to a Fibonacci's univariate optimization.
- Field<F> - Interface in dev.nm.algebra.structure
-
As an algebraic structure, every field is a ring, but not every ring is a field.
- Field.InverseNonExistent - Exception in dev.nm.algebra.structure
-
This is the exception thrown when the inverse of a field element does not exist.
- fillInStackTrace() - Method in error dev.nm.misc.license.LicenseError
-
- Filter - Interface in dev.nm.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 dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Filter the observations.
- filtering(MultivariateIntTimeTimeSeries) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Filter the observations without control variable.
- filtering(double[], double[]) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Filter the observations.
- filtering(double[]) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Filter the observations without control variable.
- filterPrices(Matrix) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
-
Filters out invalid prices.
- filterUnchangedPrices(Matrix) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
-
Filters out prices that are unchanged between consecutive times.
- Filtration - Class in dev.nm.stat.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 dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Construct a Filtration
from a Brownian path.
- FiltrationFunction - Class in dev.nm.stat.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 dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
-
- findClusters(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
-
- findFeasiblePoint(Matrix, Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
-
- findPosition(VertexTree<T>) - Static method in class dev.nm.graph.type.VertexTree
-
Finds the position of a node from its tree root recursively.
- findVertex(Matrix, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
-
- FINE_STRUCTURE_ALPHA - Static variable in class dev.nm.misc.PhysicalConstants
-
The fine-structure constant \(\alpha\) (dimensionless).
- finishTime - Variable in class dev.nm.graph.algorithm.traversal.DFS.Node
-
- finishTime() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node
-
Gets the finish time, the time to finish visiting its sub-tree, of this node.
- FiniteDifference - Class in dev.nm.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 dev.nm.analysis.differentiation.univariate.FiniteDifference
-
Construct an approximate derivative function for f using finite
difference.
- FiniteDifference.Type - Enum in dev.nm.analysis.differentiation.univariate
-
the available types of finite difference
- FirstGeneration - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
-
This interface allows customization of how the first pool of chromosomes is generated.
- FirstOrderMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
-
Construct a multivariate minimizer using the First-Order method.
- FirstOrderMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
-
Construct a multivariate minimizer using the First-Order method.
- FirstOrderMinimizer.Method - Enum in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
-
the available methods to do line search
- FisherExactDistribution - Class in dev.nm.stat.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 dev.nm.stat.test.distribution.pearson.FisherExactDistribution
-
Construct the distribution for Fisher's exact test.
- FisherExactDistribution(int[], int[], int) - Constructor for class dev.nm.stat.test.distribution.pearson.FisherExactDistribution
-
Construct the distribution for Fisher's exact test.
- fit(OrderedPairs) - Method in interface dev.nm.analysis.curvefit.CurveFitting
-
Fit a real valued function from a discrete set of data points.
- fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite
-
- fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline
-
- fit(OrderedPairs) - Method in interface dev.nm.analysis.curvefit.interpolation.univariate.Interpolation
-
Fit a real valued function from a discrete set of data points.
- fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.LinearInterpolation
-
- fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.NewtonPolynomial
-
- fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.LeastSquares
-
- fit(double[], double[], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.LinearFit
-
Fit the ACER function with OLS.
- fit(EmpiricalACER, double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
-
Fit ACER function with empirical ACER estimates.
- fit(double[], double[], double[], double, double) - Method in class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.MaximumLikelihoodFitting
-
Fit the model with the given observations.
- fit(double[]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThreshold
-
Fits the observations to a generalized Pareto distribution (GPD).
- fit(double[]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThresholdOnClusters
-
- fit(GLMProblem, Vector) - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
-
Fits a Generalized Linear Model.
- fit(GLMProblem, Vector) - Method in class dev.nm.stat.regression.linear.glm.IWLS
-
- fit(GLMProblem, Vector) - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
- fit - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- fitModel(double[]) - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995MLE
-
Fits a KST model from returns.
- fitness() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.Chromosome
-
This is the fitness to determine how good this chromosome is.
- fitness() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
- fitted() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the fitted values, y^.
- fittedValues() - Method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel
-
- fittedValues(OLSResiduals[]) - Static method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel
-
- fittedValues() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit
-
- fittedValues() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit
-
- fitWithWeights(double[], double[], double[], double, double, double) - Method in class dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
-
- FixedEffectsModel - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.panel.FixedEffectsModel
-
Constructs a "within" fixed effects model from a panel of data.
- FixedEffectsModel(PanelData, String, String[]) - Constructor for class dev.nm.stat.regression.linear.panel.FixedEffectsModel
-
Constructs a "within" fixed effects model from a panel of data.
- fixing - Variable in class dev.nm.analysis.function.SubFunction
-
the restrictions or fixed values
- fixing() - Method in interface dev.nm.solver.multivariate.constrained.ConstrainedOptimSubProblem
-
Gets the restrictions or fixed values;
- FletcherLineSearch - Class in dev.nm.solver.multivariate.unconstrained.c2.linesearch
-
This is Fletcher's inexact line search method.
- FletcherLineSearch(double, double, double, double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.linesearch.FletcherLineSearch
-
Construct a line search minimizer using the Fletcher method.
- FletcherLineSearch() - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.linesearch.FletcherLineSearch
-
Construct a line search minimizer using the Fletcher method with the
default control parameters.
- FletcherPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the squared costs penalties.
- FletcherPenalty(LessThanConstraints, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.FletcherPenalty
-
Construct a Fletcher penalty function from a collection of inequality constraints.
- FletcherPenalty(LessThanConstraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.FletcherPenalty
-
Construct a Fletcher penalty function from a collection of inequality constraints.
- FletcherPenalty(LessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.FletcherPenalty
-
Construct a Fletcher penalty function from a collection of inequality constraints.
- FletcherReevesMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
-
The Fletcher-Reeves method is a variant of the Conjugate-Gradient method.
- FletcherReevesMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.FletcherReevesMinimizer
-
Construct a multivariate minimizer using the Fletcher-Reeves method.
- FlexibleTable - Class in dev.nm.misc.datastructure
-
This is a 2D table that can shrink or grow by row or by column.
- FlexibleTable(Object[], Object[], double[][]) - Constructor for class dev.nm.misc.datastructure.FlexibleTable
-
Constructs a flexible table that can shrink or grow.
- FlexibleTable(Object[], Object[]) - Constructor for class dev.nm.misc.datastructure.FlexibleTable
-
Constructs a table by row and column labels, initializing the content to 0.
- FlexibleTable(int, int) - Constructor for class dev.nm.misc.datastructure.FlexibleTable
-
Constructs a table using default labeling.
- FlexibleTable(FlexibleTable) - Constructor for class dev.nm.misc.datastructure.FlexibleTable
-
Copy constructor.
- FloatingLicenseServer - Class in dev.nm.misc.license
-
- floatValue() - Method in class dev.nm.number.complex.Complex
-
- floatValue() - Method in class dev.nm.number.Real
-
- floatValue() - Method in class dev.nm.number.ScientificNotation
-
- Fmin(double, int) - Static method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Compute the F critical value.
- fmin - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- fmin - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the best minimum found so far
- fminLast - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- fnext - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the next guess of the minimum
- foreach(Matrix, UnivariateRealFunction) - Static method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.misc.parallel.ParallelExecutor
-
Runs a "foreach" loop in parallel.
- foreach(double[], UnivariateRealFunction) - Static method in class dev.nm.number.DoubleUtils
-
Apply a univariate function f to each element in an array.
- foreachColumn(Matrix, RealVectorFunction) - Static method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
- foreachVector(Collection<Vector>, RealScalarFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
- foreachVector(Vector[], RealVectorFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Applies a real vector function on each input vector.
- foreachVector(Collection<Vector>, RealVectorFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Applies a real vector function on each input vector.
- Forecast(int, double, double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
- Forest<V,E extends HyperEdge<V>> - Interface in dev.nm.graph
-
A forest is a disjoint union of trees.
- forLagOrder(int) - Method in interface dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.AutoCorrelationForObject
-
- forLagOrder(int) - Method in interface dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.AutoCovarianceForObject
-
- forLoop(int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
- forLoop(int, int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Runs a for-loop in parallel.
- forward(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
-
Perform a forward sweep.
- ForwardBackwardProcedure - Class in dev.nm.stat.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 dev.nm.stat.hmm.ForwardBackwardProcedure
-
Constructs the forward and backward probability matrix calculator for an
HMM model.
- ForwardBackwardProcedure(HiddenMarkovModel, int[]) - Constructor for class dev.nm.stat.hmm.ForwardBackwardProcedure
-
Constructs the forward and backward probability matrix calculator for an
HMM model.
- ForwardSelection - Class in dev.nm.stat.regression.linear.glm.modelselection
-
Constructs a GLM model for a set of observations using the forward selection method.
- ForwardSelection(GLMProblem) - Constructor for class dev.nm.stat.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 dev.nm.stat.regression.linear.glm.modelselection.ForwardSelection
-
Constructs a GLM model using the forward selection method.
- ForwardSelection.Step - Interface in dev.nm.stat.regression.linear.glm.modelselection
-
- ForwardSubstitution - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
- foundPositiveDefiniteHessian - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- fractionOfYear(LocalDate, LocalDate, double) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Computes a fraction, in years, based on the number of days between two
given dates.
- FrechetDistribution - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.FrechetDistribution
-
Create an instance with the given parameter values.
- Frobenius(Matrix) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace.Householder
-
- fromPolar(double, double) - Static method in class dev.nm.number.complex.Complex
-
Factory method to construct a complex number from the polar form: (r, θ).
- Fstat() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: F statistics
- Ft() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
-
Compute all function values at all time points.
- ft() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
-
Get an array of the function values.
- ft() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDB
-
- ft() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDt
-
- Ft - Class in dev.nm.stat.stochasticprocess.univariate.sde
-
This represents the concept 'Filtration', the information available at time t.
- Ft() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Construct an empty filtration (no information).
- Ft(Ft) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Copy constructor.
- FtAdaptedFunction - Interface in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.stochasticprocess.univariate.sde
-
This is a filtration implementation that includes the path-dependent information,
Wt.
- FtWt() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
-
Construct an empty filtration (no information).
- FtWt(FtWt) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
-
Copy constructor.
- fullMinimizer() - Method in interface dev.nm.solver.multivariate.constrained.SubProblemMinimizer.IterativeSolution
-
Gets the minimizer to the original problem.
- Function<D,R> - Interface in dev.nm.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 dev.nm.analysis.function
-
- FunctionOps - Class in dev.nm.analysis.function
-
These are some commonly used mathematical functions.
- fw() - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Evaluates \(E(w'r) - q * Var(w'r)\) at w_eff.
- fx() - Method in exception dev.nm.analysis.root.univariate.NoRootFoundException
-
Get f(x).
- g(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
The boundary value function.
- g(double) - Method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.analysis.differentiation.differentiability.C1
-
Get the gradient function, g, of a real valued function f.
- g - Variable in class dev.nm.graph.algorithm.traversal.TraversalFromRoots
-
- g() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
-
- G(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Gets G(t), the coefficient matrix of xt - 1.
- G(int) - Method in class dev.nm.stat.dlm.univariate.StateEquation
-
Get G(t), the coefficient of xt - 1.
- g1(double) - Method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- Gamma() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets Γ, the explicit factor loading matrix.
- gamma(HiddenMarkovModel, int[], Matrix[]) - Static method in class dev.nm.stat.hmm.discrete.BaumWelch
-
Gets the (T-1 * N) γ matrix, where the (t, i)-th
entry is γt(i).
- gamma() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the estimated sequence of gamma (arc length).
- gamma(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the AR coefficient of the i-th lagged differences.
- gamma() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the AR coefficients of the lagged differences; null
if p = 1
- GammaDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.GammaDistribution
-
Construct a Gamma distribution.
- GammaGergoNemes - Class in dev.nm.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 dev.nm.analysis.function.special.gamma.GammaGergoNemes
-
- GammaLanczos - Class in dev.nm.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 dev.nm.analysis.function.special.gamma.GammaLanczos
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLanczos(double, int, int) - Constructor for class dev.nm.analysis.function.special.gamma.GammaLanczos
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLanczosQuick - Class in dev.nm.analysis.function.special.gamma
-
Lanczos approximation, computations are done in double
.
- GammaLanczosQuick() - Constructor for class dev.nm.analysis.function.special.gamma.GammaLanczosQuick
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLanczosQuick(double, int, int) - Constructor for class dev.nm.analysis.function.special.gamma.GammaLanczosQuick
-
Construct an instance of a Gamma function, computed using the Lanczos approximation.
- GammaLowerIncomplete - Class in dev.nm.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 dev.nm.analysis.function.special.gamma.GammaLowerIncomplete
-
- GammaMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
-
The HMM states use the Gamma distribution to model the observations.
- GammaMixtureDistribution(GammaMixtureDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
-
Constructs a Gamma distribution for each state in the HMM model.
- GammaMixtureDistribution(GammaMixtureDistribution.Lambda[], int) - Constructor for class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
-
Constructs a Gamma distribution for each state in the HMM model.
- GammaMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
-
the Gamma distribution parameters
- GammaRegularizedP - Class in dev.nm.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 dev.nm.analysis.function.special.gamma.GammaRegularizedP
-
- GammaRegularizedPInverse - Class in dev.nm.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 dev.nm.analysis.function.special.gamma.GammaRegularizedPInverse
-
- GammaRegularizedQ - Class in dev.nm.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 dev.nm.analysis.function.special.gamma.GammaRegularizedQ
-
- GammaUpperIncomplete - Class in dev.nm.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 dev.nm.analysis.function.special.gamma.GammaUpperIncomplete
-
- GARCH11Model - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
-
An GARCH11 model takes this form.
- GARCH11Model(double, double, double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
-
Construct a GARCH(1,1) model.
- GARCHFit - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit
-
Fit a GARCH(p, q) model to a time series.
- GARCHFit(double[], int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit
-
Fit a GARCH(p, q) model to a time series.
- GARCHFit.GRADIENT - Enum in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
-
the available methods to compute the gradient to guild the optimization search
- GARCHModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
-
The GARCH(p, q) model takes this form.
- GARCHModel(double, double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Construct a GARCH model.
- GARCHModel(GARCHModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Copy constructor.
- GARCHResamplerFactory - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
-
- GARCHResamplerFactory(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
-
- GARCHResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
-
- GARCHResamplerFactory2 - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
-
- GARCHResamplerFactory2(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- GARCHResamplerFactory2() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- GARCHSim - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
-
This class simulates the GARCH models of this form.
- GARCHSim(GARCHModel, double[], RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Simulate an GARCH model.
- GARCHSim(GARCHModel, RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Simulate an GARCH model.
- GARCHSim(GARCHModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Simulate an GARCH model.
- GaussChebyshevQuadrature - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.gaussian.GaussChebyshevQuadrature
-
Create an integrator of order n.
- GaussHermiteQuadrature - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.gaussian.GaussHermiteQuadrature
-
Create an integrator of order n.
- Gaussian - Class in dev.nm.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 dev.nm.analysis.function.special.gaussian.Gaussian
-
Construct an instance of the Gaussian function.
- Gaussian() - Constructor for class dev.nm.analysis.function.special.gaussian.Gaussian
-
Construct an instance of the standard Gaussian function: \(f(x) = e^{-{\frac{(x)^2}{2}}}\)
- GaussianElimination - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Run the Gaussian elimination algorithm.
- GaussianElimination(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Run the Gaussian elimination algorithm with partial pivoting.
- GaussianElimination4SquareMatrix - Class in dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination
-
- GaussianElimination4SquareMatrix(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
Run the Gaussian elimination algorithm on a square matrix.
- GaussianElimination4SquareMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
Run the Gaussian elimination algorithm on a square matrix.
- GaussianProposalFunction - Class in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
-
Constructs a Gaussian proposal function.
- GaussianProposalFunction(double[], RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
-
Constructs a Gaussian proposal function.
- GaussianProposalFunction(double, int, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
-
Constructs a Gaussian proposal function.
- GaussianQuadrature - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature
-
Create a Gaussian quadrature integrator with the given quadrature rule.
- GaussianQuadratureRule - Interface in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
-
This interface defines a Gaussian quadrature rule used in Gaussian quadrature.
- GaussJordanElimination - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Run the Gauss-Jordan elimination algorithm.
- GaussJordanElimination(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Run the Gauss-Jordan elimination algorithm.
- GaussLaguerreQuadrature - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.gaussian.GaussLaguerreQuadrature
-
Create an integrator of order n.
- GaussLegendreQuadrature - Class in dev.nm.analysis.integration.univariate.riemann.gaussian
-
Gauss-Legendre quadrature considers the simplest case of uniform weighting: \(w(x) = 1\).
- GaussLegendreQuadrature(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussLegendreQuadrature
-
Create an integrator of order n.
- GaussNewtonImpl(C2OptimProblem, RntoMatrix) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl
-
- GaussNewtonMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
-
Construct a multivariate minimizer using the Gauss-Newton method.
- GaussNewtonMinimizer.MySteepestDescent - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
-
- GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
-
an implementation of the Gauss-Newton algorithm.
- GaussSeidelSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
Construct a Gauss-Seidel (GS) solver.
- GBMProcess - Class in dev.nm.stat.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 dev.nm.stat.stochasticprocess.univariate.sde.process.GBMProcess
-
Construct a Geometric Brownian motion.
- gcd(int, int) - Static method in class dev.nm.analysis.function.FunctionOps
-
Calculates the greatest common divisor of integer a and integer b.
- GeneralConstraints - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
-
Construct an instance of constraints from a collection of real-valued functions.
- GeneralConstraints(RealScalarFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
-
Construct an instance of constraints from an array of real-valued functions.
- GeneralEqualityConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.general
-
This is the collection of equality constraints for an optimization problem.
- GeneralEqualityConstraints(Collection<RealScalarFunction>) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralEqualityConstraints
-
Constructs an instance of equality constraints from a collection of
real-valued functions.
- GeneralEqualityConstraints(RealScalarFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralEqualityConstraints
-
Constructs an instance of equality constraints from an array of
real-valued functions.
- GeneralGreaterThanConstraints - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
Construct a GCR solver with restarts.
- GeneralizedConjugateResidualSolver(int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
Construct a GCR solver with restarts.
- GeneralizedConjugateResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
Construct a full GCR solver.
- GeneralizedEVD - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
Create an instance of generalized extreme value distribution with the given parameters.
- GeneralizedLinearModel - Class in dev.nm.stat.regression.linear.glm
-
The Generalized Linear Model (GLM) is a flexible generalization of the Ordinary Least Squares
regression.
- GeneralizedLinearModel(GLMProblem, GLMFitting) - Constructor for class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
-
Constructs a GeneralizedLinearModel
instance.
- GeneralizedLinearModel(GLMProblem) - Constructor for class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
-
Solves a generalized linear problem using the Iterative Re-weighted Least Squares algorithm.
- GeneralizedLinearModelQuasiFamily - Class in dev.nm.stat.regression.linear.glm.quasi
-
GLM for the quasi-families.
- GeneralizedLinearModelQuasiFamily(QuasiGLMProblem) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
Constructs a GeneralizedLinearModelQuasiFamily
instance.
- GeneralizedMinimalResidualSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
Construct a GMRES solver with restarts.
- GeneralizedMinimalResidualSolver(int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
Construct a GMRES solver with restarts.
- GeneralizedMinimalResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
Construct a full GMRES solver.
- GeneralizedParetoDistribution - Class in dev.nm.stat.evt.evd.univariate
-
Generalized Pareto distribution (GPD) is used for modeling exceedances over (or shortfalls below)
a threshold.
- GeneralizedParetoDistribution() - Constructor for class dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
Create an instance with zero location, and the given scale and shape parameters.
- GeneralizedParetoDistribution(double, double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
Create an instance with the given parameter values.
- GeneralizedSimulatedAnnealingMinimizer - Class in dev.nm.solver.multivariate.unconstrained.annealing
-
- GeneralizedSimulatedAnnealingMinimizer(int, double, double, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the Generalized Simulated Annealing minimizer.
- GeneralizedSimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
-
Constructs a new instance of the Generalized Simulated Annealing minimizer.
- GeneralLessThanConstraints - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
The vector which is used to generate the Householder vector.
- GenericFieldMatrix<F extends Field<F>> - Class in dev.nm.algebra.linear.matrix.generic.matrixtype
-
This is a generic matrix over a
Field
.
- GenericFieldMatrix(int, int, F) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
Construct a matrix over a field.
- GenericFieldMatrix(F[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
Construct a matrix over a field.
- GenericMatrix<T extends GenericMatrix<T,F>,F extends Field<F>> - Interface in dev.nm.algebra.linear.matrix.generic
-
This class defines a matrix over a field.
- GenericMatrixAccess<F extends Field<F>> - Interface in dev.nm.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 dev.nm.stat.timeseries.datastructure.univariate
-
This is a univariate time series indexed by some notion of time.
- GenericTimeTimeSeries(T[], double[]) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Construct a univariate time series from timestamps and values.
- GeneticAlgorithm - Class in dev.nm.solver.multivariate.geneticalgorithm
-
A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution.
- GeneticAlgorithm(RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Construct an instance of this implementation of genetic algorithm.
- geometricMultiplicity() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
-
Get the dimension of the vector space spanned by the eigenvectors.
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- get(int, int) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixAccess
-
Get the matrix entry at [i,j].
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- get(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- get(int, int) - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- get(int, int) - Method in interface dev.nm.algebra.linear.matrix.generic.GenericMatrixAccess
-
Get the matrix entry at [i,j].
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- get(int, int) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- get(int) - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
-
- get(int) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- get(int) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- get(int) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- get(int) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Get the value at position i.
- get(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
Get the value of a table entry.
- get(int) - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs
-
Get the ordered pair at index i.
- get(int) - Method in class dev.nm.analysis.sequence.Fibonacci
-
- get(int) - Method in interface dev.nm.analysis.sequence.Sequence
-
Get the i-th entry in the sequence, counting from 1.
- get(T) - Method in class dev.nm.combinatorics.Ties
-
Get the number of occurrences of an object.
- get(int) - Method in class dev.nm.interval.Intervals
-
Get the i-th interval.
- get(int, int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
- get(double, int) - Method in class dev.nm.misc.datastructure.MathTable
-
Gets a particular table entry at [i,j].
- get(double, String) - Method in class dev.nm.misc.datastructure.MathTable
-
Gets a particular table entry at [i, "header"].
- get(int) - Method in class dev.nm.misc.datastructure.MathTable.Row
-
Gets the value in the row by column index.
- get(String) - Method in class dev.nm.misc.datastructure.MathTable.Row
-
Gets the value in the row by column name.
- get(int...) - Method in class dev.nm.misc.datastructure.MultiDimensionalArray
-
- get(int...) - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection
-
Returns the element at the specified position in this collection.
- get(int) - Method in class dev.nm.misc.datastructure.SortableArray
-
- get() - Method in class dev.nm.misc.parallel.Reference
-
- get(String) - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
-
- get(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Get the i-th value.
- get(int) - Method in interface dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateIntTimeTimeSeries
-
Get the value at time t
(random access).
- get(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- get(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Get the i-th value.
- get(T) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Get the value at time t
.
- get(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- get(int) - Method in interface dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.IntTimeTimeSeries
-
Get the value at time t
.
- get(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- get(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction
-
Get the auto-correlation of Xi and Xj.
- get(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCovarianceFunction
-
Get the auto-covariance matrix for Xi and Xj.
- get(int, int) - Method in class dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction
-
Get the auto-correlation of xi and xj.
- get(int, int) - Method in class dev.nm.stat.timeseries.linear.univariate.AutoCovarianceFunction
-
Get the auto-covariance of xi and xj.
- get0s(int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets n
0 vectors.
- getAaa(int) - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getACERs() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the estimated epsilon values for different barrier levels per period.
- getActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Get the i-th active index.
- getActiveConstraints(Vector, double) - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
Get the active constraint.
- getActiveIndices() - Method in class dev.nm.misc.algorithm.ActiveSet
-
Get all active indices.
- getActiveRows(Vector, double) - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
Get the active constraint indices.
- getAll() - Method in class dev.nm.analysis.sequence.Fibonacci
-
- getAll() - Method in interface dev.nm.analysis.sequence.Sequence
-
Get a copy of the whole (finite) sequence in double[]
.
- getAllParts(Vector, Map<Integer, Double>) - Static method in class dev.nm.analysis.function.SubFunction
-
Combines the variable and fixed values to form an input to the original
function.
- getAllSubjects() - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the name of all subjects.
- getAlternativeHypothesis() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.AndersonDarling
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.HypothesisTest
-
Get the description of the alternative hypothesis.
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.mean.OneWayANOVA
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.mean.T
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.KruskalWallis
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.SiegelTukey
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.VanDerWaerden
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.variance.Bartlett
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.variance.BrownForsythe
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.variance.F
-
- getAlternativeHypothesis() - Method in class dev.nm.stat.test.variance.Levene
-
- getAplus() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
-
- getARMA() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
-
Get the ARMA part of this ARIMA model, essentially ignoring the differencing.
- getARMAForecastOneStep() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Gets the auxiliary ARMA one-step ahead forecaster.
- getARMAGARCHModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- getARMAModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the fitted ARMA model.
- getARMAModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
-
Get the ARMA part of this model.
- getARMAX() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the ARMAX part of this ARIMAX model, essentially ignoring the differencing.
- getAuxiliaryOLSRegression(Vector, LMResiduals) - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
Get the auxiliary regression.
- getAuxiliaryOLSRegression(Vector, LMResiduals) - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.White
-
- getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.BreuschPagan
-
- getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Glejser
-
- getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.HarveyGodfrey
-
- getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
Define the transformation of residuals.
- getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.White
-
- getAverageClusterSize() - Method in class dev.nm.stat.evt.cluster.Clusters
-
Get the average cluster size.
- getBarrierLevels() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the barrier levels used for estimation.
- getBase() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best1Bin
-
- getBase() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Pick a base chromosome from the population.
- getBasis(int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.Basis
-
Get the full set of the standard basis vectors.
- getBasis(int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.Basis
-
Get a subset of the standard basis vectors.
- getBasis() - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Get the orthogonal basis.
- getBCol(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Get the table entry at [i, B].
- getBeginIndex() - Method in class dev.nm.stat.evt.cluster.Clusters.Cluster
-
Get the index of the first element of this cluster.
- getBest(int) - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Get the i-th best chromosome.
- getBinKeyValues(RealScalarFunction) - Method in class dev.nm.misc.algorithm.Bins
-
Applies a function to the key of each bin.
- getBinObjectValues(Function<List<T>, Double>) - Method in class dev.nm.misc.algorithm.Bins
-
Applies a function to the items of each bin.
- getBins() - Method in class dev.nm.misc.algorithm.Bins
-
Divides the items into n
bins.
- getBuyThreshold() - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Boundaries
-
Gets the buy threshold.
- getCharacteristicPolynomial() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
Get the characteristic polynomial.
- getChild(int) - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Produce a child chromosome.
- getChild(int) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
-
- getChildren(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils
-
Get the set of vertices that have an incoming arc coming from a vertex.
- getClusterCount() - Method in class dev.nm.stat.evt.cluster.Clusters
-
Get the number of clusters.
- getClusterMaxima() - Method in class dev.nm.stat.evt.cluster.Clusters
-
Get an array of cluster maxima.
- getClusters(double[]) - Method in class dev.nm.stat.evt.cluster.ClusterAnalyzer
-
Count clusters from the given observations.
- getClusters() - Method in class dev.nm.stat.evt.cluster.Clusters
-
Get the list of clusters.
- getClusters(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
-
- getCoefficient(int) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Get an-i, the coefficient of xn-i.
- getCoefficients() - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Get a copy of the polynomial coefficients.
- getCoefficients() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
- getCoefficients() - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
-
Get the coefficients \(c_i\) associated with each evaluation point \(x_i\).
- getCoefficients() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
-
- getCoefficients() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
- getCoefficients() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
- getColLabel(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Gets the label for column i.
- getColLabel(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- getColumn(int) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
-
Get the specified column in the matrix as a vector.
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Get a column.
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- getColumn(int, int, int) - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- getColumn(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
- getComplement() - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Get the basis of the orthogonal complement.
- getComplexRoots(List<? extends Number>) - Static method in class dev.nm.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 dev.nm.stat.random.rng.RNGUtils
-
Gets the i-th component from a list of double[]
.
- getConcurrency() - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Returns the number of threads used for parallel execution.
- getConfidenceInterval() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
-
Get the estimated confidence intervals of the fitted ACER function.
- getConfidenceInterval() - Method in class dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
-
Get the confidence level.
- getConfidenceWidths() - Method in class dev.nm.stat.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 dev.nm.solver.multivariate.constrained.SubProblemMinimizer
-
- getConstraints() - Method in interface dev.nm.solver.multivariate.constrained.constraint.Constraints
-
Get the list of constraint functions.
- getConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
-
Get the constraints.
- getConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
- getConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
- getConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
- getContext(Vector) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
Generates the context information from a generating vector x.
- getCoordinate(Vector[], int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the vector entries from a particular coordinate.
- getCoordinate(Collection<Vector>, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the vector entries from a particular coordinate.
- getCoordinates() - Method in class dev.nm.geometry.Point
-
Get the coordinates of the point.
- getCostRow(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Get the table entry at [COST, j].
- getCount() - Method in class dev.nm.misc.algorithm.iterative.monitor.CountMonitor
-
Get the number of iterations.
- getCovarianceMatrix() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
-
Gets the "shrunk" covariance matrix.
- getCovariances(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, Interval) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.CovarianceEstimator
-
- getCovariances(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, Interval) - Method in class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleCovarianceEstimator
-
- getCutter(ILPProblem) - Method in interface dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer.CutterFactory
-
Construct a new Cutter
for a MILP problem.
- getDate(int, int, int, DateTimeZone) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Construct a DateTime
instance with year, month, day, and time
zone.
- getDateTime(String) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Construct a DateTime
instance from a string which ends in
TimeZone specification.
- getDateTime(String, DateFormat, DateTimeZone) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Construct a DateTime
instance from a string with no TimeZone
specified.
- getDemeanedModel() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Get the demeaned version of the time series model.
- getDemeanedModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Get the demeaned version of the time series model.
- getDifference(int) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.DividedDifferences
-
Get the divided difference of the given order.
- getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl
-
- getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer.ZangwillImpl
-
- getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
- getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl
-
- getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer.NewtonRaphsonImpl
-
- getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
Get the next search direction.
- getDisjointGraphs(UnDiGraph<V, E>, GraphUtils.GraphFactory<G>) - Static method in class dev.nm.graph.GraphUtils
-
Separate an undirected graph into disjointed connected graphs.
- getDisjointGraphs(UnDiGraph<V, E>) - Static method in class dev.nm.graph.GraphUtils
-
- getDistribution(Vector) - Method in class dev.nm.stat.distribution.multivariate.exponentialfamily.MultivariateExponentialFamily
-
Construct a probability distribution in the exponential family.
- getDistribution(Vector) - Method in class dev.nm.stat.distribution.univariate.exponentialfamily.ExponentialFamily
-
Construct a probability distribution in the exponential family.
- getDistribution() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
-
Get the univariate extreme value distribution for generating innovations.
- getDistribution() - Method in class dev.nm.stat.hmm.mixture.MixtureHMM
-
Gets the distribution in the hidden Markov model.
- getDistribution(int) - Method in enum dev.nm.stat.test.timeseries.adf.TrendType
-
Get an ADF distribution per sample size.
- getDiversifiedWeights(Corvalan2005.WeightsConstraint, Vector, Matrix, Vector) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005
-
Finds the optimal weights for a diversified portfolio.
- getDiversifiedWeights(Corvalan2005.WeightsConstraint, Vector, Matrix, Vector, EqualityConstraints, LessThanConstraints) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005
-
Finds the optimal weights for a diversified portfolio.
- getEdge(V, V, X) - Method in interface dev.nm.graph.GraphUtils.EdgeFactory
-
Creates an edge between two nodes.
- getEdgeBetweeness(UnDiGraph<V, ? extends UndirectedEdge<V>>) - Method in interface dev.nm.graph.community.GirvanNewman.EdgeBetweenessCtor
-
Construct an EdgeBetweeness from an undirected graph.
- getEdges(Graph<V, ?>, V, V) - Static method in class dev.nm.graph.GraphUtils
-
Gets the set of edges that connect the two vertices.
- getEigenvalue(int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Get the i-th eigenvalue.
- getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
- getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.DQDS
-
Gets the eigenvalues of the matrix, sorted in descending order.
- getEigenvalues() - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
- getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Gets all the eigenvalues in descending order.
- getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
-
- getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix
-
- getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
Get all the eigenvalues.
- getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Get all the eigenvalues.
- getEigenvalues() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Spectrum
-
Get all the eigenvalues.
- getEigenvalues() - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the set of real eigenvalues.
- getEigenVector(Vector, int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Get an eigenvector from an initial guess.
- getEigenVector() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Get an eigenvector.
- getEigenvector() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getEigenvectorMatrix() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Gets the eigenvector matrix, each column is an eigenvector.
- getEigenvectorMatrix() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
-
- getEigenvectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
-
Gets all the eigenvectors which corresponds to the list of eigenvalues.
- getEigenvectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
-
- getEigenvectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix
-
Gets the eigenvectors.
- getEigenVectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
- getEigenvectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Gets the eigenvectors of A, which are the columns of Q.
- getEndIndex() - Method in class dev.nm.stat.evt.cluster.Clusters.Cluster
-
Get the index of the last element of this cluster.
- getEndPoint1() - Method in class dev.nm.geometry.LineSegment
-
Get the first endpoint.
- getEndPoint2() - Method in class dev.nm.geometry.LineSegment
-
Get the second endpoint.
- getEntryList() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getEntryList() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getEntryList() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getEntryList() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix
-
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
- getEqualityConstraints() - Method in interface dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblem
-
Gets the equality constraints, hi(x) = 0
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
- getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
- getEstimates() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
-
Get the empirical estimates.
- getEstimators(int) - Method in class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.regression.linear.lasso.lars.LARSFitting
-
Calculate the LARS fitting estimators.
- getEvaluationPoints() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
- getEvaluationPoints() - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
-
Get the evaluation points for the quadrature rule (\(x_i\)).
- getEvaluationPoints() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
-
- getEvaluationPoints() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
- getEvaluationPoints() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
- getEventCountPerPeriod() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the mean peak rate, or the average number of peaks per period.
- getExceedenceCount() - Method in class dev.nm.stat.evt.cluster.Clusters
-
Get the number of observations that is greater than a given threshold.
- getExceptions() - Method in exception dev.nm.misc.parallel.MultipleExecutionException
-
Get all exceptions encountered during execution.
- getExpectedContingencyTable(int[], int[]) - Static method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
-
Assume the null hypothesis of independence, we compute the expected frequency of each
category.
- getExtHeaders() - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the extended headers, including the subject and time headers.
- getExtHeadersString() - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the extended headers, including the subject and time headers.
- getFamily() - Method in class dev.nm.stat.regression.linear.glm.GLMProblem
-
Get the exponential family distribution of the mean.
- getFeasibleInitialPoint(LinearEqualityConstraints) - Method in class dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Initialize the first population.
- getFirstGeneration() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.FirstGeneration
-
Generate the initial pool of chromosomes.
- getFirstGeneration() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.PerturbationAroundPoint
-
- getFirstGeneration() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.UniformMeshOverRegion
-
- getFirstGeneration() - Method in class dev.nm.solver.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 dev.nm.misc.datastructure.time.JodaTimeUtils
-
Get the first instant of time after a given start time.
- getFirstNonIntegralIndices(double[]) - Method in class dev.nm.solver.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getFitResult() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
-
Get the fitted parameter.
- getFittedOU(double[], double) - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFitting
-
Get the fitted OU process.
- getFittedOU(double[]) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
-
Fit an OU process by using MLE.
- getFittedOU(double[], double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
-
- getFittedOU(double[]) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
-
Fit an OU process by using least squares regression.
- getFittedOU(double[], double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
-
- getFittedParameters() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Get the fitted parameters.
- getFittedState(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the posterior expected state.
- getFittedState(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the posterior expected state.
- getFittedStates() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the posterior expected states.
- getFittedStates() - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the posterior expected states.
- getFittedStateVariance(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the posterior expected state variance.
- getFittedStateVariance(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the posterior expected state variance.
- getFlags() - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
-
Gets the factor flags.
- getFractional(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Get the fractional part of a number.
- getFt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get an empty filtration of the process.
- getFt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
-
Get the filtration.
- getFt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE
-
Get an empty filtration of the process.
- getGARCHModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
-
Get the GARCH part of this model.
- getGraph() - Method in interface dev.nm.graph.GraphUtils.GraphFactory
-
Creates an empty graph.
- getGreaterThanConstraint(Vector, int) - Static method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
Construct a greater-than constraint for the branching greater-than subproblem.
- getGreaterThanConstraints() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Get the set of linear greater-than-or-equal-to constraints.
- getHeaders() - Method in class dev.nm.misc.datastructure.MathTable
-
Gets the column names.
- getHMM() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
- getHomogeneousSoln() - Method in interface dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.Solution
-
Get the basis of the homogeneous solution for the linear system,
Ax = b.
- getHouseholderMatrices() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
Gets the householder reflections used in the reflection.
- getInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Get the i-th inactive index.
- getInactiveIndices() - Method in class dev.nm.misc.algorithm.ActiveSet
-
Get all inactive indices.
- getIncrement(Vector, Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl
-
- getIncrement(Vector, Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer.ZangwillImpl
-
- getIncrement(Vector, Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
- getIncrement(Vector, Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
Get the increment fraction, αk.
- getIndex(String) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SymbolLookup
-
Gets the index (starts from 1) of the product with a given symbol.
- getIndexFromColLabel(Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Translates a column label to a column index.
- getIndexFromRowLabel(Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Translates a row label to a row index.
- getIndices() - Method in class dev.nm.misc.datastructure.MathTable
-
Gets a copy of the row indices.
- getInitialGuess() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the initial guess of the solution for the problem.
- getInitialHessian(Vector, Vector) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
-
Get the initial Hessian matrix.
- getInitialHessian(Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- getInitialHessian(Vector, Vector, Vector) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation
-
Get the initial Hessian matrix.
- getInitialHessian(Vector, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
-
- getInitials(Vector...) - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.initialization.InitialsFactory
-
Generate a set of initial points for optimization from the fewer than required points.
- getInitials(Vector...) - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1
-
- getInitials() - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1
-
Generate a set of initial points for optimization.
- getInitials(Vector...) - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
-
- getInitials() - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
-
Generate a set of initial points for optimization.
- getIntegerIndices() - Method in interface dev.nm.solver.multivariate.constrained.integer.IPProblem
-
Get the indices of the integral variables.
- getIntegerIndices() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
- getIntegerIndices() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- getIntegralConstraint(int) - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
-
Get the integral domain of a particular integral variable.
- getIntervalLength() - Method in class dev.nm.stat.evt.cluster.ClusterAnalyzer
-
Get the clustering interval length.
- getIntervals(DateTime, DateTime, Period) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Creates a list of intervals between start
and end
every
so often.
- getInverseNorm() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getIterates() - Method in class dev.nm.misc.algorithm.iterative.monitor.IteratesMonitor
-
Get a list of all iterative states.
- getKalmanGain(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the Kalman gain.
- getKalmanGain(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the Kalman gain.
- getLambda(Vector) - Method in class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
-
Computes the value of the objective function in eq.
- getLastDayOfMonth(DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Get the last day of the month which contain a time.
- getLastMillisecondOfDay(DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Get the last millisecond the day which contain a time.
- getLastNonZeroIndex() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getLeftHouseholders() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets all the accumulated left Householders.
- getLeftPreconditioner() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the left preconditioner.
- getLessThanConstraint(Vector, int) - Static method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
Construct a less-than constraint for the branching less-than subproblem.
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
- getLessThanConstraints() - Method in interface dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblem
-
Gets the less-than-or-equal-to constraints, gi(x) ≤
0
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
-
- getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
- getLHS(double) - Method in class dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
-
- getLHS(int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
- getLicenseKey() - Static method in class dev.nm.misc.license.License
-
Gets the license key string of the current license.
- getLicenseLocation() - Static method in class dev.nm.misc.license.License
-
Gets the location of the current loaded license.
- getLinearModel(Object) - Method in class dev.nm.stat.regression.linear.panel.FixedEffectsModel
-
- getLinearModel(Object) - Method in interface dev.nm.stat.regression.linear.panel.PanelRegression
-
Gets the linear model for a particular subject/individual.
- getLinearSpan(double...) - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
- getLmask() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getLocation() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
Get the location parameter.
- getLogLikelihoodFunction() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Get the log-likelihood function.
- getLower() - Method in class dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
-
Get the lower bounds of the confidence intervals.
- getLowerLevel(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- getLowerParameter() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- getLowerRight() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Deflation
-
Gets the lower right corner of the deflation.
- getMAModel() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
Get the MA model of the inverse representation.
- getMarginal1() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- getMarginal2() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- getMaskB() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getMaskC() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getMaxIteration() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the specified maximum number of iterations.
- getMaxIterations() - Method in exception dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.MaxIterationsExceededException
-
Get the maximum number of iterations.
- getMaxIterations() - Method in interface dev.nm.analysis.integration.univariate.riemann.IterativeIntegrator
-
Get the maximum number of iterations for this iterative procedure.
- getMaxIterations() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
- getMaxIterations() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
- getMean() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACERStatistics
-
Get the mean of the empirical estimates for each barrier level.
- getMean() - Method in class dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
-
Get the mean values.
- getMeanForecast(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
Calculates the k-step ahead forecast of X in ARMA model.
- getMeanResidual() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- getMeanReturns(double[][]) - Static method in class tech.nmfin.returns.Returns
-
Computes a vector of mean returns of the input returns (one column for
one asset).
- getMeanReturns(Matrix) - Static method in class tech.nmfin.returns.Returns
-
Computes a vector of mean returns of the input returns (one column for
one asset).
- getMeans() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the (weighted) mean of the estimates for the generated barrier levels.
- getMeans(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, Interval) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.MeanEstimator
-
- getMeans(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, Interval) - Method in class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleMeanEstimator
-
- getMessage() - Method in error dev.nm.misc.license.LicenseError
-
- getMessage() - Method in exception dev.nm.misc.parallel.MultipleExecutionException
-
Gather the stack traces for the thrown exceptions.
- getMinEigenValue(Matrix, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
-
Gets the minimum of all the eigenvalues of a matrix.
- getMinEigenValue(Matrix, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
-
Gets the minimum of all the eigenvalues of a matrix.
- getMinEigenValue(Matrix, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
-
Gets the minimum of all the eigen values of a matrix.
- getMm() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getModel() - Method in class dev.nm.stat.evt.timeseries.MARMASim
-
Get the MARMA model.
- getModel() - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
-
Gets the constructed model.
- getModel() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Get the fitted ARMA model.
- getModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the fitted ARIMA model.
- getModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit
-
Get the fitted GARCH model.
- getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in interface dev.nm.stat.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 dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
-
- getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
-
- getNegCount() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getNeighbors(Graph<V, ?>, V) - Static method in class dev.nm.graph.GraphUtils
-
Gets the set of vertices which are connected to v
via any edges in this graph.
- getNewFt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
- getNewFt() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE
-
Get an empty filtration of the process.
- getNewFt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
-
- getNewFt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
-
- getNewFt() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.discrete.DiscreteSDE
-
Get an empty filtration of the process.
- getNewFt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.EulerSDE
-
- getNewFt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE
-
- getNewPool(int) - Static method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Allocate space for a population pool.
- getNextGeneration(List<Chromosome>, List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Populate the next generation using the parent and children chromosome pools.
- getNextGeneration(List<Chromosome>, List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
-
- getNn() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getNonIntegralIndices(double[]) - Method in interface dev.nm.solver.multivariate.constrained.integer.IPProblem
-
Check which elements in x do not satisfy the integral constraints.
- getNonIntegralIndices(double[]) - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
- getNonIntegralIndices(double[]) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- getNormalization() - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Get the normalized version of this polynomial so the leading coefficient is 1.
- getNullHypothesis() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
-
- getNullHypothesis() - Method in class dev.nm.stat.test.distribution.AndersonDarling
-
- getNullHypothesis() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples
-
- getNullHypothesis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov
-
- getNullHypothesis() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
-
- getNullHypothesis() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera
-
- getNullHypothesis() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors
-
- getNullHypothesis() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk
-
- getNullHypothesis() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
-
- getNullHypothesis() - Method in class dev.nm.stat.test.HypothesisTest
-
Get a description of the null hypothesis.
- getNullHypothesis() - Method in class dev.nm.stat.test.mean.OneWayANOVA
-
- getNullHypothesis() - Method in class dev.nm.stat.test.mean.T
-
- getNullHypothesis() - Method in class dev.nm.stat.test.rank.KruskalWallis
-
- getNullHypothesis() - Method in class dev.nm.stat.test.rank.SiegelTukey
-
- getNullHypothesis() - Method in class dev.nm.stat.test.rank.VanDerWaerden
-
- getNullHypothesis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
- getNullHypothesis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
- getNullHypothesis() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- getNullHypothesis() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
-
- getNullHypothesis() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
- getNullHypothesis() - Method in class dev.nm.stat.test.variance.Bartlett
-
- getNullHypothesis() - Method in class dev.nm.stat.test.variance.BrownForsythe
-
- getNullHypothesis() - Method in class dev.nm.stat.test.variance.F
-
- getNullHypothesis() - Method in class dev.nm.stat.test.variance.Levene
-
- getNumberOfPeriodsBetween(DateTime, DateTime, Period) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Return the number of periods between two times, rounding up.
- getObsDimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
-
Get the dimension of the observations.
- getObsDimension() - Method in class dev.nm.stat.dlm.univariate.DLM
-
Get the dimension of observations.
- getObservationModel() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
-
Get the observation model.
- getObservationModel() - Method in class dev.nm.stat.dlm.univariate.DLM
-
Get the observation model.
- getObservations() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
-
Get the observations.
- getObservations() - Method in class dev.nm.stat.dlm.univariate.DLMSeries
-
Get the observations.
- getObservations(HmmInnovation[], int) - Static method in class dev.nm.stat.markovchain.MCUtils
-
Get all observations that occur in a particular state.
- getObservations(int) - Method in class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable
-
Get the observations to compute an empirical distribution.
- getOffsetVectors(Vector, Vector, int, int) - Static method in class dev.nm.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 dev.nm.solver.multivariate.minmax.MinMaxProblem
-
Get the list of omegas, the domain.
- getOne() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Pick a chromosome for mutation/crossover.
- getOne() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Pick a random chromosome from the population.
- getOperation(double[], double[]) - Method in interface dev.nm.number.doublearray.CompositeDoubleArrayOperation.ImplementationChooser
-
Get an implementation based on the inputs.
- getOptimalBlockLength(double[], PattonPolitisWhite2009ForObject.Type) - Static method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Computes the optimal of block length.
- getOptimalBlockLength(Object[], PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject, PattonPolitisWhite2009ForObject.Type) - Static method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Computes the optimal of block length.
- getOptimalLag(double[]) - Static method in class dev.nm.stat.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 dev.nm.stat.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 tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
-
- getOptimalRiskAversionCoefficient(double, double, double) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Gets the optimal risk aversion coefficient w.r.t.
- getOptimalRiskAversionCoefficient() - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Gets the optimal risk aversion coefficient w.r.t.
- getOptimalValue() - Method in class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
-
- getOptimalW(double) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM
-
- getOptimalW(double) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Solves w_eff = argmin {q * (w' Σ w) - w'r}.
- getOptimalWeightForSetLambda(double) - Method in interface tech.nmfin.portfoliooptimization.clm.MarkowitzCriticalLine
-
- getOptimalWeightForSetLambda(double) - Method in class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
-
- getOptimalWeightForTargetReturn(double) - Method in interface tech.nmfin.portfoliooptimization.clm.MarkowitzCriticalLine
-
- getOptimalWeightForTargetReturn(double) - Method in class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
-
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, Interval) - Method in class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm
-
- getOptimalWeights() - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Gets the Markowitz optimal portfolio weights, for a given risk aversion
coefficient.
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, Interval) - Method in class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
-
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, Interval) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm
-
Computes the optimal weights for the products using returns.
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, Interval) - Method in class tech.nmfin.portfoliooptimization.TopNOptimizationAlgorithm
-
- getOrderedNodes() - Method in class dev.nm.graph.algorithm.traversal.BFS
-
- getOrderedNodes(Collection<V>) - Method in class dev.nm.graph.algorithm.traversal.BottomUp
-
Gets the list of visited nodes, in the order of being visited.
- getOrderedNodes() - Method in class dev.nm.graph.algorithm.traversal.BottomUp
-
- getOrderedNodes() - Method in class dev.nm.graph.algorithm.traversal.DFS
-
- getOrderedNodes() - Method in interface dev.nm.graph.algorithm.traversal.GraphTraversal
-
Gets the list of visited nodes, in the order of being visited.
- getOrderedNodes() - Method in class dev.nm.graph.algorithm.traversal.TraversalFromRoots
-
Gets the collection of visited nodes to build a spanning tree.
- getOrthogonalVector() - Method in class dev.nm.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 dev.nm.stat.regression.linear.panel.PanelData
-
Gets the (transformed) values from a panel data.
- getPanelValuesByHeaders(List<PanelData<S>.Row>, String[]) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the values from a panel data.
- getPanelValuesByHeaders(List<PanelData<S>.Row>) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the values from a panel data.
- getPanelValuesByTime(List<PanelData<S>.Row>) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the (transformed) values from a panel data.
- getPanelValuesByTime(List<PanelData<S>.Row>, String[], PanelData.Transformation[]) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the (transformed) values from a panel data.
- getParameter() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit.Result
-
- getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
-
- getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
-
- getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
-
- getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- getParams() - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution
-
Get the parameters, for each state, of the distribution.
- getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
-
- getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
-
- getParents(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils
-
Get the set of vertices that have an outgoing arc pointing to a vertex.
- getParticularSolution(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.Solution
-
Get a particular solution for the linear system.
- getPeakMean() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the average value of peaks.
- getPeaks() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
-
Get the peaks found in the observations.
- getPenaltyFunction(ConstrainedOptimProblem) - Method in interface dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer.PenaltyFunctionFactory
-
Get an instance of the penalty function.
- getPeriodicInstants(DateTime, Period, int) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Make a list of periodic time instants.
- getPivot(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
- getPivot(SimplexTable) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
-
Get the next pivot.
- getPolynomial(int) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermitePolynomials
-
- getPolynomial(int) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerrePolynomials
-
- getPolynomial(int) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendrePolynomials
-
- getPolynomial(int) - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.OrthogonalPolynomialFamily
-
Return an instance of the polynomial class of a given order.
- getPopulation() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Get the current generation.
- getPortfolioConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
-
Gets the portfolio constraints represented in the objective function.
- getPortfolioConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Gets the portfolio constraints.
- getPortfolioReturns(Vector, Vector) - Static method in class tech.nmfin.portfoliooptimization.PortfolioUtils
-
Computes the expected portfolio return.
- getPortfolioVariance(Vector, Matrix) - Static method in class tech.nmfin.portfoliooptimization.PortfolioUtils
-
Computes the portfolio variance.
- getPossiblePairs(List<String>, List<String>) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
-
- getPossiblePairs(int) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
-
- getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.ChangeOfVariable
-
- getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature
-
- getPrecision() - Method in interface dev.nm.analysis.integration.univariate.riemann.Integrator
-
Get the convergence threshold.
- getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
- getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Romberg
-
- getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
- getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.Riemann
-
- getPredictedObservation(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior observation prediction.
- getPredictedObservation(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the prior observation prediction.
- getPredictedObservations() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior observation predictions.
- getPredictedObservations() - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the prior observation predictions.
- getPredictedObservationVariance(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior observation prediction variance.
- getPredictedObservationVariance(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the prior observation prediction variance.
- getPredictedState(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior expected state.
- getPredictedState(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the prior expected state.
- getPredictedStates() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior expected states.
- getPredictedStates() - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the prior expected states.
- getPredictedStateVariance(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get the prior expected state variance.
- getPredictedStateVariance(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get the prior expected state variance.
- getPriceMatrix(Vector, Vector) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
-
- getPricesFromReturns(double, double[], ReturnsCalculator) - Static method in class tech.nmfin.returns.Returns
-
Gets the price series from a return series.
- getProblem() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the linear regression problem.
- getProblemSize() - Method in class dev.nm.solver.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 dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
-
Get the underlying OU process of this generator.
- getProjectionLength(int) - Method in class dev.nm.algebra.linear.vector.doubles.operation.Projection
-
Get the length of v projected on each dimension {wi}.
- getProjectionVector(int) - Method in class dev.nm.algebra.linear.vector.doubles.operation.Projection
-
Get the i-th projected vector of v on {wi}.
- getProperty(Number) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
- getProperty(int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
- getRandom() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Get the estimated seasonal effect of the time series.
- getRealEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Get all real eigenvalues.
- getRealRoots(List<? extends Number>) - Static method in class dev.nm.analysis.function.polynomial.root.PolyRoot
-
Get a copy of only the real roots of a polynomial.
- getReason() - Method in exception dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
-
Get the reason for the convergence failure.
- getReciprocal() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
Gets the reciprocal state switching GBM.
- getReducedLinearEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearEqualityConstraints
-
- getRegime(Matrix) - Method in class tech.nmfin.infantino2010.Infantino2010Regime
-
Gets the current regime.
- getResamplerModel(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
-
- getResamplerModel(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
-
- getResamplerModel(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
-
- getResidual() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getResultantTableau() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- getResultantTableau() - Method in interface dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- getResults(List<D>) - Method in class dev.nm.misc.algorithm.BruteForce
-
Runs in parallel the brute force algorithm on the function for a given
domain.
- getResults() - Method in exception dev.nm.misc.parallel.MultipleExecutionException
-
Get the results obtained so far.
- getResultsSerially(List<D>) - Method in class dev.nm.misc.algorithm.BruteForce
-
Deprecated.
- getReturnLevel() - Method in class dev.nm.stat.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 tech.nmfin.returns.Returns
-
- getReturnsFromPrices(double[], ReturnsCalculator) - Static method in class tech.nmfin.returns.Returns
-
Computes returns series from prices.
- getRHS(Vector, double) - Method in class dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
-
- getRHS(int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
- getRightHouseholders() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets all the accumulated right Householders.
- getRightPreconditioner() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the right preconditioner.
- getRiskAversionCoefficientForTargetReturn(double, double, double, int) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
- getRiskAversionCoefficientForTargetVariance(double, double, double, int) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
- getRNG(T) - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG
-
- getRoot(Matrix) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.DefaultRoot
-
- getRoot(Matrix) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.Diagonalization
-
- getRoot(Matrix) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.MatrixRoot
-
Gets the root of a matrix
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- getRow(int) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
-
Get the specified row in the matrix as a vector.
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Get a row.
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- getRow(int, int, int) - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- getRow(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
- getRow(S, String) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets one particular row indexed by a pair of subject and time.
- getRowLabel(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Gets the label for row i.
- getRowLabel(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- getRowOnOrAfter(double) - Method in class dev.nm.misc.datastructure.MathTable
-
Gets the row corresponding to a row index.
- getRowOnOrBefore(double) - Method in class dev.nm.misc.datastructure.MathTable
-
Gets the row corresponding to a row index.
- getRows() - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets all of the panel data.
- getRowsForSubject(S) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Get all the rows pertaining to a particular subject.
- getRowsOnOrAfter(double) - Method in class dev.nm.misc.datastructure.MathTable
-
Gets the rows having the row index value equal to or just bigger than i
.
- getRowsOnOrBefore(double) - Method in class dev.nm.misc.datastructure.MathTable
-
Gets the rows having the row index value equal to or just smaller than i
.
- getRQCorrection() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getRr() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getRSS() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit.Result
-
- getScale() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
Get the scale parameter.
- getSeasonal() - Method in class dev.nm.stat.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 tech.nmfin.trend.dai2011.Dai2011Solver.Boundaries
-
Gets the sell threshold.
- getShape() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
Get the shape parameter.
- getSharedInstance() - Static method in class dev.nm.misc.parallel.ParallelExecutor
-
Gets the globally shared executor.
- getSharpeRatio(Vector, Vector, Matrix, double) - Static method in class tech.nmfin.portfoliooptimization.PortfolioUtils
-
Computes the portfolio Sharpe ratio.
- getShift0() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getShift1() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getShiftB() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getShiftC() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getShrunkCovarianceMatrix() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
-
Gets the nonlinear shrinkage covariance matrix.
- getSignal(Matrix) - Method in class tech.nmfin.infantino2010.Infantino2010PCA
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
Override this method to put in whatever constraints in the minimization problem.
- getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory
-
- getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
-
Construct an instance of a SimpleCell
.
- getSingularValues() - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
-
- getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
- getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
-
- getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
-
- getSingularValues() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the normalized, hence positive, singular values.
- getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Returns the singular values (same as the eigenvalues) of A.
- getSolutionToOriginalProblem(Vector) - Method in class dev.nm.solver.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 dev.nm.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 dev.nm.stat.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 dev.nm.stat.markovchain.MCUtils
-
Count the numbers of occurrences of states.
- getStateDimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
-
Get the dimension of states.
- getStateDimension() - Method in class dev.nm.stat.dlm.univariate.DLM
-
Get the dimension of states.
- getStateModel() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
-
Get the state model.
- getStateModel() - Method in class dev.nm.stat.dlm.univariate.DLM
-
Get the state model.
- getStates() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
-
Get the states.
- getStates() - Method in class dev.nm.stat.dlm.univariate.DLMSeries
-
Get the states.
- getStationaryProbabilities(Matrix) - Static method in class dev.nm.stat.markovchain.SimpleMC
-
Gets the stationary state probabilities of a Markov chain that is irreducible, aperiodic and
strongly connected (positive recurrent).
- getStatistic() - Method in interface dev.nm.stat.descriptive.StatisticFactory
-
- getStats(CointegrationMLE) - Method in class dev.nm.stat.cointegration.JohansenTest
-
Get the set of likelihood ratio test statistics for testing H(r)
in H(r+1).
- getStepLength(double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
-
- getStepLength(double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.FastAnnealingFunction
-
- getStepLength(double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction
-
- getSubProblem(ConstrainedOptimSubProblem) - Static method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
-
- getSymbol(int) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SymbolLookup
-
Gets the symbol of the product at a given index (starts from 1).
- getTable() - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
Get a copy of the Neville table.
- getTailedMatrix(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
-
- getTailedMatrix(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
-
- getThreshold() - Method in class dev.nm.stat.evt.cluster.ClusterAnalyzer
-
Get the threshold for exceedance.
- getTime() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries.Entry
-
- getTime() - Method in class dev.nm.stat.dlm.univariate.DLMSeries.Entry
-
- getTime() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
-
- getTime() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
-
- getTime() - Method in interface dev.nm.stat.timeseries.datastructure.TimeSeries.Entry
-
Get the timestamp.
- getTime() - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
-
- getTolerance() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
- getTopN(Vector, int, double) - Static method in class tech.nmfin.portfoliooptimization.TopNOptimizationAlgorithm
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in interface tech.nmfin.meanreversion.cointegration.PairingModel
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel1
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel2
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel3
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel4
-
- getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel5
-
- getTransformedMatrix() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets the final matrix transformed by all the Householder transformations.
- getTransitionCounts(int[]) - Static method in class dev.nm.stat.markovchain.MCUtils
-
Count the numbers of times the state goes from one state to another.
- getTrend() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition
-
Get the estimated trend of the time series.
- getTurningPoints() - Method in interface tech.nmfin.portfoliooptimization.clm.MarkowitzCriticalLine
-
- getTurningPoints() - Method in class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
-
- getTwistIndex() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
- getType() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
Gets the bi-diagonal matrix type.
- getUmask() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getUpper() - Method in class dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
-
Get the upper bounds of the confidence intervals.
- getUpperLeft() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Deflation
-
Gets the upper left corner of the deflation.
- getUpperLevel(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- getUpperParameter() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- getValue(String) - Static method in class dev.nm.misc.license.License
-
- getValue() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries.Entry
-
- getValue() - Method in class dev.nm.stat.dlm.univariate.DLMSeries.Entry
-
- getValue() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
-
- getValue() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
-
- getValue() - Method in interface dev.nm.stat.timeseries.datastructure.TimeSeries.Entry
-
Get the entry value.
- getValue() - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
-
- getValueArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- getValueArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- getValueArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- getValueArray() - Method in interface dev.nm.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 dev.nm.stat.regression.linear.panel.PanelData
-
Gets the headers, excluding the subject and time headers.
- getValueHeadersString() - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the headers, excluding the subject and time headers.
- getVarForecast(int) - Method in class dev.nm.stat.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 dev.nm.analysis.function.SubFunction
-
Given an input to the original function, this extracts the variable parts
(excluding the fixed values).
- getVariables(SOCPGeneralConstraints) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
- getVariables() - Method in class dev.nm.solver.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 dev.nm.solver.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 dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Get the ARMA part of this ARIMA model, essentially ignoring the differencing.
- getVARMAX() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the ARMAX part of this ARIMAX model, essentially ignoring the differencing.
- getVarResidual() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
- Getvec - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
-
Computes an FP vector of a singleton.
- getVectorReturnsFromPrices(double[][], ReturnsCalculator) - Static method in class tech.nmfin.returns.Returns
-
Computes returns for a 2D array of prices (one column for one asset),
with the given
ReturnsCalculator.
- getVersion() - Static method in class dev.nm.misc.license.License
-
Gets the version number.
- getViterbiStates(double[]) - Method in class dev.nm.stat.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
-
- getWeightForObservation(double, double) - Method in interface dev.nm.analysis.curvefit.LeastSquares.Weighting
-
Specify the weight given to a particular observation.
- getWeighting(double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
- getWeighting(double) - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
-
Get the weighting \(w(x_i)\) associated with a point \(x_i\).
- getWeighting(double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
-
- getWeighting(double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
- getWeighting(double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
- getWeights() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit.Result
-
- getWhole(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Get the integral part of a number (discarding the fractional part).
- getWmask() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getWw() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- getX2() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
Get the Chi-squared distribution.
- getX2() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.White
-
- GEVFittingByMaximumLikelihood - Class in dev.nm.stat.evt.evd.univariate.fitting
-
Estimate the
GeneralizedEVD
parameter from the observations by
maximum likelihood approach.
- GEVFittingByMaximumLikelihood() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.GEVFittingByMaximumLikelihood
-
- GirvanNewman<V,E extends UndirectedEdge<V>,G extends UnDiGraph<V,E>> - Class in dev.nm.graph.community
-
The Girvan–Newman algorithm detects communities in complex systems.
- GirvanNewman(UnDiGraph<V, E>, GirvanNewman.EdgeBetweenessCtor<V>, GraphUtils.GraphFactory<G>) - Constructor for class dev.nm.graph.community.GirvanNewman
-
Construct an instance of the Girvan-Newman algorithm.
- GirvanNewman.EdgeBetweenessCtor<V> - Interface in dev.nm.graph.community
-
This allows customization of the computation of edge-betweeness.
- GirvanNewmanUnDiGraph<V,E extends UndirectedEdge<V>> - Class in dev.nm.graph.community
-
- GirvanNewmanUnDiGraph(UnDiGraph<V, E>) - Constructor for class dev.nm.graph.community.GirvanNewmanUnDiGraph
-
Construct an instance of the Girvan-Newman algorithm.
- GivensMatrix - Class in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Copy constructor.
- gk - Variable in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
the gradient at the k-th iteration
- Glejser - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
-
The Glejser test tests for conditional heteroskedasticity.
- Glejser(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.Glejser
-
Perform the Glejser test to test for heteroskedasticity in a linear regression model.
- GLMBeta - Class in dev.nm.stat.regression.linear.glm
-
This is the estimate of beta, β^, in a Generalized Linear Model.
- GLMBeta(GLMFitting, GLMResiduals) - Constructor for class dev.nm.stat.regression.linear.glm.GLMBeta
-
Construct an instance of Beta
.
- GLMBinomial - Class in dev.nm.stat.regression.linear.glm.distribution
-
This is the Binomial distribution of the error distribution in GLM model.
- GLMBinomial() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
-
- GLMExponentialDistribution - Interface in dev.nm.stat.regression.linear.glm.distribution
-
This interface represents a probability distribution from the exponential family.
- GLMFamily - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
Construct an instance of Family
.
- GLMFamily(GLMBinomial) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
Construct a Binomial family.
- GLMFamily(GLMGamma) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
Construct a Gamma family.
- GLMFamily(GLMGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
Construct a Gaussian family.
- GLMFamily(GLMInverseGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
Construct an Inverse Gaussian family.
- GLMFamily(GLMPoisson) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
Construct a Poisson family.
- GLMFitting - Interface in dev.nm.stat.regression.linear.glm
-
This interface represents a fitting method for estimating β in a
Generalized Linear Model (GLM).
- GLMGamma - Class in dev.nm.stat.regression.linear.glm.distribution
-
This is the Gamma distribution of the error distribution in GLM model.
- GLMGamma() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
-
- GLMGaussian - Class in dev.nm.stat.regression.linear.glm.distribution
-
This is the Gaussian distribution of the error distribution in GLM model.
- GLMGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
-
- GLMInverseGaussian - Class in dev.nm.stat.regression.linear.glm.distribution
-
This is the Inverse Gaussian distribution of the error distribution in GLM model.
- GLMInverseGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
-
- GLMModelSelection - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
-
Constructs automatically a GLM model to explain the observations.
- GLMModelSelection.ModelNotFound - Exception in dev.nm.stat.regression.linear.glm.modelselection
-
Throw a ModelNotFound
exception when fail to construct a model to
explain the data.
- GLMPoisson - Class in dev.nm.stat.regression.linear.glm.distribution
-
This is the Poisson distribution of the error distribution in GLM model.
- GLMPoisson() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
-
- GLMProblem - Class in dev.nm.stat.regression.linear.glm
-
This is a Generalized Linear regression problem.
- GLMProblem(Vector, Matrix, boolean, GLMFamily) - Constructor for class dev.nm.stat.regression.linear.glm.GLMProblem
-
Construct a GLM problem.
- GLMProblem(LMProblem, GLMFamily) - Constructor for class dev.nm.stat.regression.linear.glm.GLMProblem
-
Construct a GLM problem from a linear regression problem.
- GLMResiduals - Class in dev.nm.stat.regression.linear.glm
-
Residual analysis of the results of a Generalized Linear Model regression.
- GLMResiduals(GLMProblem, Vector) - Constructor for class dev.nm.stat.regression.linear.glm.GLMResiduals
-
Performs residual analysis for a GLM regression.
- GlobalSearchByLocalMinimizer - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
-
This minimizer is a global optimization method.
- GlobalSearchByLocalMinimizer(LocalSearchCellFactory.MinimizerFactory, RandomLongGenerator, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a GlobalSearchByLocalMinimizer
to solve unconstrained minimization
problems.
- GlobalSearchByLocalMinimizer(RandomLongGenerator, double, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a GlobalSearchByLocalMinimizer
to solve unconstrained minimization
problems.
- GlobalSearchByLocalMinimizer() - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a GlobalSearchByLocalMinimizer
to solve unconstrained minimization
problems.
- GMT - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
GMT
- GOLDEN_RATIO - Static variable in class dev.nm.misc.Constants
-
the Golden ratio
- GoldenMinimizer - Class in dev.nm.solver.univariate.bracketsearch
-
This is the golden section univariate minimization algorithm.
- GoldenMinimizer(double, int) - Constructor for class dev.nm.solver.univariate.bracketsearch.GoldenMinimizer
-
Construct a univariate minimizer using the Golden method.
- GoldenMinimizer.Solution - Class in dev.nm.solver.univariate.bracketsearch
-
This is the solution to a Golden section univariate optimization.
- GoldfeldQuandtTrotter - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.GoldfeldQuandtTrotter
-
Constructs a symmetric, positive definite matrix using the Goldfeld-Quandt-Trotter algorithm.
- GolubKahanSVD - Class in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
-
Runs the Golub-Kahan SVD decomposition on a tall matrix.
- GomoryMixedCutMinimizer - Class in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
-
This cutting-plane implementation uses Gomory's mixed cut method.
- GomoryMixedCutMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer
-
Construct a Gomory mixed cutting-plane minimizer to solve an MILP problem.
- GomoryMixedCutMinimizer.MyCutter - Class in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
-
This is Gomory's mixed cut.
- GomoryPureCutMinimizer - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
-
This is Gomory's pure cut.
- Gradient - Class in dev.nm.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 dev.nm.analysis.differentiation.multivariate.Gradient
-
Construct the gradient vector for a multivariate function f at point x.
- gradient(T) - Method in interface dev.nm.solver.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 dev.nm.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 dev.nm.analysis.differentiation.multivariate.GradientFunction
-
Construct the gradient function of a real scalar function f.
- GramSchmidt - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
Run the Gram-Schmidt process to orthogonalize a matrix.
- GramSchmidt(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
Run the Gram-Schmidt process to orthogonalize a matrix.
- Graph<V,E extends HyperEdge<V>> - Interface in dev.nm.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 dev.nm.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 dev.nm.graph.algorithm.traversal
-
This is a node in a spanning tree.
- GraphUtils - Class in dev.nm.graph
-
These are the utility functions to manipulate Graph
.
- GraphUtils.EdgeFactory<V,N,E extends Edge<N>,X> - Interface in dev.nm.graph
-
This interface specifies how an edge is created for two nodes.
- GraphUtils.GraphFactory<G> - Interface in dev.nm.graph
-
The factory to construct instances of the graph type.
- GRAVITATIONAL_G - Static variable in class dev.nm.misc.PhysicalConstants
-
The Newtonian constant of gravitation \(G\) in meters cubed per kilogram per second squared
(m3 kg-1 s-2).
- GreaterThanConstraints - Interface in dev.nm.solver.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 tech.nmfin.trend.dai2011.Dai2011Solver.Builder
-
- GridSearchCetaMaximizer - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
-
Searches (by brute force) for the maximal point of C(η) among a
grid of values.
- GridSearchCetaMaximizer() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
- GridSearchCetaMaximizer(double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
Constructs a maximizer with a given grid size for even grid search.
- GridSearchCetaMaximizer(GridSearchMinimizer.GridDefinition) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
Constructs a maximizer with a user-defined grid.
- GridSearchMinimizer - Class in dev.nm.solver.univariate
-
This performs a grid search to find the minimum of a univariate function.
- GridSearchMinimizer(int) - Constructor for class dev.nm.solver.univariate.GridSearchMinimizer
-
Constructs an instance with a given number of grid points for even grid.
- GridSearchMinimizer(double) - Constructor for class dev.nm.solver.univariate.GridSearchMinimizer
-
Constructs an instance with a given grid size for even grid.
- GridSearchMinimizer(GridSearchMinimizer.GridDefinition) - Constructor for class dev.nm.solver.univariate.GridSearchMinimizer
-
Constructs an instance with a user-defined grid.
- GridSearchMinimizer.GridDefinition - Interface in dev.nm.solver.univariate
-
- GridSearchMinimizer.Solution - Class in dev.nm.solver.univariate
-
This is the solution to the GridSearchMinimizer
.
- groupCount() - Method in class tech.nmfin.meanreversion.daspremont2008.IndependentCoVAR
-
Returns the total number of independent groups.
- GroupResampler - Class in dev.nm.stat.random.sampler.resampler.multivariate
-
- GroupResampler(Matrix, Resampler) - Constructor for class dev.nm.stat.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 dev.nm.stat.random.sampler.resampler.multivariate.GroupResampler
-
Constructs a re-sampler that treats each row as a group object, shuffling
the groups/rows.
- GroupResamplerFactory - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
-
Creates re-samplers that do re-sampling for the whole group of stocks
together.
- GroupResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
-
- GroupResamplerFactory(long) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
-
- groups() - Method in class tech.nmfin.meanreversion.daspremont2008.IndependentCoVAR
-
Returns the grouped variable indices.
- Gs() - Method in class dev.nm.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 dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
-
The GSA acceptance probability function.
- GSAAcceptanceProbabilityFunction(double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.GSAAcceptanceProbabilityFunction
-
Constructs a GSA acceptance probability function.
- GSAAnnealingFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
-
The GSA proposal/annealing function.
- GSAAnnealingFunction(double, RandomLongGenerator, RandomStandardNormalGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction
-
Constructs a GSA annealing function.
- GSAMarkovLength(int) - Static method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction
-
The Markov length for GSA, i.e.
- GSATemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
-
The GSA temperature function.
- GSATemperatureFunction(double, double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction
-
Constructs a GSA temperature function.
- GumbelDistribution - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.GumbelDistribution
-
Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\).
- GumbelDistribution(double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.GumbelDistribution
-
Create an instance with the given parameter values.
- i() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets the value of i.
- i - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
the row index
- I - Static variable in class dev.nm.number.complex.Complex
-
a number representing 0.0 + 1.0i, the square root of -1
- I - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- identity(int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a new identity matrix.
- IdentityHashSet<T> - Class in dev.nm.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 dev.nm.misc.datastructure.IdentityHashSet
-
Construct an empty IdentityHashSet
.
- IdentityHashSet(Collection<T>) - Constructor for class dev.nm.misc.datastructure.IdentityHashSet
-
Construct an IdentityHashSet
with a collection of items.
- IdentityPreconditioner - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
This identity preconditioner is used when no preconditioning is applied.
- IdentityPreconditioner() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
-
- ifelse(double[], DoubleUtils.ifelse) - Static method in class dev.nm.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 dev.nm.stat.random.rng.multivariate
-
An i.i.d.
- IID(RandomNumberGenerator, int) - Constructor for class dev.nm.stat.random.rng.multivariate.IID
-
Construct a rvg that outputs vectors that have i.i.d.
- ILPBranchAndBoundMinimizer - Class in dev.nm.solver.multivariate.constrained.integer.linear.bb
-
This is a Branch-and-Bound algorithm that solves Integer Linear Programming problems.
- ILPBranchAndBoundMinimizer(ILPBranchAndBoundMinimizer.ActiveListFactory) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer
-
Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.
- ILPBranchAndBoundMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer
-
Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.
- ILPBranchAndBoundMinimizer.ActiveListFactory - Interface in dev.nm.solver.multivariate.constrained.integer.linear.bb
-
This factory constructs a new instance of ActiveList
for each Integer Linear Programming problem.
- ILPNode - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
Construct a BB node and associate it with an ILP problem.
- ILPProblem - Interface in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
Construct an ILP problem, in which the variables can be real or integral.
- imaginary() - Method in class dev.nm.number.complex.Complex
-
Get the imaginary part of this complex number.
- ImmutableMatrix - Class in dev.nm.algebra.linear.matrix.doubles
-
This is a read-only view of a
Matrix
instance.
- ImmutableMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
Construct a read-only version of a matrix.
- ImmutableVector - Class in dev.nm.algebra.linear.vector.doubles
-
This is a read-only view of a
Vector
instance.
- ImmutableVector(Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
Construct a read-only version of a vector.
- ImplicitModelPCA - Class in dev.nm.stat.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 dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA
-
Constructs an implicit-model that will have K implicit factors.
- ImplicitModelPCA(Matrix) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA
-
Constructs an implicit-model that will have one and only one implicit
factors.
- ImplicitModelPCA(Matrix, double) - Constructor for class dev.nm.stat.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 dev.nm.stat.factor.implicitmodelpca
-
the regression results
- ImportanceSampling - Class in dev.nm.stat.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 dev.nm.stat.random.variancereduction.ImportanceSampling
-
- inactiveSize() - Method in class dev.nm.misc.algorithm.ActiveSet
-
Get the number of inactive indices.
- incomingArcs(V) - Method in interface dev.nm.graph.DiGraph
-
Gets the set of all incoming arcs associated with a vertex in this graph.
- incomingArcs(V) - Method in class dev.nm.graph.type.SparseDiGraph
-
- incomingArcs(V) - Method in class dev.nm.graph.type.SparseTree
-
- incomingArcs(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- IndependentCoVAR - Class in tech.nmfin.meanreversion.daspremont2008
-
This algorithm finds the independent variables based on the covariance matrix.
- IndependentCoVAR(Matrix, double) - Constructor for class tech.nmfin.meanreversion.daspremont2008.IndependentCoVAR
-
Runs the algorithm with the given covariance matrix.
- index() - Method in class dev.nm.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 dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
the index to the variable, counting from 1.
- index - Variable in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
the index of an integral variable
- Infantino2010PCA - Class in tech.nmfin.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 tech.nmfin.infantino2010.Infantino2010PCA
-
- Infantino2010PCA.Signal - Class in tech.nmfin.infantino2010
-
- Infantino2010Regime - Class in tech.nmfin.infantino2010
-
Detects the current regime (mean reversion or momentum) by cross-sectional volatility.
- Infantino2010Regime(int) - Constructor for class tech.nmfin.infantino2010.Infantino2010Regime
-
- Infantino2010Regime.Regime - Enum in tech.nmfin.infantino2010
-
- informationCriteria() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
-
Gets the model selection criteria.
- init(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
Initializes the algorithm states with initial \(x_{min}\) and
\(f_{min}\) before iterations.
- init(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution
-
- initialCondition() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
Initializes the grid with the initial conditions.
- initialReturns - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- initials - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- InitialsFactory - Interface in dev.nm.solver.multivariate.initialization
-
Some optimization algorithms, e.g., Nelder-Mead, Differential-Evolution, require a set of initial points to work with.
- initialSigma2 - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- innerProduct(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- innerProduct(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- InnerProduct - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.InnerProduct
-
Compute the inner product of two matrices.
- innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- innerProduct(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- innerProduct(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the inner or dot product of two vectors.
- innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- innerProduct(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Inner product in the Euclidean space is the dot product.
- innerProduct(H) - Method in interface dev.nm.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.number.DoubleUtils
-
Convert an int
array to a double
array.
- intArray2List(int[]) - Static method in class dev.nm.number.DoubleUtils
-
Convert an int
array to a list.
- IntegerDomain(int, int[]) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
Construct the integral domain for an integral variable.
- IntegerDomain(int, int, int) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
Construct the integral domain for an integral variable.
- IntegerDomain(int, int, int, int) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
-
Construct the integral domain for an integral variable.
- Integral - Class in dev.nm.stat.stochasticprocess.univariate.integration
-
The class represents an integral of a function, in the Lebesgue sense.
- Integral(FiltrationFunction) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.Integral
-
Construct an integral from an integrand.
- IntegralConstrainedCellFactory - Class in dev.nm.solver.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 dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory
-
Construct an instance of IntegralConstrainedCellFactory
.
- IntegralConstrainedCellFactory.AllIntegers - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
This integral constraint makes all variables in the objective function integral variables.
- IntegralConstrainedCellFactory.IntegerConstraint - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
The integral constraints are defined by implementing this interface
.
- IntegralConstrainedCellFactory.SomeIntegers - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
-
This integral constraint makes some variables in the objective function integral variables.
- IntegralDB - Class in dev.nm.stat.stochasticprocess.univariate.integration
-
This class evaluates the following class of integrals.
- IntegralDB(FiltrationFunction) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDB
-
Construct an integral for f with respect to dB.
- IntegralDt - Class in dev.nm.stat.stochasticprocess.univariate.integration
-
This class evaluates the following class of integrals.
- IntegralDt(FiltrationFunction) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDt
-
Construct an integral for f with respect to dt.
- IntegralExpectation - Class in dev.nm.stat.stochasticprocess.univariate.integration
-
This class computes the expectation of the following class of integrals.
- IntegralExpectation(Integral, double, double, int, int, long) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation
-
Compute the expectation for the integral of a stochastic process.
- IntegralExpectation(Integral, double, double, int, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation
-
Compute the expectation for the integral of a stochastic process.
- integrate(ODE1stOrder, double[]) - Method in interface dev.nm.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 dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaIntegrator
-
- integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.ChangeOfVariable
-
- integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature
-
- integrate(UnivariateRealFunction, double, double) - Method in interface dev.nm.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 dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
- integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Romberg
-
- integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
- integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.Riemann
-
- integrate(UnivariateRealFunction, double, double, SubstitutionRule) - Method in class dev.nm.analysis.integration.univariate.riemann.Riemann
-
Integrate a function, f, from a to b possibly using change of variable.
- Integrator - Interface in dev.nm.analysis.integration.univariate.riemann
-
This defines the interface for the numerical integration of definite integrals of univariate functions.
- intercept() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Checks if an intercept term is added to the linear regression.
- interpolate(BivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
-
- interpolate(BivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicSpline
-
- interpolate(BivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BilinearInterpolation
-
- interpolate(BivariateGrid) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGridInterpolation
-
Construct a real valued function from a grid of observations.
- interpolate(MultivariateGrid) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGridInterpolation
-
Construct a real valued function from a grid of observations.
- interpolate(MultivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.RecursiveGridInterpolation
-
- Interpolation - Interface in dev.nm.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 dev.nm.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 dev.nm.interval.Interval
-
Construct an interval.
- IntervalRelation - Enum in dev.nm.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 dev.nm.interval
-
This is a disjoint set of intervals.
- Intervals() - Constructor for class dev.nm.interval.Intervals
-
Construct an empty set of intervals.
- Intervals(Interval<T>) - Constructor for class dev.nm.interval.Intervals
-
Construct a set that contains only one interval.
- Intervals(T, T) - Constructor for class dev.nm.interval.Intervals
-
Construct a set that contains only one interval [begin, end].
- Intervals(Interval<T>...) - Constructor for class dev.nm.interval.Intervals
-
Construct a set of intervals.
- Intervals(Intervals<T>) - Constructor for class dev.nm.interval.Intervals
-
Copy constructor.
- IntTimeTimeSeries - Interface in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime
-
This is a univariate time series indexed by integers.
- IntTimeTimeSeries.Entry - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime
-
This is the TimeSeries.Entry
for an integer number -indexed univariate time series.
- intValue() - Method in class dev.nm.number.complex.Complex
-
- intValue() - Method in class dev.nm.number.Real
-
- intValue() - Method in class dev.nm.number.ScientificNotation
-
- InvalidLicense - Error in dev.nm.misc.license
-
This is the
LicenseError
thrown when calling a class or method that is not yet licensed.
- InvalidLicense(String) - Constructor for error dev.nm.misc.license.InvalidLicense
-
- invdet() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix
-
One over the determinant of H: 1/|H|, which is an integer.
- Inverse - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.Inverse
-
Constructs the inverse of a matrix.
- Inverse(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Inverse
-
Constructs the inverse of a matrix.
- inverse() - Method in interface dev.nm.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 dev.nm.number.complex.Complex
-
- inverse() - Method in class dev.nm.number.Real
-
- INVERSE - Static variable in class dev.nm.stat.random.variancereduction.AntitheticVariates
-
- inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkCloglog
-
- inverse(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.link.LinkFunction
-
Inverse of the link function, i.e., g-1(x).
- inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkIdentity
-
- inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverse
-
- inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverseSquared
-
- inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLog
-
- inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLogit
-
Inverse of the link function, i.e., g-1(x).
- inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkProbit
-
- inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkSqrt
-
- INVERSE_FINE_STRUCTURE_ALPHA1 - Static variable in class dev.nm.misc.PhysicalConstants
-
The inverse fine-structure constant \(\alpha^{-1}\) (dimensionless).
- inverseCovariance() - Method in interface dev.nm.stat.covariance.covarianceselection.CovarianceSelectionSolver
-
Get the estimated inverse Covariance matrix of the selection problem.
- inverseCovariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
-
Gets the inverse of the estimated covariance matrix.
- inverseCovariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Get the inverse of the estimated covariance matrix.
- inverseCovariance() - Method in class tech.nmfin.meanreversion.daspremont2008.CovarianceEstimation
-
- InverseIteration - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
-
Inverse iteration is an iterative eigenvalue algorithm.
- InverseIteration(Matrix, double, InverseIteration.StoppingCriterion) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Construct an instance of InverseIteration to find the corresponding eigenvector.
- InverseIteration(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Construct an instance of InverseIteration to find the corresponding eigenvector.
- InverseIteration.StoppingCriterion - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
-
This interface defines the convergence criterion.
- InverseNonExistent() - Constructor for exception dev.nm.algebra.structure.Field.InverseNonExistent
-
Construct an instance of InverseNonExistent
- InverseTransformSampling - Class in dev.nm.stat.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 dev.nm.stat.random.rng.univariate.InverseTransformSampling
-
Construct a random number generator to sample from a distribution.
- InverseTransformSampling(ProbabilityDistribution) - Constructor for class dev.nm.stat.random.rng.univariate.InverseTransformSampling
-
Construct a random number generator to sample from a distribution.
- InverseTransformSamplingEVDRNG - Class in dev.nm.stat.evt.evd.univariate.rng
-
Generate random numbers according to a given univariate extreme value distribution, by
inverse transform sampling.
- InverseTransformSamplingEVDRNG(UnivariateEVD) - Constructor for class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
-
Create an instance with the given extreme value distribution.
- InverseTransformSamplingEVDRNG(UnivariateEVD, long...) - Constructor for class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
-
Create an instance with the given extreme value distribution and seeds for this random number
generator.
- InverseTransformSamplingExpRNG - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.random.rng.univariate.gamma
-
- InverseTransformSamplingGammaRNG(double, double) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
-
Construct a rng that samples from a truncated standard Normal distribution using inverse
sampling technique.
- InvertingVariable - Class in dev.nm.analysis.integration.univariate.riemann.substitution
-
This is the inverting-variable transformation.
- InvertingVariable(double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
Construct an InvertingVariable
substitution rule.
- invOfwAtwA() - Method in class dev.nm.stat.regression.linear.LMProblem
-
(wA' * wA)-1
- IPMinimizer<T extends IPProblem,S extends MinimizationSolution<Vector>> - Interface in dev.nm.solver.multivariate.constrained.integer
-
An Integer Programming minimizer minimizes an objective function subject to equality/inequality
constraints as well as integral constraints.
- IPProblem - Interface in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
Construct a constrained optimization problem with integral constraints.
- IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[]) - Constructor for class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
Construct a constrained optimization problem with integral constraints.
- ir() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
-
- is(IntervalRelation, Interval<T>) - Method in class dev.nm.interval.Interval
-
Check whether this
and Y
satisfies a certain Allen's interval relation.
- isAcyclic(UnDiGraph<V, UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils
-
Check if an undirected graph is acyclic.
- isAcyclic() - Method in class dev.nm.graph.type.SparseDAGraph
-
Runs validity check to ensure that this DA graph is indeed acyclic.
- isAllZeros(double[], double) - Static method in class dev.nm.number.DoubleUtils
-
Check if a double
array contains only 0s, entry-by-entry.
- isArray(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Check if a table is a row or a column.
- isBalanced() - Method in class dev.nm.stat.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 dev.nm.interval.IntervalRelation
-
Check if X and Y satisfy a certain relation.
- isBracketing(double, double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
Check whether [xl, xu] is bracketing x.
- isCandidate() - Method in interface dev.nm.misc.algorithm.bb.BBNode
-
Check if this node is a possible solution to the original problem, e.g., not pruned.
- isCandidate() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
- isColumn(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Check if a table is a column.
- isConnected(UnDiGraph<V, ? extends UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils
-
Check whether an undirected graph is connected.
- isConverged() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
This is the convergence criterion.
- isConverged() - Method in class dev.nm.solver.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 dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
-
Checks if the algorithm converges.
- isCyclic() - Method in class dev.nm.graph.algorithm.traversal.DFS
-
Checks if the graph is cyclic.
- isCyclic - Variable in class dev.nm.graph.algorithm.traversal.DFS.Node
-
- isCyclic() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node
-
Check whether this node is on a cyclic path of the graph.
- isDiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a square matrix is a diagonal matrix, up to a precision.
- isEmpty() - Method in class dev.nm.graph.community.EdgeBetweeness
-
Checks if there is no edge, e.g., all vertices are isolated.
- isEmpty() - Method in interface dev.nm.misc.algorithm.bb.ActiveList
-
Returns true
if this collection contains no elements.
- isEmpty() - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- isFat(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Check if a table is fat.
- isFeasible() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Check if this table is feasible.
- isFixedIndex(int, Map<Integer, Double>) - Static method in class dev.nm.analysis.function.SubFunction
-
Checks whether a particular index corresponds a fixed variable/value.
- isFixedIndex(int) - Method in class dev.nm.analysis.function.SubFunction
-
Checks whether a particular index corresponds a fixed variable/value.
- isFree(int) - Method in interface dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- isFree(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- isFree(int) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
-
- isHessenberg(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Check if H is upper Hessenberg.
- isIdempotent(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is idempotent.
- isIdentity(Matrix, double) - Static method in class dev.nm.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 dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
Check if a solution is within a box.
- isInfinite(Complex) - Static method in class dev.nm.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 dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
- isLASSO() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Checks if the LASSO variation of LARS is used.
- isLowerBidiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is lower bidiagonal, up to a precision.
- isLowerTriangular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is lower triangular, up to a precision.
- isMagicSquare(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
- isMinFound() - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the convergence criterion
- isMinFound() - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution
-
the convergence criterion
- isMinFound() - Method in class dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer.Solution
-
This algorithm stops only after a pre-specified number of iterations.
- isMinFound() - Method in class dev.nm.solver.univariate.bracketsearch.GoldenMinimizer.Solution
-
- isNaN(Vector) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a vector contains any NaN
entry.
- isNaN(Complex) - Static method in class dev.nm.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 dev.nm.number.DoubleUtils
-
Check if d
is negative.
- isNegligible(Matrix, int, int, double) - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DeflationCriterion
-
Checks whether a sub-diagonal element is sufficiently small.
- isNullOrEmpty(String) - Static method in class dev.nm.misc.StringUtils
-
Checks if a string is either null or empty.
- isNullRejected(double) - Method in class dev.nm.stat.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 dev.nm.number.DoubleUtils
-
Check if a double
is a number, i.e., it is not ∞
or
NaN
.
- isOK() - Method in class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
-
- isOK() - Method in interface tech.nmfin.meanreversion.cointegration.check.PairingCheck
-
- isOrthogonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is orthogonal, up to a precision.
- isPositive(double, double) - Static method in class dev.nm.number.DoubleUtils
-
Check if d
is positive.
- isPositiveDefinite(Matrix, double) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a square matrix is positive definite, up to a precision.
- isPow2(int) - Static method in class dev.nm.number.DoubleUtils
-
Check if an integer is a power of 2.
- isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.HybridMCMC
-
- isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
-
- isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis
-
- isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings
-
- isProposalAccepted(RealScalarFunction, RandomLongGenerator, Vector, Vector) - Static method in class dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
-
- isQuasiTriangular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is quasi (upper) triangular, up to a precision.
- isReal(Complex) - Static method in class dev.nm.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 dev.nm.number.NumberUtils
-
Check if a number is a real number.
- isReducedRowEchelonForm(Matrix, double) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Check if H is upper Hessenberg and is reducible.
- isReducible() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearEqualityConstraints
-
Check if we can reduce the number of linear equalities.
- isResidualSmall(double) - Method in class dev.nm.misc.algorithm.iterative.tolerance.AbsoluteTolerance
-
- isResidualSmall(double) - Method in class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance
-
- isResidualSmall(double) - Method in interface dev.nm.misc.algorithm.iterative.tolerance.Tolerance
-
Checks if the updated residual satisfies the tolerance criteria.
- isRow(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Check if a table is a row.
- isRowEchelonForm(Matrix, double) - Static method in class dev.nm.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 dev.nm.misc.datastructure.DimensionCheck
-
Check if two tables have the same dimension.
- isSatisfied(Constraints, Vector, double) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
-
Checks if the constraints are satisfied.
- isSatisfied(Constraints, Vector) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
-
Checks if the constraints are satisfied.
- isScalar(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
- isSelected(int) - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
-
Checks whether a particular indexed factor is selected in the model.
- isSingular(Matrix, double) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is skew symmetric.
- isSpanned(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Check whether a vector is in the span of the basis.
- isSquare(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Check if a table is square.
- isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AfterIterations
-
- isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AfterNoImprovement
-
- isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AndStopConditions
-
- isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AtThreshold
-
- isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.OrStopConditions
-
- isStopped(Vector, double...) - Method in interface dev.nm.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 dev.nm.graph.GraphUtils
-
Check whether a directed graph is strongly connected.
- isSymmetric(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is symmetric.
- isSymmetricPositiveDefinite(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a square matrix is symmetric and positive definite.
- isTall(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Check if a table is tall.
- isTridiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is tridiagonal, up to a precision.
- isTrue(double, int) - Method in interface dev.nm.number.DoubleUtils.which
-
Decide whether x is to be selected.
- isUnique() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSolution
-
Return true
if the quadratic programming problem has only one
solution.
- isUnreduced(double) - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is upper bidiagonal, up to a precision.
- isUpperTriangular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a matrix is upper triangular, up to a precision.
- isValidated(Matrix) - Method in class dev.nm.stat.test.distribution.pearson.AS159
-
Checks whether a matrix satisfies the row and column sums.
- isWeekend(DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Check if the given time is a weekend.
- isZero() - Method in class dev.nm.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 dev.nm.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 dev.nm.number.DoubleUtils
-
Check if d
is zero.
- iter - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- iter - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the current iteration count
- iterate(PrimalDualSolution, Vector, Vector, double) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.AntoniouLu2007
-
- iterate(PrimalDualSolution, Vector, Vector, double) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.SDPT3v4
-
- IteratesMonitor<S> - Class in dev.nm.misc.algorithm.iterative.monitor
-
- IteratesMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.IteratesMonitor
-
Construct a monitor to keep track of the states in all iterations.
- iteration - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- iteration(Elliott2005DLM, double[]) - Static method in class tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter
-
- IterationBody<T> - Interface in dev.nm.misc.parallel
-
This interface defines the code snippet to be run in parallel.
- IterationMonitor<S> - Interface in dev.nm.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 dev.nm.solver.multivariate.unconstrained.c2
-
A maximization problem is simply minimizing the negative of the objective function.
- IterativeC2Maximizer(T) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer
-
Construct a multivariate maximizer to maximize an objective function.
- IterativeC2Maximizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer
-
Construct a multivariate maximizer to maximize an objective function.
- IterativeC2Maximizer.Solution - Interface in dev.nm.solver.multivariate.unconstrained.c2
-
- IterativeC2Minimizer - Interface in dev.nm.solver.multivariate.unconstrained.c2
-
This is a minimizer that minimizes a twice continuously differentiable, multivariate function.
- IterativeCentralDifference - Class in dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.solver.multivariate.unconstrained
-
This is an iterative multivariate minimizer.
- IterativeSolution<S> - Interface in dev.nm.solver
-
Many minimization algorithms work by starting from some given initials and iteratively moving
toward an approximate solution.
- iterator() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- iterator() - Method in class dev.nm.combinatorics.Twiddle
-
- iterator() - Method in class dev.nm.misc.algorithm.CartesianProduct
-
- iterator() - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- iterator() - Method in class dev.nm.misc.datastructure.MultiDimensionalGrid
-
- iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
- iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
- iterator() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
-
- iterator() - Method in class dev.nm.stat.dlm.univariate.DLMSeries
-
- iterator() - Method in class dev.nm.stat.random.rng.ConstantSeeder
-
- iterator() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.DynamicCreator
-
- iterator() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid
-
- iterator() - Method in class dev.nm.stat.stochasticprocess.timegrid.UnitGrid
-
- iterator() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries
-
- iterator() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- iterator() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
-
- IWLS - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.glm.IWLS
-
Construct an instance to run the Iteratively Re-weighted Least Squares algorithm.
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
The sub-diagonal entries of the unit lower triangular matrix L.
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot
-
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the lower triangular matrix L, such that P * A = L * U.
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
-
- L() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Cholesky
-
Get the lower triangular matrix L.
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewicz
-
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized
-
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskySparse
-
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyWang2006
-
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Get L as in the LDL decomposition.
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
-
- L() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition
-
Get the lower triangular matrix L as in the LU decomposition.
- L() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Gets the lower triangular matrix L in the LDL decomposition.
- Label(SimplexTable.LabelType, int) - Constructor for class dev.nm.solver.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 dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries
by lagging the time series.
- lag(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries
by lagging the time series.
- lag(int, int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries
by lagging the time series.
- lag(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries
by lagging the time series.
- LaguerrePolynomials - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
-
Laguerre polynomials are defined by the recurrence relation below.
- LaguerrePolynomials() - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerrePolynomials
-
- LaguerreRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
-
- LaguerreRule(int, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
Create a Laguerre rule of the given order.
- Lai2010NPEBModel - Class in tech.nmfin.portfoliooptimization.lai2010
-
The Non-Parametric Empirical Bayes (NPEB) model described in the reference
computes the optimal weights for asset allocation.
- Lai2010NPEBModel(MVOptimizer, int) - Constructor for class tech.nmfin.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(MVOptimizer, int, ReturnsMoments.Estimator, ReturnsResamplerFactory, CetaMaximizer) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel
-
Constructs an instance of the model.
- Lai2010NPEBModel.OptimalWeights - Class in tech.nmfin.portfoliooptimization.lai2010
-
- Lai2010OptimizationAlgorithm - Class in tech.nmfin.portfoliooptimization
-
- Lai2010OptimizationAlgorithm(MVOptimizer, ReturnsMoments.Estimator, ReturnsResamplerFactory, int, double) - Constructor for class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm
-
- Lai2010OptimizationAlgorithm(double) - Constructor for class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm
-
- lambda - Variable in class dev.nm.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 dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
-
Gets sample eigenvalues.
- Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
-
Stores the Beta distribution parameters.
- Lambda(int, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
-
Stores the Binomial distribution parameters.
- Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution.Lambda
-
Stores the Gamma distribution parameters.
- Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda
-
Construct a Log-Normal distribution.
- Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
-
Construct a Normal distribution.
- lambda() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSOProblem
-
Gets the penalization parameter for the unconstrained form of LASSO.
- lambda() - Method in class tech.nmfin.portfoliooptimization.clm.TurningPoint
-
- lambda1() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
Gets the transition intensity from bull market to bear market.
- lambda1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
-
- lambda2() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
Gets the transition intensity from bear market to bull market.
- lambda2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
-
- lambda_Jacobian - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
lambda Jacobian
- lambdaCol - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- Lanczos - Class in dev.nm.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 dev.nm.analysis.function.special.gamma.Lanczos
-
Construct a Lanczos approximation instance.
- Lanczos() - Constructor for class dev.nm.analysis.function.special.gamma.Lanczos
-
Construct a Lanczos approximation instance using default parameters.
- LARSFitting - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.regression.linear.lasso.lars.LARSFitting
-
Estimates the entire LARS sequence of coefficients with the default
epsilon.
- LARSFitting(LARSProblem) - Constructor for class dev.nm.stat.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 dev.nm.stat.regression.linear.lasso.lars
-
- LARSProblem - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Constructs a Least Angel Regression (LARS) problem.
- LARSProblem(Vector, Matrix, boolean, boolean) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Copy constructor.
- lastEdge(V) - Method in class dev.nm.graph.algorithm.shortestpath.Dijkstra
-
- lastEdge(V) - Method in interface dev.nm.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 dev.nm.analysis.function.FunctionOps
-
Calculates the least common multiple of integer a and integer b.
- LD() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- LDDecomposition - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- LDFactorizationFromRoot - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot
-
Creates a decomposition for a symmetric tridiagonal matrix T.
- LDLt - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Run the LDL decomposition on a real and symmetric (hence square) matrix.
- LDLt(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Run the LDL decomposition on a real and symmetric (hence square) matrix.
- LeapFrogging - Class in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Constructs a new instance with the given parameters.
- LeapFrogging.DynamicsState - Class in dev.nm.stat.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 dev.nm.solver.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 dev.nm.solver.multivariate.minmax.LeastPth
-
Construct a minmax minimizer using the Least p-th method.
- LeastSquares - Class in dev.nm.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 dev.nm.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 dev.nm.analysis.curvefit.LeastSquares
-
Construct a new instance of this algorithm, which uses uniform weighting for the
observations.
- LeastSquares.Weighting - Interface in dev.nm.analysis.curvefit
-
This interface defines a weighting for observations.
- Lebesgue - Class in dev.nm.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 dev.nm.analysis.integration.univariate.Lebesgue
-
Construct a Lebesgue integral.
- LEcuyer - Class in dev.nm.stat.random.rng.univariate.uniform.linear
-
This is the uniform random number generator recommended by L'Ecuyer in 1996.
- LEcuyer() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
-
Construct a LEcuyer pseudo uniform random generator.
- LEcuyer(long, long, long, long, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
-
Construct a LEcuyer pseudo uniform random generator and then seed.
- LedoitWolf2004 - Class in dev.nm.stat.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 dev.nm.stat.covariance.LedoitWolf2004
-
Creates the algorithm instance, using an unbiased sample covariance
matrix by default.
- LedoitWolf2004(boolean) - Constructor for class dev.nm.stat.covariance.LedoitWolf2004
-
Creates the algorithm instance, with the option to use an unbiased or
biased sample covariance matrix.
- LedoitWolf2004.Result - Class in dev.nm.stat.covariance
-
The estimator and some intermediate values computed by the algorithm.
- LedoitWolf2016 - Class in dev.nm.stat.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 dev.nm.stat.covariance.nlshrink.LedoitWolf2016
-
- LedoitWolf2016(boolean) - Constructor for class dev.nm.stat.covariance.nlshrink.LedoitWolf2016
-
- LedoitWolf2016.Result - Class in dev.nm.stat.covariance.nlshrink
-
the estimator and some intermediate values computed by the algorithm
- leftConfidenceInterval(double) - Method in class dev.nm.stat.test.mean.T
-
Get the one sided left confidence interval, [0, a]
- leftConfidenceInterval(double) - Method in class dev.nm.stat.test.variance.F
-
Compute the one sided left confidence interval, [0, a]
- leftMultiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Left multiplies a matrix.
- leftOneSidedPvalue() - Method in class dev.nm.stat.test.mean.T
-
Get the left, one-sided p-value.
- leftOneSidedPvalue() - Method in class dev.nm.stat.test.rank.SiegelTukey
-
Get the left, one-sided p-value.
- leftOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
Get the left, one-sided p-value.
- leftOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
Get the left, one-sided p-value.
- leftOneSidedPvalue() - Method in class dev.nm.stat.test.variance.F
-
Get the left, one-sided p-value.
- leftShift(T[]) - Static method in class dev.nm.misc.ArrayUtils
-
Get a left shifted array.
- leftShift(double...) - Static method in class dev.nm.number.DoubleUtils
-
Perform a in-memory left-shift (by 1 cell} to an array.
- leftShift(double[], int) - Static method in class dev.nm.number.DoubleUtils
-
Perform a in-memory right-shift (by k
cells} to an array.
- leftShiftCopy(double...) - Static method in class dev.nm.number.DoubleUtils
-
Get a left shifted (by 1 cell) copy of an array.
- leftShiftCopy(double[], int) - Static method in class dev.nm.number.DoubleUtils
-
Get a left shifted (by k
cells) copy of an array.
- LegendrePolynomials - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
-
A Legendre polynomial is defined by the recurrence relation below.
- LegendrePolynomials() - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendrePolynomials
-
- LegendreRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
-
- LegendreRule(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
Create a Legendre rule of the given order.
- Lehmer - Class in dev.nm.stat.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 dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
-
Construct a Lehmer (pure) linear congruential generator.
- Lehmer(long, long, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
-
Construct a skipping ahead Lehmer (pure) linear congruential generator.
- Lehmer() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
-
Construct a Lehmer (pure) linear congruential generator.
- length() - Method in class dev.nm.analysis.sequence.Fibonacci
-
- length() - Method in interface dev.nm.analysis.sequence.Sequence
-
Get the number of computed terms in the sequence.
- length() - Method in class dev.nm.geometry.LineSegment
-
Get the length of the line segment.
- LessThanConstraints - Interface in dev.nm.solver.multivariate.constrained.constraint
-
The domain of an optimization problem may be restricted by less-than or equal-to constraints.
- Levene - Class in dev.nm.stat.test.variance
-
The Levene test tests for the equality of variance of groups.
- Levene(double...) - Constructor for class dev.nm.stat.test.variance.Levene
-
Perform the Levene test to test for equal variances across the groups.
- Levene(Levene.Type, double[]...) - Constructor for class dev.nm.stat.test.variance.Levene
-
Perform the Levene test to test for equal variances across the groups.
- Levene.Type - Enum in dev.nm.stat.test.variance
-
the available implementations when computing the absolute deviations
- leverage() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the leverage.
- License - Class in dev.nm.misc.license
-
This is the license management system for the library.
- LICENSE_FILE_PROPERTY - Static variable in class dev.nm.misc.license.License
-
The system property term for setting license file.
- LicenseError - Error in dev.nm.misc.license
-
General error regarding the license, e.g., errors when loading license.
- LicenseError(String) - Constructor for error dev.nm.misc.license.LicenseError
-
- LIGHT_SPEED_C - Static variable in class dev.nm.misc.PhysicalConstants
-
The speed of light in a vacuum \(c\), \(c_0\) in meters per second (m s-1).
- Lilliefors - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Construct a sparse matrix in LIL format.
- LILSparseMatrix(int, int, int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Construct a sparse matrix in LIL format.
- LILSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
Copy constructor.
- LinearCongruentialGenerator - Interface in dev.nm.stat.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 dev.nm.solver.multivariate.constrained.constraint.linear
-
This is a collection of linear constraints for a real-valued optimization problem.
- LinearConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
Construct a collection of linear constraints.
- LinearEqualityConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
-
This is a collection of linear equality constraints.
- LinearEqualityConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearEqualityConstraints
-
Construct a collection of linear equality constraints.
- linearEqualityConstraints() - Method in interface tech.nmfin.portfoliooptimization.markowitz.constraints.QPConstraint
-
- linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
-
- linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint
-
Deprecated.
- linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling
-
- linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity
-
- linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit
-
- LinearFit - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.LinearFit
-
Create an instance with the assumption of c = 2
.
- LinearFit(double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.LinearFit
-
This fitting assumes c
is a constant.
- LinearGreaterThanConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
-
This is a collection of linear greater-than-or-equal-to constraints.
- LinearGreaterThanConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
-
Construct a collection of linear greater-than or equal-to constraints.
- linearGreaterThanConstraints() - Method in interface tech.nmfin.portfoliooptimization.markowitz.constraints.QPConstraint
-
- linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
-
- linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint
-
Deprecated.
- linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling
-
- linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity
-
- linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit
-
- linearInterpolate(double, double, double, double, double) - Static method in class dev.nm.analysis.function.FunctionOps
-
Linear interpolation between two points.
- LinearInterpolation - Class in dev.nm.analysis.curvefit.interpolation.univariate
-
(Piecewise-)Linear interpolation fits a curve by interpolating linearly between two adjacent
data-points.
- LinearInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.LinearInterpolation
-
- LinearInterpolator - Class in dev.nm.analysis.curvefit.interpolation
-
Define a univariate function by linearly interpolating between adjacent points.
- LinearInterpolator(OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.LinearInterpolator
-
Construct a univariate function by linearly interpolating between adjacent points.
- LinearKalmanFilter - Class in dev.nm.stat.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 dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Construct a Kalman filter from a univariate controlled dynamic linear model.
- LinearLessThanConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
-
This is a collection of linear less-than-or-equal-to constraints.
- LinearLessThanConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearLessThanConstraints
-
Construct a collection of linear less-than or equal-to constraints.
- linearLessThanConstraints() - Method in interface tech.nmfin.portfoliooptimization.markowitz.constraints.QPConstraint
-
- linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
-
- linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint
-
Deprecated.
- linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling
-
- linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity
-
- linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit
-
- LinearModel - Interface in dev.nm.stat.regression.linear
-
A linear model provides fitting and the residual analysis (goodness of fit).
- LinearRepresentation - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
Construct the linear representation of an ARMA model.
- LinearRepresentation(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
- LinearRoot - Class in dev.nm.analysis.function.polynomial.root
-
This is a solver for finding the roots of a linear equation.
- LinearRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.LinearRoot
-
- LinearSystemSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver
-
Construct a solver for a linear system of equations.
- LinearSystemSolver.NoSolution - Exception in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.linearsystem
-
This is the solution to a linear system of equations.
- LineSearch - Interface in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.linesearch.LineSearch.Solution
-
Get the increment α so that f(x + α * d) is (approximately) minimized.
- linesearch - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- LineSearch.Solution - Interface in dev.nm.solver.multivariate.unconstrained.c2.linesearch
-
This is the solution to a line search minimization.
- LineSegment - Class in dev.nm.geometry
-
Represent a line segment.
- LineSegment(Point, Point) - Constructor for class dev.nm.geometry.LineSegment
-
Create a line segment with two given endpoints.
- link() - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
Get the link function of this distribution.
- LinkCloglog - Class in dev.nm.stat.regression.linear.glm.distribution.link
-
This class represents the complementary log-log link function:
g(x) = log(-log(1 - x))
- LinkCloglog() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkCloglog
-
- LinkFunction - Interface in dev.nm.stat.regression.linear.glm.distribution.link
-
This interface represents a link function g(x) in Generalized Linear Model (GLM).
- LinkIdentity - Class in dev.nm.stat.regression.linear.glm.distribution.link
-
This class represents the identity link function:
g(x) = x
- LinkIdentity() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkIdentity
-
- LinkInverse - Class in dev.nm.stat.regression.linear.glm.distribution.link
-
This class represents the inverse link function:
g(x) = 1/x
- LinkInverse() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverse
-
- LinkInverseSquared - Class in dev.nm.stat.regression.linear.glm.distribution.link
-
This class represents the inverse-squared link function:
g(x) = 1/x2
- LinkInverseSquared() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverseSquared
-
- LinkLog - Class in dev.nm.stat.regression.linear.glm.distribution.link
-
This class represents the log link function:
g(x) = log(x)
- LinkLog() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkLog
-
- LinkLogit - Class in dev.nm.stat.regression.linear.glm.distribution.link
-
This class represents the logit link function:
\[
g(x) = \log(\frac{\mu}{1-\mu})
\]
- LinkLogit() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkLogit
-
- LinkProbit - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.glm.distribution.link.LinkProbit
-
- LinkSqrt - Class in dev.nm.stat.regression.linear.glm.distribution.link
-
This class represents the square-root link function:
g(x) = sqrt(x)
- LinkSqrt() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkSqrt
-
- linshrink_tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
-
Gets linear shrinkage tau in ascending order.
- LjungBox - Class in dev.nm.stat.test.timeseries.portmanteau
-
The Ljung-Box test (named for Greta M.
- LjungBox(double[], int, int) - Constructor for class dev.nm.stat.test.timeseries.portmanteau.LjungBox
-
Perform the Ljung-Box test to check auto-correlation in a time series.
- LLD() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- LMBeta - Class in dev.nm.stat.regression.linear
-
Beta coefficients are the outcomes of fitting a linear regression model.
- LMBeta(Vector) - Constructor for class dev.nm.stat.regression.linear.LMBeta
-
Constructs an instance of Beta
.
- LMDiagnostics - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics
-
Constructs an instance of the Diagnostics
from the results of residual analysis.
- LMInformationCriteria - Class in dev.nm.stat.regression.linear.residualanalysis
-
The information criteria measure the goodness of fit of an estimated statistical model.
- LMInformationCriteria(LMResiduals) - Constructor for class dev.nm.stat.regression.linear.residualanalysis.LMInformationCriteria
-
Computes the information criteria from residual analysis.
- LMProblem - Class in dev.nm.stat.regression.linear
-
This is a linear regression or a linear model (LM) problem.
- LMProblem(Vector, Matrix, boolean, Vector) - Constructor for class dev.nm.stat.regression.linear.LMProblem
-
Constructs a linear regression problem.
- LMProblem(Vector, Matrix, Vector) - Constructor for class dev.nm.stat.regression.linear.LMProblem
-
Constructs a linear regression problem, assuming a constant term (the intercept).
- LMProblem(Vector, Matrix, boolean) - Constructor for class dev.nm.stat.regression.linear.LMProblem
-
Constructs a linear regression problem, assuming equal weights to all observations.
- LMProblem(Vector, Matrix) - Constructor for class dev.nm.stat.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 dev.nm.stat.regression.linear.LMProblem
-
Copy constructor.
- LMResiduals - Class in dev.nm.stat.regression.linear.residualanalysis
-
This is the residual analysis of the results of a linear regression model.
- LMResiduals(LMProblem, Vector) - Constructor for class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Performs residual analysis for a linear regression problem.
- loading(int) - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the loading vector of the i-th principal component.
- loading(int) - Method in class dev.nm.stat.factor.pca.PCAbyEigen
-
- loadings() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
-
Gets the rotated loading matrix.
- loadings() - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the matrix of variable loadings.
- loadings() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
-
- loadings() - Method in class dev.nm.stat.factor.pca.PCAbySVD
-
- LocalSearchCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell
-
- LocalSearchCellFactory<P extends OptimProblem,T extends IterativeMinimizer<OptimProblem>> - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
-
- LocalSearchCellFactory(LocalSearchCellFactory.MinimizerFactory<T>, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory
-
Construct an instance of a LocalSearchCellFactory
.
- LocalSearchCellFactory.LocalSearchCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
-
A LocalSearchCell
implements the two genetic operations.
- LocalSearchCellFactory.MinimizerFactory<U extends IterativeMinimizer<OptimProblem>> - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
-
This factory constructs a new Minimizer
for each mutation operation.
- log(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the log of a vector, element-by-element.
- log(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute log(x).
- log(BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute log(x) up to a scale.
- log(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Natural logarithm of a complex number.
- log(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the logs of values.
- logAcceptanceRatio(RealScalarFunction, Vector, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisUtils
-
Computes the log of the acceptance ratio.
- LogBeta - Class in dev.nm.analysis.function.special.beta
-
This class represents the log of Beta function log(B(x, y))
.
- LogBeta() - Constructor for class dev.nm.analysis.function.special.beta.LogBeta
-
- logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
- logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
- logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
-
- logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
- logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
- logDensity(double) - Method in interface dev.nm.stat.evt.evd.univariate.UnivariateEVD
-
Get the logarithm of the probability density function at \(x\), that is, \(\log(f(x))\).
- logGamma(double) - Method in class dev.nm.analysis.function.special.gamma.Lanczos
-
Compute log-gamma for a positive value x.
- logGamma(BigDecimal) - Method in class dev.nm.analysis.function.special.gamma.Lanczos
-
Compute log-gamma for a positive value x to arbitrary precision.
- LogGamma - Class in dev.nm.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 dev.nm.analysis.function.special.gamma.LogGamma
-
Construct an instance of log-Gamma.
- LogGamma() - Constructor for class dev.nm.analysis.function.special.gamma.LogGamma
-
Construct an instance of log-Gamma.
- LogGamma.Method - Enum in dev.nm.analysis.function.special.gamma
-
the available methods to compute \(\log (\Gamma(z))\)
- logGammaQuick(double) - Method in class dev.nm.analysis.function.special.gamma.Lanczos
-
Compute log-gamma for a positive value x.
- LogisticBeta - Class in dev.nm.stat.regression.linear.logistic
-
Beta coefficient estimator, β^, of a logistic regression model.
- LogisticBeta(Vector, LogisticResiduals) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticBeta
-
Construct an instance of Beta
.
- LogisticProblem - Class in dev.nm.stat.regression.linear.logistic
-
A logistic regression problem is a variation of the OLS regression problem.
- LogisticProblem(LMProblem) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticProblem
-
Constructs a logistic regression problem from a linear regression problem.
- LogisticRegression - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.logistic.LogisticRegression
-
Constructs a Logistic instance.
- LogisticRegression(LMProblem) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticRegression
-
Constructs a Logistic instance.
- LogisticResiduals - Class in dev.nm.stat.regression.linear.logistic
-
Residual analysis of the results of a logistic regression.
- logLikelihood() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Compute the log-likelihood at the fitted value.
- logLikelihood() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
-
Gets the log-likelihood value.
- logLikelihood() - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
-
Gets the likelihood of the given observations.
- logLikelihood - Variable in class dev.nm.stat.hmm.mixture.MixtureHMMEM.TrainedModel
-
the log-likelihood
- logLikelihood() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
-
- logLikelihood() - Method in class dev.nm.stat.regression.linear.glm.IWLS
-
- logLikelihood() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
- logLikelihood(LogisticProblem) - Static method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
-
Constructs the log-likelihood function for a logistic regression problem.
- logMu - Variable in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda
-
the log-mean μ ∈ R
- LogNormalDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.LogNormalDistribution
-
Construct a log-normal distribution.
- LogNormalMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
-
The HMM states use the Log-Normal distribution to model the observations.
- LogNormalMixtureDistribution(LogNormalMixtureDistribution.Lambda[], boolean, boolean) - Constructor for class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
-
Constructs a log-normal distribution for each state in the HMM model.
- LogNormalMixtureDistribution(LogNormalMixtureDistribution.Lambda[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
-
Constructs a log-normal distribution for each state in the HMM model.
- LogNormalMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
-
the log-normal distribution parameters
- LogNormalRNG - Class in dev.nm.stat.random.rng.univariate
-
This random number generator samples from the log-normal distribution.
- LogNormalRNG(NormalRNG) - Constructor for class dev.nm.stat.random.rng.univariate.LogNormalRNG
-
Construct a random number generator to sample from the log-normal distribution.
- LogNormalRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.LogNormalRNG
-
Construct a random number generator to sample from the log-normal distribution.
- logProbability(int[], double[]) - Method in class dev.nm.stat.hmm.HiddenMarkovModel
-
Gets the probability of observing the observations and having gone thru
the state sequence.
- logProbability(int[], int[]) - Method in class dev.nm.stat.hmm.HiddenMarkovModel
-
Gets the probability of observing the observations and having gone thru
the state sequence.
- logProbability(HmmInnovation[]) - Method in class dev.nm.stat.hmm.HiddenMarkovModel
-
Gets the probability of observing the observations and having gone thru
the state sequence.
- logSigma - Variable in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda
-
the log-standard deviation; shape
- longValue() - Method in class dev.nm.number.complex.Complex
-
- longValue() - Method in class dev.nm.number.Real
-
- longValue() - Method in class dev.nm.number.ScientificNotation
-
- LoopBody - Interface in dev.nm.misc.parallel
-
The implementation of this interface contains the code inside a for-loop
construct.
- lower() - Method in class dev.nm.interval.RealInterval
-
Get the lower bound of this interval.
- lower() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
- lower - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- lowerBound() - Method in class dev.nm.analysis.function.polynomial.CauchyPolynomial
-
Cauchy's lower bound on polynomial zeros is the unique positive root of the Cauchy polynomial.
- lowerBound() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
Gets the lower bounds.
- lowerBoundConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
Split the box constraints and get the greater-than-the-lower-bounds part.
- LowerBoundConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
-
This is a lower bound constraints such that for all xi's,
xi ≥ b
- LowerBoundConstraints(RealScalarFunction, double) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LowerBoundConstraints
-
Construct a lower bound constraints for all variables in a function.
- lowerBounds() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
Gets the lower bounds.
- LowerTriangularMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle
-
A lower triangular matrix has 0 entries where column index > row index.
- LowerTriangularMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Constructs a lower triangular matrix of dimension dim * dim.
- LowerTriangularMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Constructs a lower triangular matrix from a 2D double[][]
array.
- LowerTriangularMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Constructs a lower triangular matrix from a matrix.
- LowerTriangularMatrix(LowerTriangularMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Copy constructor.
- LPBoundedMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
-
This is the solution to a bounded linear programming problem.
- LPBoundedMinimizer(SimplexTable) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
Constructs the solution for a bounded linear programming problem.
- LPCanonicalProblem1 - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
-
Construct a linear programming problem in the canonical form.
- LPCanonicalProblem1(Vector, LinearGreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
-
Construct a linear programming problem in the canonical form.
- LPCanonicalProblem1(LPCanonicalProblem2) - Constructor for class dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2
-
Construct a linear programming problem in the canonical form.
- LPCanonicalProblem2(Vector, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2
-
Construct a linear programming problem in the canonical form.
- LPCanonicalProblem2(LPCanonicalProblem1) - Constructor for class dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPCanonicalSolver
-
- LPDimensionNotMatched - Exception in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPDimensionNotMatched
-
Construct an instance of LPDimensionNotMatched
.
- LPEmptyCostVector - Exception in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPEmptyCostVector
-
- LPException - Exception in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPException
-
Construct an instance of LPException
.
- LPInfeasible - Exception in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPInfeasible
-
- LPMinimizer - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp
-
An LP minimizer minimizes the objective of an LP problem, satisfying all the constraints.
- LPNoConstraint - Exception in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPNoConstraint
-
- LPProblem - Interface in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Construct a general linear programming problem.
- LPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearEqualityConstraints) - Constructor for class dev.nm.solver.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.
- LPRevisedSimplexSolver - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
- LPRevisedSimplexSolver(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
-
- LPRevisedSimplexSolver.Problem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
-
- LPRuntimeException - Exception in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException
-
Construct an instance of LPRuntimeException
.
- LPRuntimeException(String) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException
-
Construct an instance of LPRuntimeException
.
- LPSimplexMinimizer - Interface in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp
-
An LP solver solves a Linear Programming (LP) problem.
- LPStandardProblem - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPStandardProblem
-
Construct a linear programming problem in the standard form.
- LPTwoPhaseSolver - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
Construct an LP solver to solve LP problems.
- LPTwoPhaseSolver() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
Construct an LP solver to solve LP problems.
- LPUnbounded - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
-
This is the exception thrown when the LP problem is unbounded.
- LPUnbounded(int) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPUnbounded
-
Construct an instance of LPUnbounded
.
- LPUnboundedMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
Construct the solution for an unbounded linear programming problem.
- LPUnboundedMinimizerScheme2 - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.algebra.linear.matrix.doubles.linearsystem
-
This is the problem of solving a system of linear equations.
- LSProblem(Matrix, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Constructs a system of linear equations Ax = b.
- Lt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
-
Gets the transpose of the lower triangular matrix, L'.
- Lt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
-
Get the transpose of L as in the LDL decomposition.
- Lt() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Gets the transpose of the lower triangular matrix L in the LDL decomposition.
- LU - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
-
Run the LU decomposition on a square matrix.
- LU(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
-
Run the LU decomposition on a square matrix.
- LUDecomposition - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.triangle
-
LU decomposition decomposes an n x n matrix A so that P * A = L * U.
- LUSolver - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
-
Use LU decomposition to solve Ax = b where A is square and
det(A) != 0.
- LUSolver() - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver
-
- m() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
-
Gets the number of interior x-axis grid points in the solution.
- M() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
-
Gets the number of interior time-axis grid points in the solution.
- m() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the number of interior time-axis grid points in the solution.
- m() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets the dimension of the system, i.e., m = the dimension of y.
- m() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Gets the number of covariates (number of columns of X), excluding
the intercept.
- m0() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
-
Get the the mean of x0.
- m0() - Method in class dev.nm.stat.dlm.univariate.DLM
-
Get the the mean of x0.
- MA() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
-
Get the MA coefficients.
- MA(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the i-th MA coefficient; MA(0) = 1.
- MA(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the i-th MA coefficient; MA(0) = 1.
- MACH_EPS - Static variable in class dev.nm.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 dev.nm.misc.Constants
-
the scale for the machine epsilon
- MADecomposition - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.misc.PhysicalConstants
-
The magnetic flux quantum \(\Phi_0\) in webers (Wb).
- MAGNETIC_MU0 - Static variable in class dev.nm.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 dev.nm.misc.license.FloatingLicenseServer
-
- MAModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
-
This class represents a univariate MA model.
- MAModel(double, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model.
- MAModel(double, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model with unit variance.
- MAModel(double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model with zero-mean.
- MAModel(double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model with unit variance and zero-mean.
- MAModel(MAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Copy constructor.
- mapClusterIndices(List<List<Integer>>) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
-
- marginalInverseTransform(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
Inverse of marginal transform.
- marginalTransform(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
Transform to exponential margins under the GEV model.
- MarketImpact1 - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
-
Constructs a market impact term.
- MarkowitzByCLM - Class in tech.nmfin.portfoliooptimization.markowitz
-
Solves for the optimal weights in the Markowitz formulation by critical line
method.
- MarkowitzByCLM(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM
-
Solves w_eff = argmin {q * (w' V w) - w'r}, w'1 = 1, w ≥ 0.
- MarkowitzByCLM(Vector, Matrix, Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM
-
Solves w_eff = argmin {q * (w' V w) - w'r}, w'1 = 1, w ≥ w_lower, w
≤ w_upper.
- MarkowitzByCLM(Vector, Matrix, Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM
-
Constructs a Markowitz portfolio from expected future returns
and future covariance for a given benchmark rate, with lower and
upper limits on asset weights.
- MarkowitzByQP - Class in tech.nmfin.portfoliooptimization.markowitz
-
Modern portfolio theory (MPT) is a theory of investment which attempts to
maximize portfolio expected return for a given amount of portfolio risk, or
equivalently minimize risk for a given level of expected return, by carefully
choosing the proportions of various assets.
- MarkowitzByQP(Vector, Matrix, QPConstraint) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Constructs a Markowitz portfolio from expected future returns
and future covariance, assuming zero benchmark rate for Sharpe
ratio calculation.
- MarkowitzByQP(Vector, Matrix, QPConstraint, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Constructs a Markowitz portfolio from expected future returns
and future covariance.
- MarkowitzByQP(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Constructs a Markowitz portfolio from expected future returns
and future covariance, assuming no short selling constraint and
zero benchmark rate.
- MarkowitzByQP(Vector, Matrix, Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Constructs a Markowitz portfolio from expected future returns
and future covariance, with lower and upper limits on asset
weights, assuming zero benchmark rate.
- MarkowitzByQP(Vector, Matrix, Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Constructs a Markowitz portfolio from expected future returns
and future covariance for a given benchmark rate, with lower and
upper limits on asset weights.
- MarkowitzCriticalLine - Interface in tech.nmfin.portfoliooptimization.clm
-
- MARMAModel - Class in dev.nm.stat.evt.timeseries
-
Simulation of max autoregressive moving average processes, i.e., MARMA(p, q) processes.
- MARMAModel(UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MARMAModel
-
Create an instance with a given GEV distribution for generating innovations.
- MARMAModel(double[], double[]) - Constructor for class dev.nm.stat.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 dev.nm.stat.evt.timeseries.MARMAModel
-
Create an instance with the AR and MA coefficients, and a GEV distribution for generating
innovations.
- MARMASim - Class in dev.nm.stat.evt.timeseries
-
Generate random numbers based on a given MARMA model.
- MARMASim(MARMAModel) - Constructor for class dev.nm.stat.evt.timeseries.MARMASim
-
- MARMASim(MARMAModel, RandomNumberGenerator) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.evt.timeseries
-
This is equivalent to MARMA(p, 0).
- MARModel(double[]) - Constructor for class dev.nm.stat.evt.timeseries.MARModel
-
Create an instance with the AR coefficients, using
FrechetDistribution
as the GEV
distribution.
- MARModel(double[], UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MARModel
-
Create an instance with the AR coefficients, and a GEV distribution for generating
innovations.
- MarsagliaBray1964 - Class in dev.nm.stat.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 dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964
-
Construct a random number generator to sample from the standard Normal distribution.
- MarsagliaBray1964() - Constructor for class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964
-
Construct a random number generator to sample from the standard Normal distribution.
- MarsagliaTsang2000 - Class in dev.nm.stat.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 dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the gamma distribution.
- MarsagliaTsang2000(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the gamma distribution.
- MarsagliaTsang2000() - Constructor for class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the standard gamma distribution.
- Mass(X, double) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassFunction.Mass
-
Creates an instance with an outcome and its associated probability.
- MAT - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
MAT
is the inverse operator of
SVEC
.
- MAT(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.MAT
-
Constructs the MAT of a vector.
- MathTable - Class in dev.nm.misc.datastructure
-
A mathematical table consists of numbers showing the results of calculation with varying
arguments.
- MathTable(String...) - Constructor for class dev.nm.misc.datastructure.MathTable
-
Constructs an empty table by headers.
- MathTable(int) - Constructor for class dev.nm.misc.datastructure.MathTable
-
Constructs an empty table.
- MathTable.Row - Class in dev.nm.misc.datastructure
-
A row is indexed by a number and contains multiple values.
- Matrix - Interface in dev.nm.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 dev.nm.algebra.linear.matrix.doubles
-
This interface defines the methods for accessing entries in a matrix.
- MatrixAccessException - Exception in dev.nm.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 dev.nm.algebra.linear.matrix.MatrixAccessException
-
Construct an instance of MatrixAccessException
.
- MatrixAccessException(String) - Constructor for exception dev.nm.algebra.linear.matrix.MatrixAccessException
-
Construct an instance of MatrixAccessException
with a message.
- MatrixCoordinate - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
Construct a matrix coordinate specifying an entry location.
- MatrixFactory - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
These are the utility functions to create a new matrix/vector from existing ones.
- MatrixMathOperation - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation
-
This interface defines some standard operations for generic matrices.
- MatrixMeasure - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
- MatrixMismatchException - Exception in dev.nm.algebra.linear.matrix
-
This is the runtime exception thrown when an operation acts on matrices that have incompatible dimensions.
- MatrixMismatchException() - Constructor for exception dev.nm.algebra.linear.matrix.MatrixMismatchException
-
Construct an instance of MatrixMismatchException
.
- MatrixMismatchException(String) - Constructor for exception dev.nm.algebra.linear.matrix.MatrixMismatchException
-
Construct an instance of MatrixMismatchException
with a message.
- MatrixPropertyUtils - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles
-
A matrix ring is the set of all
n × n matrices over an arbitrary
Ring
R.
- MatrixRootByDiagonalization - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixRootByDiagonalization
-
Constructs the square root of a Matrix by diagonalization.
- MatrixSingularityException - Exception in dev.nm.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 dev.nm.algebra.linear.matrix.MatrixSingularityException
-
Construct an instance of MatrixSingularityException
.
- MatrixSingularityException(String) - Constructor for exception dev.nm.algebra.linear.matrix.MatrixSingularityException
-
Construct an instance of MatrixSingularityException
with a message.
- MatrixTable - Interface in dev.nm.algebra.linear.matrix.doubles
-
A matrix is represented by a rectangular table structure with accessors.
- MatrixUtils - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
These are the utility functions to apply to matrices.
- MatthewsDavies - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
-
Constructs a symmetric, positive definite matrix using the Matthews-Davies algorithm.
- max(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the maximal entry in a matrix.
- max(DateTime, DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Return the later of two DateTime
instances.
- max(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the maximum of the values.
- max(int...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the maximum of the values.
- Max - Class in dev.nm.stat.descriptive.rank
-
The maximum of a sample is the biggest value in the sample.
- Max() - Constructor for class dev.nm.stat.descriptive.rank.Max
-
Construct an empty Max
calculator.
- Max(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Max
-
Construct a Max
calculator,
initialized with a sample.
- Max(Max) - Constructor for class dev.nm.stat.descriptive.rank.Max
-
Copy constructor.
- max_abs_cor() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the estimated sequence of maximal absolute correlations.
- MAX_D - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
- MAX_ITERATION - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
- MAX_P - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
- MAX_Q - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
- maxBinSize() - Method in class dev.nm.misc.algorithm.Bins
-
Gets the maximal size of the bins.
- maxClusterSize() - Method in class dev.nm.graph.community.GirvanNewman
-
Get the size of the maximal cluster.
- maxDomain() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
-
Get the biggest abscissae.
- maxEdge() - Method in class dev.nm.graph.community.EdgeBetweeness
-
Gets the edge with the maximal edge-betweeness.
- maxEdge() - Method in class dev.nm.graph.community.GirvanNewman
-
Gets the edge with the maximal edge-betweeness.
- MaximaDistribution - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.solver
-
This is the solution to a maximization problem.
- maximizer() - Method in interface dev.nm.solver.MaximizationSolution
-
Get the maximizer (solution) to the maximization problem.
- maximum() - Method in interface dev.nm.solver.MaximizationSolution
-
Get the (approximate) maximum found.
- MaximumLikelihoodFitting - Interface in dev.nm.stat.evt.evd.univariate.fitting
-
This interface defines model fitting by maximum likelihood algorithm.
- maxIndex(boolean, int, int, double...) - Static method in class dev.nm.number.DoubleUtils
-
Get the index of the maximum of the values, skipping
Double.NaN
.
- maxIndex(double...) - Static method in class dev.nm.number.DoubleUtils
-
Get the index of the maximum of the values, skipping
Double.NaN
.
- maxIterations - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
- maxIterations - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- maxIterations - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
the maximum number of iterations
- maxIterations - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer
-
the maximum number of iterations
- MaxIterationsExceededException(int) - Constructor for exception dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.MaxIterationsExceededException
-
Construct a new MaxIterationsExceededException
, indicating the number of iterations.
- Maxmizer<P extends OptimProblem,S extends MaximizationSolution<?>> - Interface in dev.nm.solver
-
This interface represents an optimization algorithm that maximizers a real valued objective
function, one or multi dimension.
- maxOrder() - Method in class dev.nm.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 dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the maximum of AR length or MA length.
- maxPQ() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the maximum of AR length or MA length.
- maxPQ() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the maximum of the ARCH length or GARCH length.
- maxValue() - Method in class dev.nm.graph.community.EdgeBetweeness
-
Gets the maximum of edge-betweeness-es.
- maxValue() - Method in class dev.nm.graph.community.GirvanNewman
-
Get the maximum of edge-betweeness.
- maxZ() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
-
- McCormickMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
-
- McCormickMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.McCormickMinimizer
-
- MCLNiedermayer - Class in tech.nmfin.portfoliooptimization.clm
-
Implements Markowitz's critical line algorithm.
- MCLNiedermayer(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
-
Creates the critical line for given gain vector and covariance matrix,
with non-negativity constraint.
- MCLNiedermayer(Vector, Matrix, Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
-
Creates the critical line for given gain vector and covariance matrix,
with given lower and upper bounds for weights.
- MCUtils - Class in dev.nm.stat.markovchain
-
These are the utility functions to examine a Markov chain.
- mean() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
-
Get the sample mean.
- Mean - Class in dev.nm.stat.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 dev.nm.stat.descriptive.moment.Mean
-
Construct an empty Mean
calculator.
- Mean(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Mean
-
Construct a Mean
calculator,
initialized with a sample.
- Mean(Mean) - Constructor for class dev.nm.stat.descriptive.moment.Mean
-
Copy constructor.
- mean() - Method in class dev.nm.stat.descriptive.moment.Skewness
-
Get the sample mean.
- mean() - Method in class dev.nm.stat.descriptive.moment.Variance
-
Get the sample mean.
- mean() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
-
- mean() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
-
- mean() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
- mean() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the mean of this distribution.
- mean() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
Gets the mean of this distribution.
- mean() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
-
- mean() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
Gets the mean of this distribution.
- mean() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.TDistribution
-
Gets the mean of this distribution.
- mean() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
- mean() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
-
- mean() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
-
- mean() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
- mean() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
\[
\mu + \frac{\sigma}{1-\xi}
\]
for \(\xi < 1\).
- mean() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
-
- mean() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
- mean() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
- mean() - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the sample means that were subtracted.
- mean() - Method in class dev.nm.stat.factor.pca.PCAbySVD
-
- mean() - Method in interface dev.nm.stat.random.Estimator
-
Gets the expectation of the estimator.
- mean() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation
-
Compute the mean of the integral.
- mean() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- mean() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- mean() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- mean() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- mean() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- mean() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- mean() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
Computes the sample mean of the in-sample spread.
- mean() - Method in interface tech.nmfin.meanreversion.volarb.MeanEstimator
-
- mean() - Method in class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
-
- mean1() - Method in class dev.nm.stat.test.mean.T
-
Get the mean of the first sample.
- mean2() - Method in class dev.nm.stat.test.mean.T
-
Get the mean of the second sample.
- MeanEstimator - Interface in dev.nm.stat.random
-
- MeanEstimator - Interface in tech.nmfin.meanreversion.volarb
-
Defines how to estimate the mean price.
- MeanEstimatorMaxLevelShift - Class in tech.nmfin.meanreversion.volarb
-
- MeanEstimatorMaxLevelShift(int, double, double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
-
- MeanEstimatorMaxLevelShift(int, double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
-
- median() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
-
Gets the median of this distribution.
- median() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
-
- median() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
Gets the median of this distribution.
- median() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.TDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
- median() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
-
- median() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
- median() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
\[
\mu + \frac{\sigma( 2^{\xi} -1)}{\xi}
\]
- median() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
-
- median() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
- median() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
- median() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- median() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- median() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- median() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- median() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Deprecated.
- median() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Deprecated.
- mergeIntervals(RealInterval[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenBoundUtils
-
- MersenneExponent - Enum in dev.nm.stat.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 dev.nm.stat.random.rng.univariate.uniform.mersennetwister
-
Mersenne Twister is one of the best pseudo random number generators
available.
- MersenneTwister() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
Constructs a random number generator to sample uniformly from [0, 1).
- MersenneTwister(long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
Constructs a random number generator to sample uniformly from [0, 1).
- MersenneTwister(MersenneTwisterParam) - Constructor for class dev.nm.stat.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 dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
- MersenneTwisterParam - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister
-
- MersenneTwisterParam() - Constructor for class dev.nm.stat.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 dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
-
- MersenneTwisterParamSearcher - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation
-
Searches for Mersenne-Twister parameters.
- MersenneTwisterParamSearcher(RandomLongGenerator) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.metropolis
-
This basic Metropolis implementation assumes using symmetric proposal function.
- Metropolis(RealScalarFunction, RealVectorFunction, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis
-
Constructs a new instance with the given parameters.
- Metropolis(RealScalarFunction, Vector, double, RandomLongGenerator) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
-
Uses the classic Metropolis rule, f_{t+1}/f_t.
- MetropolisAcceptanceProbabilityFunction() - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.MetropolisAcceptanceProbabilityFunction
-
- MetropolisHastings - Class in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings
-
Constructs a new instance with the given parameters.
- MetropolisHastings.ProposalDensityFunction - Interface in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
-
Defines the density of a proposal function, i.e.
- MetropolisUtils - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
-
Utility functions for Metropolis algorithms.
- Midpoint - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.newtoncotes.Midpoint
-
Construct an integrator that implements the Midpoint rule.
- MilsteinSDE - Class in dev.nm.stat.stochasticprocess.univariate.sde.discrete
-
Milstein scheme is a first-order approximation to a continuous-time SDE.
- MilsteinSDE(SDE) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE
-
Discretize a continuous-time SDE using the Milstein scheme.
- min(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the minimal entry in a matrix.
- min(DateTime, DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Return the earlier of two DateTime
instances.
- min(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the minimum of the values.
- min() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution
-
- Min - Class in dev.nm.stat.descriptive.rank
-
The minimum of a sample is the smallest value in the sample.
- Min() - Constructor for class dev.nm.stat.descriptive.rank.Min
-
Construct an empty Min
calculator.
- Min(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Min
-
Construct a Min
calculator,
initialized with a sample.
- Min(Min) - Constructor for class dev.nm.stat.descriptive.rank.Min
-
Copy constructor.
- MIN_P - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
- MIN_Q - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
- minDomain() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
-
Get the smallest abscissae.
- MinimaDistribution - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
- MinimalResidualSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
Construct a MINRES solver.
- MinimalResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
Construct a MINRES solver.
- MinimizationSolution<T> - Interface in dev.nm.solver
-
This is the solution to a minimization problem.
- minimizer() - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
-
- minimizer() - Method in interface dev.nm.solver.MinimizationSolution
-
Get the minimizer (solution) to the minimization problem.
- Minimizer<P extends OptimProblem,S extends MinimizationSolution<?>> - Interface in dev.nm.solver
-
This interface represents an optimization algorithm that minimizes a real valued objective
function, one or multi dimension.
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- minimizer() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
-
- minimizer() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSolution
-
Get a minimizing vector.
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- minimizer() - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- minimizer() - Method in class dev.nm.solver.univariate.GridSearchMinimizer.Solution
-
- minimizers() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
Get all optimal minimizers.
- minimum() - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
-
- minimum() - Method in interface dev.nm.solver.MinimizationSolution
-
Get the (approximate) minimum found.
- minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
Get the (approximate) minimum found.
- minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
- minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution
-
Deprecated.
- minimum() - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- minimum() - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- minimum() - Method in class dev.nm.solver.univariate.GridSearchMinimizer.Solution
-
- MinimumWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.constraint
-
This constraint puts lower bounds on weights.
- MinimumWeights(Vector) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.constraint.MinimumWeights
-
- minIndex(boolean, int, int, double...) - Static method in class dev.nm.number.DoubleUtils
-
Get the index of the minimum of the values, skipping
Double.NaN
.
- minIndex(double...) - Static method in class dev.nm.number.DoubleUtils
-
Get the index of the minimum of the values, skipping
Double.NaN
.
- MinMaxMinimizer<T> - Interface in dev.nm.solver.multivariate.minmax
-
A minmax minimizer minimizes a minmax problem.
- MinMaxProblem<T> - Interface in dev.nm.solver.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- minus(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
-
this - that
- minus(DenseData) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Subtract the elements in this
by that
, element-by-element.
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Computes the difference between two diagonal matrices.
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- minus(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- minus(MatrixAccess, MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A1 - A2
- minus(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- minus(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- minus(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- minus(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- minus(ComplexMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- minus(GenericFieldMatrix<F>) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- minus(RealMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- minus(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- minus(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- minus(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
A vector subtracts another vector, element-by-element.
- minus(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Subtracts a constant from a vector, element-by-element.
- minus(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- minus(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
\(this - that\)
- minus(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Subtract a constant from all entries in this vector.
- minus(G) - Method in interface dev.nm.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 dev.nm.analysis.function.polynomial.Polynomial
-
- minus(Complex) - Method in class dev.nm.number.complex.Complex
-
- minus(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
-
- minus(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
-
Subtract one double
array from another, entry-by-entry.
- minus(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
-
- minus(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
-
- minus(Real) - Method in class dev.nm.number.Real
-
- minusWeekdayPeriod(DateTime, Period) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Subtract a weekday-period (i.e., skipping weekends) from a
DateTime
.
- minZ() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
-
- MixedRule - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.substitution.MixedRule
-
Construct a MixedRule
substitution rule.
- MixtureDistribution - Interface in dev.nm.stat.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 dev.nm.stat.hmm.mixture
-
This is the mixture hidden Markov model (HMM).
- MixtureHMM(Vector, Matrix, MixtureDistribution) - Constructor for class dev.nm.stat.hmm.mixture.MixtureHMM
-
Constructs a mixture hidden Markov model.
- MixtureHMM(MixtureHMM) - Constructor for class dev.nm.stat.hmm.mixture.MixtureHMM
-
Copy constructor.
- MixtureHMMEM - Class in dev.nm.stat.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 dev.nm.stat.hmm.mixture.MixtureHMMEM
-
Constructs a mixture HMM model by training an initial model using the
Baum-Welch algorithm.
- MixtureHMMEM.TrainedModel - Class in dev.nm.stat.hmm.mixture
-
the result of the EM algorithm
- ML() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
-
Gets the maximum log-likelihood.
- MMAModel - Class in dev.nm.stat.evt.timeseries
-
This is equivalent to MARMA(0, q).
- MMAModel(double[]) - Constructor for class dev.nm.stat.evt.timeseries.MMAModel
-
Create an instance with the MA coefficients, using
FrechetDistribution
as the GEV
distribution.
- MMAModel(double[], UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MMAModel
-
Create an instance with the MA coefficients, and a GEV distribution for generating
innovations.
- mod(long, long) - Static method in class dev.nm.analysis.function.FunctionOps
-
Compute the positive modulus of a number.
- mode() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
-
- mode() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
-
- mode() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
- mode() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
-
Gets the mode of this distribution.
- mode() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
- mode() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
-
- model - Variable in class dev.nm.stat.hmm.mixture.MixtureHMMEM.TrainedModel
-
the newly trained model as a result of the EM algorithm
- ModelNotFound(String) - Constructor for exception dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection.ModelNotFound
-
Construct a ModelNotFound
exception with an error message.
- ModelParam(double, double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
-
- ModelParam(double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
-
- ModelResamplerFactory - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
-
- ModelResamplerFactory(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
-
- ModelResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
-
- modpow(long, long, long) - Static method in class dev.nm.analysis.function.FunctionOps
-
be mod m
- modulus() - Method in class dev.nm.number.complex.Complex
-
Get the modulus.
- modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
-
- modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
-
- modulus() - Method in interface dev.nm.stat.random.rng.univariate.uniform.linear.LinearCongruentialGenerator
-
Get the modulus of this linear congruential generator.
- modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
-
- MOLAR_GAS_R - Static variable in class dev.nm.misc.PhysicalConstants
-
The molar gas constant \(R\) in joule per kelvin mole (J mol-1 K-1).
- moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
-
- moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
-
- moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
- moment(Vector) - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
-
The moment generating function is the expected value of etX.
- moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
-
- moment(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
The moment generating function is the expected value of etX.
- moment(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
- moment(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
-
- moment(Vector) - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
-
- moment(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
- moment(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
- moment(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
-
- moment(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
- moment(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
- moment(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- moment(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- moment(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- moment(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- moment(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Deprecated.
- moment(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Deprecated.
- moment1() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
-
- moment2() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
-
- Moments - Class in dev.nm.stat.descriptive.moment
-
Compute the central moment of a data set incrementally.
- Moments(int) - Constructor for class dev.nm.stat.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 dev.nm.stat.descriptive.moment.Moments
-
Construct a moment calculator, computing all moments up to and including the order
-th moment.
- Moments(Moments) - Constructor for class dev.nm.stat.descriptive.moment.Moments
-
Copy constructor.
- moments() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
-
- MomentsEstimatorLedoitWolf - Class in tech.nmfin.returns.moments
-
- MomentsEstimatorLedoitWolf() - Constructor for class tech.nmfin.returns.moments.MomentsEstimatorLedoitWolf
-
- Monoid<G> - Interface in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Swaps a column of a permutation matrix with the last column.
- moveRow2End(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Swaps a row of the permutation matrix with the last row.
- MovingAverage - Class in dev.nm.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 dev.nm.dsp.univariate.operation.system.doubles.MovingAverage
-
Construct a moving average filter.
- MovingAverage(double[]) - Constructor for class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage
-
Construct a moving average filter using a symmetric window.
- MovingAverage.Side - Enum in dev.nm.dsp.univariate.operation.system.doubles
-
the available types of moving average filtering
- MovingAverageByExtension - Class in dev.nm.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 dev.nm.dsp.univariate.operation.system.doubles.MovingAverageByExtension
-
Construct a moving average filter with prepending and appending.
- MR3 - Class in dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.stat.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 dev.nm.stat.random.rng.univariate.uniform.linear.MRG
-
Construct a Multiple Recursive Generator.
- MRModel - Interface in tech.nmfin.meanreversion.volarb
-
A Mean Reversion Model computes the target position given the current price.
- MRModelRanged - Class in tech.nmfin.meanreversion.volarb
-
- MRModelRanged(double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MRModelRanged
-
- MRModelRanged(double) - Constructor for class tech.nmfin.meanreversion.volarb.MRModelRanged
-
- mu(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
-
Gets the convection coefficient at a given time t and a position
x.
- mu - Variable in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
-
the mean
- mu() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
-
Gets μ as in
E(Y) = μ = g-1(Xβ)
- mu() - Method in class dev.nm.stat.regression.linear.glm.IWLS
-
- mu() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
- mu() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get the drift: \(\mu(t,X_t,Z_t,...)\).
- mu() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
-
- mu() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUProcess
-
Get the overall mean.
- mu() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the intercept (constant) vector.
- mu() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the intercept vector.
- mu() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the intercept (constant) term.
- mu() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets the long term mean.
- mu() - Method in class tech.nmfin.portfoliooptimization.clm.TurningPoint
-
- mu() - Method in class tech.nmfin.returns.moments.ReturnsMoments
-
Gets the mean vector.
- mu - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
- mu1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
-
- mu1() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
Gets the drift in the bull market.
- mu2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
-
- mu2() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
Gets the drift in the bear market.
- MultiCubicSpline - Class in dev.nm.analysis.curvefit.interpolation.multivariate
-
Implementation of natural cubic spline interpolation for an arbitrary number of dimensions.
- MultiCubicSpline() - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultiCubicSpline
-
Create an instance with
CubicSpline
as the implementation of the univariate cubic
spline interpolation algorithm.
- MultiDimensionalArray<T> - Class in dev.nm.misc.datastructure
-
A generic multi-dimensional array, with an arbitrary number of dimensions.
- MultiDimensionalArray(int...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalArray
-
Creates an instance with the specified size along each dimension.
- MultiDimensionalArray(MultiDimensionalCollection<T>) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalArray
-
A copy constructor that constructs a shallow copy of the given collection of instances.
- MultiDimensionalCollection<T> - Interface in dev.nm.misc.datastructure
-
A generic collection with an arbitrary number of dimensions.
- MultiDimensionalGrid - Class in dev.nm.misc.datastructure
-
An arbitrary dimensional grid.
- MultiDimensionalGrid(Double[]...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid
-
Constructs a multi-dimensional grid of points.
- MultiDimensionalGrid(double[]...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid
-
Constructs a multi-dimensional grid of points.
- MultiDimensionalGrid(MultiDimensionalGrid.Discretization...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid
-
Constructs a multi-dimensional grid of points.
- MultiDimensionalGrid.Discretization - Class in dev.nm.misc.datastructure
-
Specifies the discretization of an interval.
- MultiLinearInterpolation - Class in dev.nm.analysis.curvefit.interpolation.multivariate
-
Implementation of linear interpolation for an arbitrary number of dimensions.
- MultiLinearInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultiLinearInterpolation
-
Create an instance with
LinearInterpolation
as the implementation of the univariate
linear interpolation algorithm.
- MultinomialBetaFunction - Class in dev.nm.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 dev.nm.analysis.function.special.beta.MultinomialBetaFunction
-
Constructs an instance of a multinomial Beta function.
- MultinomialDistribution - Class in dev.nm.stat.distribution.multivariate
-
- MultinomialDistribution(int, double...) - Constructor for class dev.nm.stat.distribution.multivariate.MultinomialDistribution
-
Constructs an instance of a Multinomial distribution.
- MultinomialRVG - Class in dev.nm.stat.random.rng.multivariate
-
A multinomial distribution puts N objects into K bins according to the bins'
probabilities.
- MultinomialRVG(int, double[], RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.MultinomialRVG
-
Constructs a multinomial random vector generator.
- MultinomialRVG(int, double[]) - Constructor for class dev.nm.stat.random.rng.multivariate.MultinomialRVG
-
Constructs a multinomial random vector generator.
- MultipleExecutionException - Exception in dev.nm.misc.parallel
-
This exception is thrown when any of the parallel tasks throws an exception during execution.
- MultipleExecutionException(List<?>, List<ExecutionException>) - Constructor for exception dev.nm.misc.parallel.MultipleExecutionException
-
Construct an exception with the (partial) results and all exceptions encountered during
execution.
- MultiplicativeModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel
-
Construct a univariate time series by multiplying the components.
- MultiplicativeModel(double[], double[], RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel
-
Construct a univariate time series by multiplying the components.
- MultiplierPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
-
A multiplier penalty function allows different weights to be assigned to the constraints.
- MultiplierPenalty(Constraints, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- MultiplierPenalty(Constraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- MultiplierPenalty(Constraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- multiply(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
-
Right multiply this matrix, A, by a vector.
- multiply(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
-
this * that
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
this * that
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
Computes the product of two diagonal matrices.
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Left multiplication by G, namely, G * A.
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- multiply(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- multiply(MatrixAccess, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- multiply(MatrixAccess, MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A1 * A2
- multiply(MatrixAccess, Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A * v
- multiply(double[], double[], double[], int, int, int) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplication
-
Multiplies two matrices, C = A %*% B.
- multiply(double[], double[], double[], int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock
-
- multiply(double[], double[], double[], int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByIjk
-
- multiply(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- multiply(MatrixAccess, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Left multiplication by P.
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Left multiplication by P.
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- multiply(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- multiply(ComplexMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- multiply(GenericFieldMatrix<F>) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- multiply(RealMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- multiply(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- multiply(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- multiply(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Multiplies two vectors, element-by-element.
- multiply(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- multiply(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Multiply this
by that
, entry-by-entry.
- multiply(G) - Method in interface dev.nm.algebra.structure.Monoid
-
× : G × G → G
- multiply(Polynomial) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
- multiply(Complex) - Method in class dev.nm.number.complex.Complex
-
Compute the product of this complex number and that complex number.
- multiply(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
-
- multiply(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
-
Multiply one double
array to another, entry-by-entry.
- multiply(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
-
- multiply(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
-
- multiply(Real) - Method in class dev.nm.number.Real
-
- multiplyInPlace(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Left multiplication by G, namely, G * A.
- multiplyInPlace(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Right multiplies this matrix, A, by a vector.
- MultipointHybridMCMC - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
-
Constructs a new instance with equal weights to the M configurations.
- MultivariateArrayGrid - Class in dev.nm.analysis.curvefit.interpolation.multivariate
-
- MultivariateArrayGrid(MultiDimensionalCollection<Double>, double[]...) - Constructor for class dev.nm.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 dev.nm.stat.timeseries.linear.multivariate
-
This is the auto-correlation function of a multi-dimensional time series {Xt}.
- MultivariateAutoCorrelationFunction() - Constructor for class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction
-
- MultivariateAutoCovarianceFunction - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCovarianceFunction
-
- MultivariateBrownianRRG - Class in dev.nm.stat.stochasticprocess.multivariate.random
-
This is the Random Walk construction of a multivariate Brownian motion.
- MultivariateBrownianRRG(int, TimeGrid, Vector) - Constructor for class dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.stochasticprocess.multivariate.sde.discrete
-
A multivariate Brownian motion is a stochastic process with the following properties.
- MultivariateBrownianSDE(int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
Construct a standard multi-dimensional Brownian motion.
- MultivariateBrownianSDE(Vector, Matrix) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
Construct a multi-dimensional Brownian motion.
- MultivariateDiscreteSDE - Interface in dev.nm.stat.stochasticprocess.multivariate.sde.discrete
-
This interface represents the discrete approximation of a multivariate SDE.
- MultivariateDLM - Class in dev.nm.stat.dlm.multivariate
-
This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.
- MultivariateDLM(Vector, Matrix, MultivariateObservationEquation, MultivariateStateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLM
-
Construct a (multivariate) controlled dynamic linear model.
- MultivariateDLM(MultivariateDLM) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLM
-
Copy constructor.
- MultivariateDLMSeries - Class in dev.nm.stat.dlm.multivariate
-
This is a simulator for a multivariate controlled dynamic linear model process.
- MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
-
Simulate a multivariate controlled dynamic linear model process.
- MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
-
Simulate a multivariate controlled dynamic linear model process.
- MultivariateDLMSeries(int, MultivariateDLM) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
-
Simulate a multivariate controlled dynamic linear model process.
- MultivariateDLMSeries.Entry - Class in dev.nm.stat.dlm.multivariate
-
This is the TimeSeries.Entry
for a multivariate DLM time series.
- MultivariateDLMSim - Class in dev.nm.stat.dlm.multivariate
-
This is a simulator for a multivariate controlled dynamic linear model process.
- MultivariateDLMSim(MultivariateDLM, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSim
-
Simulates a multivariate controlled dynamic linear model process.
- MultivariateDLMSim.Innovation - Class in dev.nm.stat.dlm.multivariate
-
a simulated innovation
- MultivariateEulerSDE - Class in dev.nm.stat.stochasticprocess.multivariate.sde.discrete
-
The Euler scheme is the first order approximation of an SDE.
- MultivariateEulerSDE(MultivariateSDE) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
-
Discretize a multivariate, continuous-time SDE using the Euler scheme.
- MultivariateExponentialFamily - Class in dev.nm.stat.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 dev.nm.stat.distribution.multivariate.exponentialfamily.MultivariateExponentialFamily
-
Construct a factory to construct probability distribution in the exponential family of this
form.
- MultivariateFiniteDifference - Class in dev.nm.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 dev.nm.analysis.differentiation.multivariate.MultivariateFiniteDifference
-
Construct the partial derivative of a multi-variable function.
- MultivariateForecastOneStep - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
-
Construct an instance of InnovationAlgorithm for a multivariate time series with known auto-covariance structure.
- MultivariateFt - Class in dev.nm.stat.stochasticprocess.multivariate.sde
-
This represents the concept 'Filtration', the information available at time t.
- MultivariateFt() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Construct an empty filtration (no information).
- MultivariateFt(MultivariateFt) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Copy constructor.
- MultivariateFtWt - Class in dev.nm.stat.stochasticprocess.multivariate.sde
-
This is a filtration implementation that includes the path-dependent information,
Wt.
- MultivariateFtWt() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
-
Construct an empty filtration (no information).
- MultivariateFtWt(MultivariateFtWt) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
-
Copy constructor.
- MultivariateGenericTimeTimeSeries<T extends Comparable<? super T>> - Class in dev.nm.stat.timeseries.datastructure.multivariate
-
This is a multivariate time series indexed by some notion of time.
- MultivariateGenericTimeTimeSeries(T[], Vector[]) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct a multivariate time series from timestamps and vectors.
- MultivariateGenericTimeTimeSeries(T[], double[][]) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct a multivariate time series from timestamps and vectors.
- MultivariateGenericTimeTimeSeries(T[], Matrix) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct a multivariate time series from timestamps and vectors.
- MultivariateGrid - Interface in dev.nm.analysis.curvefit.interpolation.multivariate
-
A multivariate rectilinear (not necessarily uniform) grid of double
values.
- MultivariateGridInterpolation - Interface in dev.nm.analysis.curvefit.interpolation.multivariate
-
Interpolation on a rectilinear multi-dimensional grid.
- MultivariateInnovationAlgorithm - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
-
Run the Innovation Algorithm to compute the prediction parameters,
V and Θ.
- MultivariateIntTimeTimeSeries - Interface in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
-
This is a multivariate time series indexed by integers.
- MultivariateIntTimeTimeSeries.Entry - Class in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
-
This is the TimeSeries.Entry
for an integer -indexed multivariate time series.
- MultivariateLinearKalmanFilter - Class in dev.nm.stat.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 dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Construct a Kalman filter from a multivariate controlled dynamic linear model.
- MultivariateMinimizer<P extends OptimProblem,S extends MinimizationSolution<Vector>> - Interface in dev.nm.solver.multivariate.unconstrained
-
This is a minimizer that minimizes a multivariate function or a
Vector function.
- MultivariateNormalDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
Constructs an instance with the given mean and covariance matrix.
- MultivariateNormalDistribution(int) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
Constructs an instance of the standard Normal distribution.
- MultivariateObservationEquation - Class in dev.nm.stat.dlm.multivariate
-
This is the observation equation in a controlled dynamic linear model.
- MultivariateObservationEquation(R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs an observation equation.
- MultivariateObservationEquation(R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs an observation equation.
- MultivariateObservationEquation(Matrix, Matrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs a time-invariant an observation equation.
- MultivariateObservationEquation(Matrix, Matrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs a time-invariant an observation equation.
- MultivariateObservationEquation(ObservationEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs a multivariate observation equation from a univariate observation equation.
- MultivariateObservationEquation(MultivariateObservationEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Copy constructor.
- MultivariateProbabilityDistribution - Interface in dev.nm.stat.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 dev.nm.stat.stochasticprocess.multivariate.random
-
This interface represents a multivariate random process a.k.a.
- MultivariateRandomProcess(int, TimeGrid) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Construct a multivariate random process.
- MultivariateRandomRealizationGenerator - Interface in dev.nm.stat.stochasticprocess.multivariate.random
-
This interface defines a generator to construct random realizations from a multivariate stochastic process.
- MultivariateRandomRealizationOfRandomProcess - Class in dev.nm.stat.stochasticprocess.multivariate.random
-
This class generates random realizations from a multivariate random/stochastic process.
- MultivariateRandomRealizationOfRandomProcess(MultivariateRandomProcess, int, RandomLongGenerator) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
Construct a random realization generator from a multivariate random/stochastic process.
- MultivariateRandomRealizationOfRandomProcess(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
Construct a random realization generator from a multivariate discrete SDE.
- MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
Construct a random realization generator from a multivariate SDE.
- MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
Construct a random realization generator from a multivariate SDE.
- MultivariateRandomWalk - Class in dev.nm.stat.stochasticprocess.multivariate.random
-
This is the Random Walk construction of a multivariate stochastic process per SDE specification.
- MultivariateRandomWalk(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomWalk
-
Construct a multivariate stochastic process from an SDE.
- MultivariateRealization - Interface in dev.nm.stat.timeseries.datastructure.multivariate.realtime
-
A multivariate realization is a multivariate time series indexed by real numbers, e.g., real time.
- MultivariateRealization.Entry - Class in dev.nm.stat.timeseries.datastructure.multivariate.realtime
-
This is the TimeSeries.Entry
for a real number -indexed multivariate time series.
- MultivariateRegularGrid - Class in dev.nm.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 dev.nm.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 dev.nm.analysis.curvefit.interpolation.multivariate
-
Specify the positioning and spacing along one dimension.
- MultivariateResampler - Interface in dev.nm.stat.random.sampler.resampler.multivariate
-
This is the interface of a multivariate re-sampler method.
- MultivariateSDE - Class in dev.nm.stat.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 dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
-
Construct a multi-dimensional diffusion type stochastic differential equation.
- MultivariateSimpleTimeSeries - Class in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
-
This simple multivariate time series has its vectored values indexed by integers.
- MultivariateSimpleTimeSeries(Matrix) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries
.
- MultivariateSimpleTimeSeries(double[]...) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries
.
- MultivariateSimpleTimeSeries(Vector...) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries
.
- MultivariateSimpleTimeSeries(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of MultivariateSimpleTimeSeries
from a univariate time series.
- MultivariateStateEquation - Class in dev.nm.stat.dlm.multivariate
-
This is the state equation in a controlled dynamic linear model.
- MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a state equation.
- MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a state equation.
- MultivariateStateEquation(R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a state equation without control variables.
- MultivariateStateEquation(Matrix, Matrix, Matrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a time-invariant state equation.
- MultivariateStateEquation(Matrix, Matrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a time-invariant state equation without control variables.
- MultivariateStateEquation(StateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a multivariate state equation from a univariate state equation.
- MultivariateStateEquation(MultivariateStateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Copy constructor.
- MultivariateTDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
Constructs an instance with the given mean and scale matrix.
- MultivariateTDistribution(int, int) - Constructor for class dev.nm.stat.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 dev.nm.stat.timeseries.datastructure.multivariate
-
A multivariate time series is a sequence of vectors indexed by some notion of time.
- MultivariateTimeSeries.Entry<T> - Class in dev.nm.stat.timeseries.datastructure.multivariate
-
This is the TimeSeries.Entry
for a multivariate time series.
- mutate() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.Chromosome
-
Construct a Chromosome
by mutation.
- mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell
-
- mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
-
- mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
-
- mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
-
- mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell
-
Mutate by a local search in the neighborhood.
- mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
-
Mutate by random disturbs in a neighborhood.
- Mutex - Class in dev.nm.misc.parallel
-
Provides mutual exclusive execution of a Runnable
.
- Mutex() - Constructor for class dev.nm.misc.parallel.Mutex
-
- MVOptimizer - Interface in tech.nmfin.portfoliooptimization.lai2010.optimizer
-
Solves for the optimal weight using Mean-Variance optimization.
- MVOptimizerLongOnly - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer
-
A long-only MV optimizer.
- MVOptimizerLongOnly() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerLongOnly
-
- MVOptimizerMinWeights - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer
-
Solves for weights with lower bounds.
- MVOptimizerMinWeights(Vector) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights
-
Constructs the solver with a constraint on the minimum weights (w ≥
w0).
- MVOptimizerNoConstraint - Class in tech.nmfin.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 tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerNoConstraint
-
- MVOptimizerShrankMean - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer
-
Shrinks the mean towards average before passing the inputs to another
MVOptimizer.
- MVOptimizerShrankMean(MVOptimizer, double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerShrankMean
-
- MWC8222 - Class in dev.nm.stat.random.rng.univariate.uniform
-
Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.
- MWC8222() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.MWC8222
-
Construct a random number generator to sample uniformly from [0, 1].
- MyCutter(ILPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer.MyCutter
-
Construct a Gomory mixed cutter.
- MyCutter(PureILPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer.MyCutter
-
Construct a Gomory pure cutter.
- MySteepestDescent(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent
-
- N() - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
Get the number of data points.
- n() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
-
Gets the number of interior y-axis grid points in the solution.
- N() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
-
Gets the number of interior space-axis grid points in the solution.
- n() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the number of interior x-axis grid points in the solution.
- n - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- n() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Gets the dimension of the square matrices C and As.
- n() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets the number of columns of Ai.
- n() - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the number of rows of the multivariate time series used in
regression.
- n - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
sample size
- N() - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
-
- N() - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
-
- N() - Method in class dev.nm.stat.descriptive.covariance.Covariance
-
- N() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
-
- N() - Method in class dev.nm.stat.descriptive.moment.Mean
-
- N() - Method in class dev.nm.stat.descriptive.moment.Moments
-
- N() - Method in class dev.nm.stat.descriptive.moment.Skewness
-
- N() - Method in class dev.nm.stat.descriptive.moment.Variance
-
- N() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
-
- N() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
-
- N() - Method in class dev.nm.stat.descriptive.rank.Max
-
- N() - Method in class dev.nm.stat.descriptive.rank.Min
-
- N() - Method in class dev.nm.stat.descriptive.rank.Quantile
-
- N() - Method in interface dev.nm.stat.descriptive.Statistic
-
Get the size of the sample.
- N() - Method in class dev.nm.stat.descriptive.SynchronizedStatistic
-
- N - Variable in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
the number of observations
- N - Variable in class tech.nmfin.infantino2010.Infantino2010PCA
-
- N() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
-
Gets the number of H inversions.
- NaiveRule - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
- name - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
the variable name
- NaN - Static variable in class dev.nm.number.complex.Complex
-
a number representing the complex Not-a-Number (NaN
)
- natural() - Static method in class dev.nm.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 dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get the dimension of the Brownian motion (or the number of driving 1D Brownian motions).
- nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
-
- nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
- nB() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix
-
Get the number of independent Brownian motions.
- nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
-
- nB() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE
-
Get the number of independent driving Brownian motions.
- nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
-
- nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the number of independent driving Brownian motions.
- nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get the number of driving Brownian motions.
- nChildren() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Get the number of children before populating the next generation.
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.SubMatrixBlock
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- nCols() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- nCols() - Method in class dev.nm.misc.datastructure.FlexibleTable
-
- nCols() - Method in interface dev.nm.misc.datastructure.Table
-
Gets the number of columns.
- nColumns() - Method in class dev.nm.misc.datastructure.MathTable
-
Gets the number of columns in the table.
- NEGATIVE_INFINITY - Static variable in class dev.nm.number.complex.Complex
-
a number representing -∞ + -∞i
- NegCetaFunction(Ceta) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.NegCetaFunction
-
- neighbor(V) - Method in interface dev.nm.graph.Edge
-
Gets the neighboring vertex connected to vertex
.
- neighbor(V) - Method in class dev.nm.graph.type.SimpleArc
-
- neighbor(V) - Method in class dev.nm.graph.type.SimpleEdge
-
Get the unique neighboring vertex connected to vertex
.
- neighbors(V) - Method in interface dev.nm.graph.HyperEdge
-
Gets the set of neighboring vertices connected to vertex
.
- neighbors(V) - Method in class dev.nm.graph.type.SimpleArc
-
- neighbors(V) - Method in class dev.nm.graph.type.SimpleEdge
-
- NelderMeadMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer
-
Construct a Nelder-Mead multivariate minimizer.
- NelderMeadMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer
-
Construct a Nelder-Mead multivariate minimizer.
- NelderMeadMinimizer.Solution - Class in dev.nm.solver.multivariate.unconstrained.c2
-
This is the solution to an optimization problem by the Nelder-Mead method.
- nEqualities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Get the number of equality constraints.
- NEUTRON_MASS_M - Static variable in class dev.nm.misc.PhysicalConstants
-
The neutron mass in kilograms (kg).
- NevilleTable - Class in dev.nm.analysis.curvefit.interpolation
-
Neville's algorithm is a polynomial interpolation algorithm.
- NevilleTable(int, OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
Construct a Neville table of size n, initialized with data {(x, y)}.
- NevilleTable(OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
Construct a Neville table of size n, initialized with data {(x, y)}.
- NevilleTable() - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
Construct an empty Neville table.
- newActiveList() - Method in interface dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer.ActiveListFactory
-
Construct a new instance of ActiveList
for an Integer Linear Programming problem.
- newCellFactory() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.NewCellFactory
-
Construct a new instance of DEOptimCellFactory
for a minimization problem.
- newCellFactory() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.NewCellFactoryCtor
-
Construct a new instance of SimpleCellFactory
for a minimization problem.
- newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
-
- newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
-
- newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
-
- newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- newDistributions() - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution
-
Get the distributions (possibly differently parameterized) for all
states.
- newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
-
- newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
-
- newInstance(double[], int, int, DoubleArrayOperation) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- newInstance(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.PreconditionerFactory
-
- newInstance() - Method in interface dev.nm.solver.multivariate.constrained.SubProblemMinimizer.ConstrainedMinimizerFactory
-
- newInstance() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.MinimizerFactory
-
Construct a new instance of Minimizer
for a mutation operation.
- newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution
-
Construct a new distribution from a set of parameters, one set per state.
- newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
-
- newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
-
- newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
-
- newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
-
- newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
-
- newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
-
- newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
-
- newRandomNumberGenerators() - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution
-
Get the random number generators corresponding to the distributions
(possibly differently parameterized) for all states.
- newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
-
- newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
-
- newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
- newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
- newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
- newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
- newResample() - Method in class dev.nm.stat.random.sampler.resampler.multivariate.GroupResampler
-
- newResample() - Method in interface dev.nm.stat.random.sampler.resampler.multivariate.MultivariateResampler
-
Gets a resample from the original sample.
- newResample() - Method in interface dev.nm.stat.random.sampler.resampler.ObjectResampler
-
Gets a resample from the original sample.
- newResample() - Method in interface dev.nm.stat.random.sampler.resampler.Resampler
-
Gets a resample from the original sample.
- newResampler(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- newResampler(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
-
- newResampler(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
-
- newResampler(Matrix) - Method in interface tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ReturnsResamplerFactory
-
Constructs a new instance of a re-sampling mechanism.
- newSOCPGeneralConstraints(SOCPPortfolioConstraint.Variable...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
- NewtonCotes - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
Construct an instance of the Newton-Cotes quadrature.
- NewtonCotes.Type - Enum in dev.nm.analysis.integration.univariate.riemann.newtoncotes
-
There are two types of the Newton-Cotes method: OPEN and CLOSED.
- NewtonPolynomial - Class in dev.nm.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 dev.nm.analysis.curvefit.interpolation.univariate.NewtonPolynomial
-
- NewtonRaphsonImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer.NewtonRaphsonImpl
-
- NewtonRaphsonMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer
-
Construct a multivariate minimizer using the Newton-Raphson method.
- NewtonRaphsonMinimizer.NewtonRaphsonImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
-
- NewtonRoot - Class in dev.nm.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 dev.nm.analysis.root.univariate.NewtonRoot
-
Constructs an instance of Newton's root finding algorithm.
- NewtonSystemRoot - Class in dev.nm.analysis.root.multivariate
-
This class solves the root for a non-linear system of equations.
- NewtonSystemRoot(double, int) - Constructor for class dev.nm.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.VariationFactory
-
Construct a new instance of SQPASVariation
for an SQP
problem.
- nExogenousFactors() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Gets the number of factors, excluding the intercept.
- next() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
-
- next(int, UnivariateRealFunction, double, double, double) - Method in interface dev.nm.analysis.integration.univariate.riemann.IterativeIntegrator
-
Compute a refined sum for the integral.
- next(int, UnivariateRealFunction, double, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
-
- next(int, UnivariateRealFunction, double, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
- next() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassSampler
-
Gets the next random element from the range of the probability distribution.
- next(Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim
-
Gets the next innovation.
- next(double) - Method in class dev.nm.stat.dlm.univariate.DLMSim
-
Get the next innovation.
- next() - Method in class dev.nm.stat.hmm.HMMRNG
-
Gets the next simulated innovation: state and observation.
- next() - Method in interface dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator.Generator
-
Returns the next value in the underlying generated sequence.
- next() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator
-
Returns the next value in the generated sequence.
- next() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast
-
Gets the next forecast.
- next(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast
-
Gets the next n-step forecasts.
- next() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Gets the next forecast.
- next(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Gets the next n-step forecasts.
- nextDouble() - Method in class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
-
- nextDouble() - Method in class dev.nm.stat.evt.markovchain.ExtremeValueMC
-
- nextDouble() - Method in class dev.nm.stat.evt.timeseries.MARMASim
-
- nextDouble() - Method in class dev.nm.stat.hmm.HMMRNG
-
Gets the next simulated observation.
- nextDouble() - Method in class dev.nm.stat.markovchain.SimpleMC
-
Gets the next simulated state.
- nextDouble() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRNG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
-
Get the next random double
, which is either 1 (success) or 0 (failure).
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.beta.Cheng1978
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969
-
Deprecated.
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.BinomialRNG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.BurnInRNG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.exp.Ziggurat2000Exp
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.InverseTransformSampling
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.LogNormalRNG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.BoxMuller
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.NormalRNG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.Zignor2005
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.poisson.Knuth1969
-
- nextDouble() - Method in interface dev.nm.stat.random.rng.univariate.RandomNumberGenerator
-
Get the next random double
.
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.ThinRNG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.MWC8222
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3
-
- nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
-
- nextDouble() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk
-
- nextDouble() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
-
- nextDouble() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
-
- nextDouble() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
- nextDouble() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
Gets the next simulated observation.
- nextInt() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0
-
- nextInt() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3
-
- nextLogTrial(RandomNumberGenerator, double) - Static method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability ep.
- nextLogTrial() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability ep.
- nextLong() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG
-
- nextLong() - Method in class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
-
- nextLong() - Method in interface dev.nm.stat.random.rng.univariate.RandomLongGenerator
-
Get the next random long
.
- nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
-
- nextLong() - Method in class dev.nm.stat.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 dev.nm.stat.random.rng.univariate.uniform.linear.MRG
-
- nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
- nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.MWC8222
-
- nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0
-
- nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3
-
- nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
-
- nextMatrix() - Method in class dev.nm.stat.test.distribution.pearson.AS159
-
Constructs a random matrix based on the row and column sums.
- nextN(RandomNumberGenerator, int) - Static method in class dev.nm.stat.random.rng.RNGUtils
-
Generates n
random numbers from a given random number generator.
- nextN(RandomVectorGenerator, int) - Static method in class dev.nm.stat.random.rng.RNGUtils
-
Generates n
random vectors from a given random vector generator.
- nextP(double, double, double) - Method in class tech.nmfin.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Get the next random (e2_t, h_t).
- nextProposal(Vector, double) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.AnnealingFunction
-
Gets the next proposal, given the current state and the temperature.
- nextProposal(Vector, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoxGSAAnnealingFunction
-
- nextProposal(Vector, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction
-
- nextProposal(Vector, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction
-
- nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.HybridMCMC
-
- nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
-
- nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
Proposes a next state for the system.
- nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis
-
- nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings
-
- nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
-
- nextRealization() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG
-
- nextRealization() - Method in interface dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationGenerator
-
Construct a realization of a multivariate stochastic process.
- nextRealization() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
- nextRealization() - Method in interface dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationGenerator
-
Construct a realization of a univariate stochastic process.
- nextRealization() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
- nextState() - Method in class dev.nm.stat.markovchain.SimpleMC
-
Gets the next simulated state.
- nextTime() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get the next time point in the time grid.
- nextTime() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
-
Get the next time point in the time grid.
- nextTrial(RandomNumberGenerator, double) - Static method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability p.
- nextTrial() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability p.
- nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- nextVector() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRVG
-
- nextVector() - Method in class dev.nm.stat.random.rng.multivariate.BurnInRVG
-
- nextVector() - Method in class dev.nm.stat.random.rng.multivariate.HypersphereRVG
-
- nextVector() - Method in class dev.nm.stat.random.rng.multivariate.IID
-
- nextVector() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
-
- nextVector() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
- nextVector() - Method in class dev.nm.stat.random.rng.multivariate.MultinomialRVG
-
- nextVector() - Method in class dev.nm.stat.random.rng.multivariate.NormalRVG
-
- nextVector() - Method in interface dev.nm.stat.random.rng.multivariate.RandomVectorGenerator
-
Get the next random vector.
- nextVector() - Method in class dev.nm.stat.random.rng.multivariate.ThinRVG
-
- nextVector() - Method in class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox
-
- nextVector() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomWalk
-
- nextVector() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
-
- nextWeekDay(DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Get the next weekday, i.e., skipping Saturdays and Sundays.
- nextX(double, double, double) - Method in class tech.nmfin.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 dev.nm.stat.factor.factoranalysis.FactorAnalysis
-
Gets the number of factors.
- nFactors() - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the number of variables in the original data.
- nFactors() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Gets the number of factors, including the intercept if any.
- nGreaterThanInequalities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
Get the number of greater-than-or-equal-to constraints.
- nGroups() - Method in class dev.nm.stat.test.HypothesisTest
-
Get the number of groups of observations.
- nidx - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
number of unique values in dis_G_list
- nlshrink_tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
-
Gets nonlinear shrinkage tau in ascending order.
- NMSAAM - Class in tech.nmfin.portfoliooptimization.nmsaam
-
- NMSAAM(double, double, int, int, double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
-
- NMSAAM(double, double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
-
- NMSAAM(double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
-
- NMSAAM(double, double, int) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
-
- NMSAAM(double, int) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
-
- nNoChanges - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- nNonZeros() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseStructure
-
Get the number of non-zero entries in the structure.
- nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- no(double) - Method in interface dev.nm.number.DoubleUtils.ifelse
-
Return value for a false
element of test.
- nObs() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
-
Gets the number of observations.
- nObs() - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the number of observations in the original data; sample size.
- nObs() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Gets the number of observations.
- nObs() - Method in class dev.nm.stat.test.HypothesisTest
-
Get the total number of observations.
- NoChangeOfVariable - Class in dev.nm.analysis.integration.univariate.riemann.substitution
-
This is a dummy substitution rule that does not change any variable.
- NoChangeOfVariable(double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
Construct an NoChangeOfVariable
substitution rule.
- NoConstraints - Class in tech.nmfin.portfoliooptimization.corvalan2005.constraint
-
- NoConstraints() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoConstraints
-
- Node(V) - Constructor for class dev.nm.graph.algorithm.traversal.BFS.Node
-
Constructs a node for a spanning tree.
- Node(V) - Constructor for class dev.nm.graph.algorithm.traversal.DFS.Node
-
Constructs a node for a spanning tree.
- Node(V) - Constructor for class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
-
Constructs a node for a spanning tree.
- NoModelFitted() - Constructor for exception tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter.NoModelFitted
-
- noNaN(double[]) - Static method in class dev.nm.number.DoubleUtils
-
Remove the NaN
from an array.
- NonlinearFit - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
-
- NonlinearFit.Result - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
-
- NonlinearShrinkageEstimator - Class in dev.nm.stat.covariance.nlshrink
-
The nonlinear shrinkage method for given population eigenvalues.
- NonlinearShrinkageEstimator(Vector, int) - Constructor for class dev.nm.stat.covariance.nlshrink.NonlinearShrinkageEstimator
-
- NonlinearShrinkageEstimator(QuEST.Result) - Constructor for class dev.nm.stat.covariance.nlshrink.NonlinearShrinkageEstimator
-
- nonlinearShrunkEigenvalues() - Method in class dev.nm.stat.covariance.nlshrink.NonlinearShrinkageEstimator
-
Gets the nonlinear shrinkage eigenvalues in ascending order.
- NonNegativityConstraintOptimProblem - Class in dev.nm.solver.multivariate.constrained.problem
-
This is a constrained optimization problem for a function which has all non-negative variables.
- NonNegativityConstraintOptimProblem(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
-
Construct a constrained optimization problem with only non-negative variables.
- NonNegativityConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
-
These constraints ensures that for all variables are non-negative.
- NonNegativityConstraints(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.NonNegativityConstraints
-
Construct a lower bound constraints for all variables in a function.
- NoPairFoundException - Exception in tech.nmfin.meanreversion.cointegration
-
- NoPairFoundException(String) - Constructor for exception tech.nmfin.meanreversion.cointegration.NoPairFoundException
-
- norm() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- norm(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- norm(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- norm() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- norm(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the norm of a vector.
- norm(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the norm of a vector.
- norm() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- norm(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- norm() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- norm() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Compute the length or magnitude or Euclidean norm of a vector, namely, \(\|v\|\).
- norm(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Gets the \(L^p\)-norm \(\|v\|_p\) of this vector.
- norm() - Method in interface dev.nm.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 dev.nm.stat.distribution.univariate
-
The Normal distribution has its density a Gaussian function.
- NormalDistribution() - Constructor for class dev.nm.stat.distribution.univariate.NormalDistribution
-
Construct an instance of the standard Normal distribution with mean 0 and standard deviation 1.
- NormalDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.NormalDistribution
-
Construct a Normal distribution with mean mu
and standard deviation sigma
.
- NormalMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
-
The HMM states use the Normal distribution to model the observations.
- NormalMixtureDistribution(NormalMixtureDistribution.Lambda[], boolean, boolean) - Constructor for class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
-
Constructs a Normal distribution for each state in the HMM model.
- NormalMixtureDistribution(NormalMixtureDistribution.Lambda[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
-
Constructs a Normal distribution for each state in the HMM model.
- NormalMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
-
the Normal distribution parameters
- NormalOfExpFamily1 - Class in dev.nm.stat.distribution.univariate.exponentialfamily
-
Normal distribution, univariate, unknown mean, known variance.
- NormalOfExpFamily1(double) - Constructor for class dev.nm.stat.distribution.univariate.exponentialfamily.NormalOfExpFamily1
-
- NormalOfExpFamily2 - Class in dev.nm.stat.distribution.univariate.exponentialfamily
-
Normal distribution, univariate, unknown mean, unknown variance.
- NormalOfExpFamily2() - Constructor for class dev.nm.stat.distribution.univariate.exponentialfamily.NormalOfExpFamily2
-
- NormalRNG - Class in dev.nm.stat.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 dev.nm.stat.random.rng.univariate.normal.NormalRNG
-
Construct a random number generator to sample from the Normal distribution.
- NormalRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.normal.NormalRNG
-
Construct a random number generator to sample from the Normal distribution.
- NormalRVG - Class in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.NormalRVG
-
Constructs a multivariate Normal random vector generator.
- NormalRVG(Vector, Matrix, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG
-
Constructs a multivariate Normal random vector generator.
- NormalRVG(Vector, Matrix) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG
-
Constructs a multivariate Normal random vector generator.
- NormalRVG(int) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG
-
Constructs a standard multivariate Normal random vector generator.
- NoRootFoundException - Exception in dev.nm.analysis.root.univariate
-
This is the
Exception
thrown when it fails to find a root.
- NoRootFoundException(double, double) - Constructor for exception dev.nm.analysis.root.univariate.NoRootFoundException
-
Construct a NoRootFoundException
.
- NoShortSelling - Class in tech.nmfin.portfoliooptimization.corvalan2005.constraint
-
Weights cannot be negative.
- NoShortSelling(int) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoShortSelling
-
Constructs the constraint with the number of assets in the portfolio.
- NoSolution(String) - Constructor for exception dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.NoSolution
-
Construct an LinearSystemSolver.NoSolution
exception.
- notAKnot() - Static method in class dev.nm.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the number of parameters for the estimation/fitting.
- NPEBPortfolioMomentsEstimator - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb
-
Uses Non-Parametric Empirical Bayes (NPEB) approach to estimate the first and
the second moments of the weighted portfolios.
- NPEBPortfolioMomentsEstimator(Matrix, ReturnsMoments.Estimator, MVOptimizer, ReturnsResamplerFactory, int) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.NPEBPortfolioMomentsEstimator
-
- nPopulation() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Get the size of the population pool, that is the number of chromosomes.
- nquant - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
number of points we select between in F[i]
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.SubMatrixBlock
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- nRows() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- nRows() - Method in class dev.nm.misc.datastructure.FlexibleTable
-
- nRows() - Method in interface dev.nm.misc.datastructure.Table
-
Gets the number of rows.
- nSamples() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
Get the number of samples in the empirical distribution.
- nSim - Variable in class dev.nm.stat.test.distribution.normality.JarqueBera
-
- nStableIterations - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- nStates() - Method in class dev.nm.stat.markovchain.SimpleMC
-
Gets the number of states.
- nSymbols() - Method in class dev.nm.stat.hmm.discrete.DiscreteHMM
-
Gets the number of observation symbols per state.
- nullDeviance() - Method in class dev.nm.stat.regression.linear.logistic.LogisticResiduals
-
Gets the null deviance.
- nullity() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the nullity of A.
- nullity(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
- NullMonitor<S> - Class in dev.nm.misc.algorithm.iterative.monitor
-
- NullMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.NullMonitor
-
- numberOfChildren(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils
-
Gets the number of children.
- numberOfClusters() - Method in class dev.nm.graph.community.GirvanNewman
-
Gets the number of connected clusters.
- numberOfEdges(Graph<V, ?>) - Static method in class dev.nm.graph.GraphUtils
-
Gets the number of edges in this graph.
- numberOfParents(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils
-
Gets the number of parents.
- numberOfVertices(Graph<V, ?>) - Static method in class dev.nm.graph.GraphUtils
-
Gets the number of vertices in this graph.
- NumberUtils - Class in dev.nm.number
-
These are the utility functions to manipulate
Number
s.
- NumberUtils.Comparable<T extends Number> - Interface in dev.nm.number
-
We need a precision parameter to determine whether two numbers are close enough to be treated
as equal.
- numint - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
number of intervals
- nVariables() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
-
Gets the number of variables in the original data set.
- P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the permutation matrix, P, such that P * A = L * U.
- P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
- P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
- P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
Gets P, the pivoting matrix in the QR decomposition.
- P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
-
- P() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get P, the pivoting matrix in the QR decomposition.
- P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
-
- P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
-
- P() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition
-
Get the permutation matrix P as in P * A = L * U.
- p() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the number of interior y-axis grid points in the solution.
- p() - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction
-
- p() - Method in class dev.nm.analysis.function.special.beta.BetaRegularized
-
Get p, the shape parameter.
- p() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
-
Gets the size of b.
- p - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- p - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
number of variables
- p() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
-
Get the number of AR terms.
- p - Variable in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
-
the success probability in each trial
- p() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState
-
Gets the momentum.
- p() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Gets the current momentum.
- p() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the number of AR terms.
- p() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the order of the VECM model.
- p() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the number of AR terms.
- p() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the number of GARCH terms.
- p - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
- Package - Class in dev.nm.misc.license
-
- Pair - Class in dev.nm.analysis.function.tuple
-
An ordered pair (x,y) is a pair of mathematical objects.
- Pair(double, double) - Constructor for class dev.nm.analysis.function.tuple.Pair
-
Construct a pair.
- PairComparatorByAbscissaFirst - Class in dev.nm.analysis.function.tuple
-
- PairComparatorByAbscissaFirst() - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaFirst
-
- PairComparatorByAbscissaFirst(double) - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaFirst
-
- PairComparatorByAbscissaOnly - Class in dev.nm.analysis.function.tuple
-
- PairComparatorByAbscissaOnly() - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaOnly
-
- PairComparatorByAbscissaOnly(double) - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaOnly
-
- PairingCheck - Interface in tech.nmfin.meanreversion.cointegration.check
-
- PairingModel - Interface in tech.nmfin.meanreversion.cointegration
-
Given a set of symbols, their prices and other information, we find mean
reverting pairs for trading.
- PairingModel1 - Class in tech.nmfin.meanreversion.cointegration
-
- PairingModel1(double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel1
-
- PairingModel2 - Class in tech.nmfin.meanreversion.cointegration
-
- PairingModel2(double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel2
-
- PairingModel3 - Class in tech.nmfin.meanreversion.cointegration
-
- PairingModel3(double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel3
-
- PairingModel4 - Class in tech.nmfin.meanreversion.cointegration
-
- PairingModel4(double, double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel4
-
- PairingModel5 - Class in tech.nmfin.meanreversion.cointegration
-
- PairingModel5(double, double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel5
-
- PairingModelUtils - Class in tech.nmfin.meanreversion.cointegration
-
- PairingModelUtils() - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModelUtils
-
- PanelData<S> - Class in dev.nm.stat.regression.linear.panel
-
A panel data refers to multi-dimensional data frequently involving
measurements over time.
- PanelData(String, String, String[]) - Constructor for class dev.nm.stat.regression.linear.panel.PanelData
-
Constructs a panel of two-dimensional data.
- PanelData.Row - Class in dev.nm.stat.regression.linear.panel
-
This is one row of the data in a panel.
- PanelData.Transformation - Interface in dev.nm.stat.regression.linear.panel
-
Transforms the data, e.g., taking log.
- PanelRegression - Interface in dev.nm.stat.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 dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
This indicate if the algorithm is to run in parallel (multi-core).
- ParallelDoubleArrayOperation - Class in dev.nm.number.doublearray
-
This is a multi-threaded implementation of the array math operations.
- ParallelDoubleArrayOperation() - Constructor for class dev.nm.number.doublearray.ParallelDoubleArrayOperation
-
- ParallelExecutor - Class in dev.nm.misc.parallel
-
This class provides a framework for executing an algorithm in parallel.
- ParallelExecutor() - Constructor for class dev.nm.misc.parallel.ParallelExecutor
-
Creates an instance using default concurrency number, which is the
number of available processors returned by
- ParallelExecutor(String) - Constructor for class dev.nm.misc.parallel.ParallelExecutor
-
Creates an instance with a specified executor name.
- ParallelExecutor(int) - Constructor for class dev.nm.misc.parallel.ParallelExecutor
-
Creates an instance with a specified concurrency number.
- ParallelExecutor(String, int) - Constructor for class dev.nm.misc.parallel.ParallelExecutor
-
Creates an instance with a specified concurrency number, and a name of
the executor.
- parent() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
-
Gets the parent of the node.
- parent(V) - Method in interface dev.nm.graph.Tree
-
Gets the unique parent of a vertex.
- parent(V) - Method in class dev.nm.graph.type.SparseTree
-
- parent(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- parents(V) - Method in interface dev.nm.graph.DiGraph
-
Gets the set of all parents of this vertex.
- parents(V) - Method in class dev.nm.graph.type.SparseDiGraph
-
- parents(V) - Method in class dev.nm.graph.type.SparseTree
-
- parents(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- parse(String) - Static method in class dev.nm.number.NumberUtils
-
Construct a number from a String.
- parseArray(String...) - Static method in class dev.nm.number.NumberUtils
-
Convert an array of numbers in
String
to an array of numbers in
Number
.
- PartialDerivativesByCenteredDifferencing - Class in dev.nm.analysis.curvefit.interpolation.bivariate
-
This implementation computes the partial derivatives by centered differencing.
- PartialDerivativesByCenteredDifferencing() - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
-
- PartialFunction - Class in dev.nm.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 dev.nm.analysis.function.tuple.PartialFunction
-
Construct a partial function from {(x,y)}.
- paste(Collection<String>, String) - Static method in class dev.nm.misc.StringUtils
-
Concatenates String
s into one String
.
- path - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- PattonPolitisWhite2009 - Class in dev.nm.stat.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 dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009(double[], long, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009(double[], PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009(double[], PattonPolitisWhite2009ForObject.Type) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009(double[]) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject<X> - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject(X[], Class<X>, long, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject(X[], Class<X>, long, PattonPolitisWhite2009ForObject.Type, ConcurrentCachedRLG, ConcurrentCachedRNG) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
Constructs a block bootstrap sample generator.
- PattonPolitisWhite2009ForObject.AutoCorrelationForObject - Interface in dev.nm.stat.random.sampler.resampler.bootstrap.block
-
- PattonPolitisWhite2009ForObject.AutoCovarianceForObject - Interface in dev.nm.stat.random.sampler.resampler.bootstrap.block
-
- PattonPolitisWhite2009ForObject.Type - Enum in dev.nm.stat.random.sampler.resampler.bootstrap.block
-
- PCA - Interface in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.factor.pca.PCAbyEigen
-
Performs Principal Component Analysis, using the eigen method, on a data
matrix.
- PCAbyEigen(Matrix) - Constructor for class dev.nm.stat.factor.pca.PCAbyEigen
-
Performs Principal Component Analysis, using the eigen method and using
correlation matrix, on a data matrix.
- PCAbySVD - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.factor.pca.PCAbySVD
-
Performs Principal Component Analysis, using the preferred SVD method, on
a centered and scaled data matrix.
- PDE - Interface in dev.nm.analysis.differentialequation.pde
-
A partial differential equation (PDE) is a differential equation that contains unknown
multivariable functions and their partial derivatives.
- PDESolutionGrid2D - Interface in dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.analysis.differentialequation.pde
-
A PDE solver solves a set of PDEs.
- PDETimeSpaceGrid1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference
-
This grid numerically solves a 1D PDE, e.g., using the Crank-Nicolson scheme.
- PDETimeSpaceGrid1D(int) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
Constructs a time-space grid.
- PeaksOverThreshold - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThreshold
-
Create an instance for POT method with a given threshold.
- PeaksOverThresholdOnClusters - Class in dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThresholdOnClusters
-
Create an instance with a
ClusterAnalyzer
which is used to find clusters from
observations.
- PearsonMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
-
This is the Pearson method.
- PearsonMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.PearsonMinimizer
-
Construct a multivariate minimizer using the Pearson method.
- pearsonStat(Matrix, Matrix, boolean) - Static method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
-
Compute the Pearson's cumulative test statistic, which asymptotically approaches a
χ2 distribution.
- penalizedCardinality(Matrix) - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Gets the value of a cardinality-penalized function.
- penalizedL1(Matrix) - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Gets the value of an L1-penalized function.
- PenaltyFunction - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyFunction
-
- PenaltyMethodMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
Construct a constrained minimizer using the penalty method.
- PenaltyMethodMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
Construct a constrained minimizer using the penalty method.
- PenaltyMethodMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
Construct a constrained minimizer using the penalty method.
- PenaltyMethodMinimizer.PenaltyFunctionFactory - Interface in dev.nm.solver.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 dev.nm.analysis.function.FunctionOps
-
Compute the permutation function.
- permutation(int, int) - Static method in class dev.nm.number.big.BigIntegerUtils
-
Compute the permutation function.
- PermutationMatrix - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Construct an identity permutation matrix.
- PermutationMatrix(int[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Construct a permutation matrix from an 1D double[]
.
- PermutationMatrix(PermutationMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Copy constructor.
- perpendicularDistance(Point) - Method in class dev.nm.geometry.LineSegment
-
- PerturbationAroundPoint - Class in dev.nm.solver.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 dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.PerturbationAroundPoint
-
Generate an initial pool of chromosomes by adding a variance around a given initial.
- phi - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- phi() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get all the AR coefficients.
- phi() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get all the AR coefficients.
- phiPolynomial() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the polynomial (1 - φ).
- PhysicalConstants - Class in dev.nm.misc
-
A collection of fundamental physical constants.
- PI - Static variable in class dev.nm.number.big.BigDecimalUtils
-
the value of PI
- PI() - Method in class dev.nm.stat.markovchain.SimpleMC
-
Gets the initial state probabilities.
- pi() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the impact matrix.
- PI() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
Get the coefficients of the linear representation of the time series.
- PI_SQ - Static variable in class dev.nm.misc.Constants
-
\(\pi^2\)
- Pivot(int, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
-
Construct a pivot.
- PLANCK_H - Static variable in class dev.nm.misc.PhysicalConstants
-
The Planck constant \(h\) in joule seconds (J s).
- PLANCK_REDUCED_HBAR - Static variable in class dev.nm.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 dev.nm.misc.datastructure.time.JodaTimeUtils
-
Add a weekday-period (i.e., skipping weekends) to a DateTime
.
- Point - Class in dev.nm.geometry
-
Represent a n-dimensional point.
- Point(double...) - Constructor for class dev.nm.geometry.Point
-
Create a point with given coordinates.
- Point(Vector) - Constructor for class dev.nm.geometry.Point
-
Create a point with given coordinates.
- points(double, double) - Method in interface dev.nm.solver.univariate.GridSearchMinimizer.GridDefinition
-
- PoissonDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.PoissonDistribution
-
Construct a Poisson distribution.
- PoissonEquation2D - Class in dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
-
Constructs a Poisson's equation problem.
- PoissonMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
-
The HMM states use the Poisson distribution to model the observations.
- PoissonMixtureDistribution(Double[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
-
Constructs a Poisson distribution for each state in the HMM model.
- PolygonalChain - Interface in dev.nm.geometry.polyline
-
A polygonal chain, polygonal curve, polygonal path, or piecewise linear curve, is a connected
series of line segments.
- PolygonalChainByArray - Class in dev.nm.geometry.polyline
-
- PolygonalChainByArray(List<? extends Point>) - Constructor for class dev.nm.geometry.polyline.PolygonalChainByArray
-
Create a new instance which uses the given vertices.
- Polynomial - Class in dev.nm.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 dev.nm.analysis.function.polynomial.Polynomial
-
Construct a polynomial from an array of coefficients.
- Polynomial(Polynomial) - Constructor for class dev.nm.analysis.function.polynomial.Polynomial
-
Copy constructor.
- PolyRoot - Class in dev.nm.analysis.function.polynomial.root
-
This is a solver for finding the roots of a polynomial equation.
- PolyRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.PolyRoot
-
- PolyRootSolver - Interface in dev.nm.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 dev.nm.misc.algorithm.bb.ActiveList
-
Get the next node.
- population - Variable in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
This is the (current) population pool.
- PortfolioMoments(double, double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
-
- PortfolioOptimizationAlgorithm - Interface in tech.nmfin.portfoliooptimization
-
Computes the optimal weights based only on returns.
- PortfolioOptimizationAlgorithm.CovarianceEstimator - Interface in tech.nmfin.portfoliooptimization
-
Define how the expected covariances of an asset for a future period is
computed.
- PortfolioOptimizationAlgorithm.MeanEstimator - Interface in tech.nmfin.portfoliooptimization
-
Define how the expected mean of an asset for a future period is
computed.
- PortfolioOptimizationAlgorithm.SampleCovarianceEstimator - Class in tech.nmfin.portfoliooptimization
-
Estimate the expected covariances of an asset using sample covariances.
- PortfolioOptimizationAlgorithm.SampleMeanEstimator - Class in tech.nmfin.portfoliooptimization
-
Estimate the expected mean of an asset using sample mean.
- PortfolioOptimizationAlgorithm.SymbolLookup - Interface in tech.nmfin.portfoliooptimization
-
Provides a lookup for product symbols and indices.
- PortfolioRiskExactSigma - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
- PortfolioRiskExactSigma.Diagonalization - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
- PortfolioRiskExactSigma.MatrixRoot - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Specifies the method to compute the root of a matrix.
- PortfolioUtils - Class in tech.nmfin.portfoliooptimization
-
- position(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- position() - Method in interface tech.nmfin.meanreversion.volarb.MRModel
-
Gets the current target position computed by this model.
- position() - Method in class tech.nmfin.meanreversion.volarb.MRModelRanged
-
- POSITIVE_INFINITY - Static variable in class dev.nm.number.complex.Complex
-
a number representing +∞ + ∞i
- PositiveDefiniteMatrixByPositiveDiagonal - Class in dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- Pow - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.Pow
-
Construct the power matrix An so that
An = basescale * B
- Pow(Matrix, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Pow
-
Construct the power matrix An so that
An = (1e100)scale * B
- pow(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- pow(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Takes a power of a vector, element-by-element.
- pow(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- pow(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- pow(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Take the exponentiation of all entries in this vector, entry-by-entry.
- pow(int) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
- pow(BigDecimal, BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute a to the power of b.
- pow(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute a to the power of b.
- pow(BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute a to the power of n, where n is an integer.
- pow(BigDecimal, int, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute a to the power of n, where n is an integer.
- pow(Complex, Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
z1 to the power z2.
- pow(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Raise each element in an array to the power of the given exponent.
- PowellImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl
-
- PowellMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer
-
Construct a multivariate minimizer using the Powell method.
- PowellMinimizer.PowellImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
-
an implementation of Powell's algorithm
- PowerLawSingularity - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
Construct a PowerLawSingularity
substitution rule.
- PowerLawSingularity.PowerLawSingularityType - Enum in dev.nm.analysis.integration.univariate.riemann.substitution
-
the type of end point divergence
- PrecisionUtils - Class in dev.nm.misc
-
Precision-related utility functions.
- Preconditioner - Interface in dev.nm.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 dev.nm.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 dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
-
- predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
-
- previousWeekDay(DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils
-
Get the previous weekday, i.e., skipping Saturdays and Sundays.
- price1 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- price2 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- pricing(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
This is pivot column selection (pricing) rule.
- pricing(SimplexTable) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
-
This is pivot column selection (pricing) rule.
- pricing(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule
-
This is pivot column selection (pricing) rule.
- PrimalDualInteriorPointMinimizer - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
-
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming
problems.
- PrimalDualPathFollowingMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
-
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming
problems.
- PrimalDualPathFollowingMinimizer.Solution - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
-
Construct a solution to a primal and a dual SOCP problems.
- prob - Variable in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix
-
the probability of observing this matrix
- probability() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassFunction.Mass
-
Gets the probability of the outcome.
- ProbabilityDistribution - Interface in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.distribution.discrete
-
Stores a possible outcome for a probability distribution and its associated probability.
- ProbabilityMassQuantile<X> - Class in dev.nm.stat.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 dev.nm.stat.distribution.discrete.ProbabilityMassQuantile
-
Constructs the quantile function for a probability mass function.
- ProbabilityMassQuantile(Iterable<ProbabilityMassFunction.Mass<X>>) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassQuantile
-
Constructs the quantile function for a probability mass function.
- ProbabilityMassSampler<X> - Class in dev.nm.stat.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 dev.nm.stat.distribution.discrete.ProbabilityMassSampler
-
Creates an instance with the probable values and an RNG.
- problem - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- Problem(Matrix, Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
-
- problem - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- problem() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Get the original covariance selection problem.
- problem() - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
-
Returns the original GLM problem.
- process() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightPhase1
-
Find a feasible table, if any.
- process() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightScheme2
-
Remove equalities and free variables, if possible.
- product(GivensMatrix[]) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- product(HouseholderReflection[], int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Compute Q from Householder matrices {Qi}.
- product(HouseholderReflection[], int, int, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Compute Q from Householder matrices {Qi}.
- ProductOfWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification
-
Defines portfolio diversification as
\[
D(w) = \prod_i w_i
\]
- ProductOfWeights() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.ProductOfWeights
-
- Projection - Class in dev.nm.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 dev.nm.algebra.linear.vector.doubles.operation.Projection
-
Project a vector v onto a set of basis {wi}.
- Projection(Vector, Vector[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection
-
Project a vector v onto a set of basis {wi}.
- Projection(Vector, Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection
-
Project a vector v onto another vector.
- propagate() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
Propagates the grid to the next time step by solving \(Au=d\).
- proportionVar() - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the proportion of overall variance explained by each of the
principal components.
- proportionVar(int) - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the proportion of overall variance explained by the i-th
principal component.
- proportionVar() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
-
- ProposalFunction - Class in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.ProposalFunction
-
- PROTON_ELECTRON_MASS_RATIO - Static variable in class dev.nm.misc.PhysicalConstants
-
The proton-electron mass ratio \(m_p/m_e\) (dimensionless).
- PROTON_MASS_MP - Static variable in class dev.nm.misc.PhysicalConstants
-
The proton mass \(m_p\) in kilograms (kg).
- PrZt0() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
Computes the stationary probability of in state Z_t = 0.
- PrZt1() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
Computes the stationary probability of in state Z_t = 1.
- PseudoInverse - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.PseudoInverse
-
Construct the Moore-Penrose pseudo-inverse matrix of a matrix.
- PseudoInverse(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.PseudoInverse
-
Construct the Moore-Penrose pseudo-inverse matrix of A.
- psi() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
-
Gets the estimated (optimal) psi, E(ee'), p.
- psi() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the coefficients of the deterministic terms.
- psi() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the coefficients of the deterministic terms.
- psi() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the coefficients of the deterministic terms.
- psi(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
-
- PureILPProblem - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.linear.problem.PureILPProblem
-
Construct a pure ILP problem.
- pValue() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
-
Calculates the p-value of the test statistics, given the degree of
freedom.
- pValue() - Method in class dev.nm.stat.test.distribution.AndersonDarling
-
- pValue() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples
-
- pValue() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov1Sample
-
- pValue() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov2Samples
-
- pValue() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
-
- pValue() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera
-
- pValue() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors
-
- pValue() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk
-
- pValue() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
-
- pValue() - Method in class dev.nm.stat.test.HypothesisTest
-
Get the p-value for the test statistics.
- pValue() - Method in class dev.nm.stat.test.mean.OneWayANOVA
-
- pValue() - Method in class dev.nm.stat.test.mean.T
-
- pValue() - Method in class dev.nm.stat.test.rank.KruskalWallis
-
- pValue() - Method in class dev.nm.stat.test.rank.SiegelTukey
-
- pValue() - Method in class dev.nm.stat.test.rank.VanDerWaerden
-
- pValue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
- pValue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Compute the two-sided p-value for a critical value.
- pValue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
- pValue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Compute the two-sided p-value for a critical value.
- pValue() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- pValue() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
-
- pValue() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
- pValue() - Method in class dev.nm.stat.test.variance.Bartlett
-
- pValue() - Method in class dev.nm.stat.test.variance.BrownForsythe
-
- pValue() - Method in class dev.nm.stat.test.variance.F
-
- pValue() - Method in class dev.nm.stat.test.variance.Levene
-
- pValueAlternative() - Method in class dev.nm.stat.test.distribution.AndersonDarling
-
Gets the alternative p-value (adjusted for ties).
- pvalueZ1() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
-
Get the p-value for Z1.
- pvalueZ2() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
-
Get the p-value for Z2.
- pw - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
number of each distinct eigenvalues
- pzw - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
number of eigenvalues that are less or equals to zero
- R() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
- R() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
- R() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
-
- R() - Method in interface dev.nm.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 dev.nm.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
-
the pivot row
- r - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
- r(CointegrationMLE, double) - Method in class dev.nm.stat.cointegration.JohansenTest
-
Get the (most likely) order of cointegration.
- R1Projection - Class in dev.nm.analysis.function.rn2r1
-
- R1Projection(RealVectorFunction, int) - Constructor for class dev.nm.analysis.function.rn2r1.R1Projection
-
Construct a \(R^n \rightarrow R\) projection from a \(R^n \rightarrow R^m\) function f.
- R1toConstantMatrix - Class in dev.nm.analysis.function.matrix
-
A constant matrix function maps a real number to a constant matrix: \(R^n \rightarrow A\).
- R1toConstantMatrix(Matrix) - Constructor for class dev.nm.analysis.function.matrix.R1toConstantMatrix
-
Construct a constant matrix function.
- R1toMatrix - Class in dev.nm.analysis.function.matrix
-
This is a function that maps from R1 to a Matrix space.
- R1toMatrix() - Constructor for class dev.nm.analysis.function.matrix.R1toMatrix
-
- R2() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: R-squared.
- R2toMatrix - Class in dev.nm.analysis.function.matrix
-
This is a function that maps from R2 to a Matrix space.
- R2toMatrix() - Constructor for class dev.nm.analysis.function.matrix.R2toMatrix
-
- R_bar() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets R_bar, the average of observations over time per subject.
- R_bar() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets R_bar, the average of observations over time per subject.
- RamerDouglasPeucker - Class in dev.nm.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 dev.nm.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 dev.nm.solver.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 dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin
-
Construct an instance of Rand1Bin
.
- Rand1Bin.DeRand1BinCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
-
This chromosome defines the Rand-1-Bin rule.
- RandomBetaGenerator - Interface in dev.nm.stat.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- randomDenseMatrix(int, int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- randomDOKSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- RandomExpGenerator - Interface in dev.nm.stat.random.rng.univariate.exp
-
This is a random number generator that generates random deviates according to the exponential distribution.
- RandomGammaGenerator - Interface in dev.nm.stat.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- RandomLongGenerator - Interface in dev.nm.stat.random.rng.univariate
-
A (pseudo) random number generator that generates a sequence of long
s that lack any pattern and are uniformly distributed.
- randomLowerTriangularMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- RandomNumberGenerator - Interface in dev.nm.stat.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a random symmetric, positive definite matrix.
- RandomProcess - Class in dev.nm.stat.stochasticprocess.univariate.random
-
This interface represents a univariate random process a.k.a.
- RandomProcess(TimeGrid) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
-
Construct a univariate random process.
- RandomRealizationGenerator - Interface in dev.nm.stat.stochasticprocess.univariate.random
-
This interface defines a generator to construct random realizations from a univariate stochastic process.
- RandomRealizationOfRandomProcess - Class in dev.nm.stat.stochasticprocess.univariate.random
-
This class generates random realizations from a random/stochastic process.
- RandomRealizationOfRandomProcess(RandomProcess, int, RandomLongGenerator) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from a random/stochastic process.
- RandomRealizationOfRandomProcess(RandomProcess, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from a random/stochastic process.
- RandomRealizationOfRandomProcess(DiscreteSDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from a discrete SDE.
- RandomRealizationOfRandomProcess(SDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from an SDE.
- RandomRealizationOfRandomProcess(SDE, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from an SDE.
- RandomStandardNormalGenerator - Interface in dev.nm.stat.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- randomUpperTriangularMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
- RandomVectorGenerator - Interface in dev.nm.stat.random.rng.multivariate
-
A (pseudo) multivariate random number generator samples a random vector from a multivariate distribution.
- RandomWalk - Class in dev.nm.stat.stochasticprocess.univariate.random
-
This is the Random Walk construction of a stochastic process per SDE
specification.
- RandomWalk(DiscreteSDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk
-
Constructs a univariate stochastic process from an SDE.
- RandomWalk(DiscreteSDE, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk
-
Constructs a univariate stochastic process from an SDE.
- rank() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
- rank() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
Computes the rank by counting the number of non-zero rows in R.
- rank() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
-
- rank() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get the numerical rank of A as computed by the QR decomposition.
- rank() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the rank of A.
- rank(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the numerical rank of a matrix.
- rank(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the numerical rank of a matrix.
- rank() - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Get the rank of this vector space.
- rank() - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the rank of the system, i.e., the number of (real) eigenvalues.
- Rank - Class in dev.nm.stat.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 dev.nm.stat.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- Rank(double[], double) - Constructor for class dev.nm.stat.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- Rank(double[], Rank.TiesMethod) - Constructor for class dev.nm.stat.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- Rank(double[], double, Rank.TiesMethod) - Constructor for class dev.nm.stat.descriptive.rank.Rank
-
Compute the sample ranks of the values.
- rank(int) - Method in class dev.nm.stat.descriptive.rank.Rank
-
Get the rank of the i-th element.
- Rank.TiesMethod - Enum in dev.nm.stat.descriptive.rank
-
The method for assigning ranks when some values are equal (called
'ties').
- RankOneMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.quasinewton.RankOneMinimizer
-
Construct a multivariate minimizer using the Rank One method.
- ranks() - Method in class dev.nm.stat.descriptive.rank.Rank
-
Get the ranks of the values.
- Rastrigin - Class in dev.nm.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 dev.nm.analysis.function.special.Rastrigin
-
Constructs a Rastrigin function.
- rate0 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
- rate1 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
- ratioTest(SimplexTable, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
-
This is pivot row selection (Ratio test) rule.
- ratioTest(SimplexTable, int) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
-
This is pivot row selection (Ratio test) rule.
- RayleighDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.RayleighDistribution
-
Construct a Rayleigh distribution.
- RayleighRNG - Class in dev.nm.stat.random.rng.univariate
-
This random number generator samples from the Rayleigh distribution using the inverse transform sampling method.
- RayleighRNG(double) - Constructor for class dev.nm.stat.random.rng.univariate.RayleighRNG
-
Construct a random number generator to sample from the Rayleigh distribution.
- rBar() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
-
Gets the average correlation \(\bar{r}\).
- rbind(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of vectors by rows.
- rbind(SparseVector...) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines a list of array of vectors by rows.
- rbind(Matrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of matrices by rows.
- rbind(SparseMatrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of sparse matrices by rows.
- rbinom(int, int, Vector, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates n
random binomial numbers.
- rbinom(int, int, Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates n
random binomial numbers.
- readCSV1d(String) - Static method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.number.complex.Complex
-
Get the real part of this complex number.
- Real - Class in dev.nm.number
-
A real number is an arbitrary precision number.
- Real(double) - Constructor for class dev.nm.number.Real
-
Construct a Real
from a double
.
- Real(long) - Constructor for class dev.nm.number.Real
-
Construct a Real
from an integer.
- Real(BigDecimal) - Constructor for class dev.nm.number.Real
-
Construct a Real
from a BigDecimal
.
- Real(BigInteger) - Constructor for class dev.nm.number.Real
-
Construct a Real
from a BigInteger
.
- Real(String) - Constructor for class dev.nm.number.Real
-
Construct a Real
from a String
.
- RealInterval - Class in dev.nm.interval
-
This is an interval on the real line.
- RealInterval(Double, Double) - Constructor for class dev.nm.interval.RealInterval
-
Construct an interval on the real line.
- Realization - Interface in dev.nm.stat.timeseries.datastructure.univariate.realtime
-
This is a univariate time series indexed real numbers.
- Realization.Entry - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime
-
This is the TimeSeries.Entry
for a real number -indexed univariate time series.
- realizedReturns(int) - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal
-
Gets the last H-period accumulated realized return.
- RealMatrix - Class in dev.nm.algebra.linear.matrix.generic.matrixtype
-
- RealMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- RealMatrix(Real[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- RealMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- RealScalarFunction - Interface in dev.nm.analysis.function.rn2r1
-
A real valued function a \(R^n \rightarrow R\) function, \(y = f(x_1, ..., x_n)\).
- RealScalarFunctionChromosome - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
-
This chromosome encodes a real valued function.
- RealScalarFunctionChromosome(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
Construct an instance of RealScalarFunctionChromosome
.
- RealScalarSubFunction - Class in dev.nm.analysis.function.rn2r1
-
- RealScalarSubFunction(RealScalarFunction, Map<Integer, Double>) - Constructor for class dev.nm.analysis.function.rn2r1.RealScalarSubFunction
-
Construct a scalar sub-function.
- RealVectorFunction - Interface in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from a matrix (a set of column vectors).
- RealVectorSpace(Matrix) - Constructor for class dev.nm.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 dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from a list of vectors.
- RealVectorSpace(List<Vector>) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from a list of vectors.
- RealVectorSpace(double, Vector...) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from an array of vectors.
- RealVectorSpace(Vector...) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from an array of vectors.
- RealVectorSubFunction - Class in dev.nm.analysis.function.rn2rm
-
- RealVectorSubFunction(RealVectorFunction, Map<Integer, Double>) - Constructor for class dev.nm.analysis.function.rn2rm.RealVectorSubFunction
-
Construct a vector-valued sub-function.
- reciprocal(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the reciprocals of values.
- RecursiveGridInterpolation - Class in dev.nm.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 dev.nm.analysis.curvefit.interpolation.multivariate.RecursiveGridInterpolation
-
Create an n-dimensional interpolation with a given univariate interpolation algorithm.
- reduce(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
- Reference<T> - Class in dev.nm.misc.parallel
-
- Reference() - Constructor for class dev.nm.misc.parallel.Reference
-
- reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
-
- reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4ZeroGenerator
-
- reflect(HouseholderInPlace.Householder, int, int) - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Apply the Householder matrix, H, to a column vector, x.
- reflect(Matrix) - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
- REFLECTION - Static variable in class dev.nm.stat.random.variancereduction.AntitheticVariates
-
- reflectRows(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
- reflectVectors(Vector[], int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Apply the Householder matrix, H, to an array of vectors.
- relations(Interval<T>) - Method in class dev.nm.interval.Interval
-
Determine the interval relations between this
and Y
.
- relativeError(double, double) - Static method in class dev.nm.number.DoubleUtils
-
Compute the relative error for {x1, x0}.
- RelativeTolerance - Class in dev.nm.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 dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance
-
- RelativeTolerance(double, double) - Constructor for class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance
-
Construct an instance with specified tolerance
.
- remainder() - Method in class dev.nm.analysis.function.polynomial.HornerScheme
-
Get the remainder, P(x0).
- REMAINING_ROWS_PCT_THRESHOLD - Static variable in class tech.nmfin.meanreversion.cointegration.RobustCointegration
-
- remove() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
-
Overridden to avoid the vector being altered.
- remove(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- removeActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Remove an active index.
- removeActiveByIndex(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Remove an active constraint by index.
- removeAll(Collection<?>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- removeContext() - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG
-
- removeEdge(E) - Method in interface dev.nm.graph.Graph
-
Removes an edge from this graph.
- removeEdge(E) - Method in class dev.nm.graph.type.SparseGraph
-
- removeEdge(Arc<V>) - Method in class dev.nm.graph.type.SparseTree
-
- removeEdge(Arc<VertexTree<T>>) - Method in class dev.nm.graph.type.VertexTree
-
- removeInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Remove an inactive index.
- removeInactiveByIndex(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Remove an active constraint by index.
- removeIsolatedVertices(Graph<V, E>) - Static method in class dev.nm.graph.GraphUtils
-
Removes those nodes that have no edges from a graph.
- removeMaxEdge() - Method in class dev.nm.graph.community.GirvanNewman
-
Removes the edge with the highest edge-betweeness.
- removeVertex(V) - Method in interface dev.nm.graph.Graph
-
Removes a vertex from this graph.
- removeVertex(V) - Method in class dev.nm.graph.type.SparseGraph
-
- removeVertex(V) - Method in class dev.nm.graph.type.SparseTree
-
- removeVertex(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- renameCol(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Renames column i.
- renameRow(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Renames row i.
- rep(double, int) - Static method in class dev.nm.number.DoubleUtils
-
Generates an array of double
s of repeated values.
- rep(int, int) - Static method in class dev.nm.number.DoubleUtils
-
Generates an array of int
s of repeated values.
- replaceInPlace(Matrix, int, int, int, int, Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Replaces a sub-matrix of a matrix with a smaller matrix.
- Resampler - Interface in dev.nm.stat.random.sampler.resampler
-
This is the interface of a re-sampler method.
- ResamplerModel - Interface in tech.nmfin.portfoliooptimization.lai2010.fit
-
- residuals() - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
-
Gets the residual analysis of this GLM regression.
- residuals() - Method in class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
-
Gets the residual analysis.
- residuals() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
-
- residuals() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP
-
- residuals() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
-
- residuals() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP
-
- residuals() - Method in interface dev.nm.stat.regression.linear.LinearModel
-
Gets the residual analysis of an OLS regression.
- residuals() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
-
- residuals() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
-
- residuals() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the residuals, ε, the differences between sample and fitted values.
- residuals - Variable in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- Result(NonlinearFit.Result, ACERConfidenceInterval, ACERReturnLevel, EmpiricalACER) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
-
- retainAll(Collection<?>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- ReturnLevel - Class in dev.nm.stat.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 dev.nm.stat.evt.function.ReturnLevel
-
Construct the return level function for a given univariate extreme value distribution.
- ReturnLevel(UnivariateRealFunction) - Constructor for class dev.nm.stat.evt.function.ReturnLevel
-
Construct the return level function with the inverse function of a univariate extreme value
distribution.
- ReturnPeriod - Class in dev.nm.stat.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 dev.nm.stat.evt.function.ReturnPeriod
-
Construct the return period function for a given univariate extreme value distribution.
- ReturnPeriod(UnivariateRealFunction) - Constructor for class dev.nm.stat.evt.function.ReturnPeriod
-
Construct the return period function with the cumulative distribution function of a
univariate extreme value distribution.
- returns - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- Returns - Class in tech.nmfin.returns
-
Contains utility methods related to returns computation.
- ReturnsCalculator - Interface in tech.nmfin.returns
-
This interface defines how return is computed from two values of a portfolio.
- ReturnsCalculators - Enum in tech.nmfin.returns
-
Various ways of return calculations.
- ReturnsMatrix - Class in tech.nmfin.returns
-
- ReturnsMatrix(Matrix, ReturnsCalculator) - Constructor for class tech.nmfin.returns.ReturnsMatrix
-
- ReturnsMatrix(Matrix) - Constructor for class tech.nmfin.returns.ReturnsMatrix
-
- ReturnsMoments - Class in tech.nmfin.returns.moments
-
Contains the estimated moments of asset returns.
- ReturnsMoments(Vector, Matrix) - Constructor for class tech.nmfin.returns.moments.ReturnsMoments
-
Constructs an instance.
- ReturnsMoments.Estimator - Interface in tech.nmfin.returns.moments
-
The interface to estimate moments from returns.
- ReturnsResamplerFactory - Interface in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
-
This is a factory interface to construct new instances of multivariate
resamplers.
- reverse(T[]) - Static method in class dev.nm.misc.ArrayUtils
-
Reverse an array in place.
- reverse(double...) - Static method in class dev.nm.number.DoubleUtils
-
Reverse a double
array.
- reverse(int...) - Static method in class dev.nm.number.DoubleUtils
-
Reverse an int
array.
- reverseCopy(double...) - Static method in class dev.nm.number.DoubleUtils
-
Get a reversed copy of a double
array.
- reverseCopy(int...) - Static method in class dev.nm.number.DoubleUtils
-
Get a reversed copy of a int
array.
- ReversedWeibullDistribution - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.evt.evd.univariate.ReversedWeibullDistribution
-
Create an instance with the given parameter values.
- reverseRange(double[], int, int) - Static method in class dev.nm.number.DoubleUtils
-
Reverses a range of elements in an array.
- rho() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets ρ as discussed in the reference.
- Ridders - Class in dev.nm.analysis.differentiation
-
Ridders' method computes the numerical derivative of a function.
- Ridders(UnivariateRealFunction, int, double, int) - Constructor for class dev.nm.analysis.differentiation.Ridders
-
Construct the derivative function of a univariate function using Ridder's method.
- Ridders(UnivariateRealFunction, int) - Constructor for class dev.nm.analysis.differentiation.Ridders
-
Construct the derivative function of a univariate function using Ridder's method.
- Ridders(RealScalarFunction, int[], double, int) - Constructor for class dev.nm.analysis.differentiation.Ridders
-
Construct the derivative function of a vector-valued function using Ridder's method.
- Ridders(RealScalarFunction, int[]) - Constructor for class dev.nm.analysis.differentiation.Ridders
-
Construct the derivative function of a vector-valued function using Ridder's method.
- Riemann - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.Riemann
-
Construct an integrator.
- Riemann() - Constructor for class dev.nm.analysis.integration.univariate.riemann.Riemann
-
Construct an integrator.
- rightConfidenceInterval(double) - Method in class dev.nm.stat.test.mean.T
-
Get the one sided right confidence interval, [a, ∞)
- rightConfidenceInterval(double) - Method in class dev.nm.stat.test.variance.F
-
Compute the one sided right confidence interval, [a, ∞)
- rightMultiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Right multiplication by G, namely, A * G.
- rightMultiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Right multiplication by P.
- rightMultiplyInPlace(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Right multiplication by G, namely, A * G.
- rightOneSidedPvalue() - Method in class dev.nm.stat.test.mean.T
-
Get the right, one-sided p-value.
- rightOneSidedPvalue() - Method in class dev.nm.stat.test.rank.SiegelTukey
-
Get the right, one-sided p-value.
- rightOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
Get the right, one-sided p-value.
- rightOneSidedPvalue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Compute the one-sided p-value for the statistic greater than a critical value.
- rightOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
Get the right, one-sided p-value.
- rightOneSidedPvalue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Compute the one-sided p-value for the statistic greater than a critical value.
- rightOneSidedPvalue() - Method in class dev.nm.stat.test.variance.F
-
Get the right, one-sided p-value.
- rightReflect(HouseholderInPlace.Householder, int, int) - Method in class dev.nm.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 dev.nm.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 dev.nm.misc.ArrayUtils
-
Get a right shifted array.
- rightShift(double...) - Static method in class dev.nm.number.DoubleUtils
-
Perform a in-memory right-shift (by 1 cell} to an array.
- rightShift(double[], int) - Static method in class dev.nm.number.DoubleUtils
-
Perform a in-memory right-shift (by k
cells} to an array.
- rightShiftCopy(double...) - Static method in class dev.nm.number.DoubleUtils
-
Get a right shifted (by 1 cell) copy of an array.
- rightShiftCopy(double[], int) - Static method in class dev.nm.number.DoubleUtils
-
Get a right shifted (by k
cells) copy of an array.
- Ring<R> - Interface in dev.nm.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 dev.nm.stat.random.rng
-
Provides static methods that wraps random number generators to produce synchronized generators.
- rnorm(int, RandomStandardNormalGenerator) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates n
random standard Normals.
- rnorm(int) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates n
random standard Normals.
- RNORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
-
- RntoMatrix - Interface in dev.nm.analysis.function.matrix
-
This interface is a function that maps from Rn to a Matrix space.
- RobustAdaptiveMetropolis - Class in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
-
Constructs a new instance with the given parameters.
- RobustAdaptiveMetropolis(RealScalarFunction, double, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.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 tech.nmfin.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 tech.nmfin.meanreversion.cointegration.RobustCointegration
-
- Romberg - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.newtoncotes.Romberg
-
Extend an integrator using Romberg's method.
- root() - Method in interface dev.nm.graph.RootedTree
-
Gets the root of this tree.
- root() - Method in class dev.nm.graph.type.SparseTree
-
- root() - Method in class dev.nm.graph.type.VertexTree
-
- root() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- ROOT_2 - Static variable in class dev.nm.misc.Constants
-
\(\sqrt{2}\)
- ROOT_2_PI - Static variable in class dev.nm.misc.Constants
-
\(\sqrt{2\pi}\)
- ROOT_PI - Static variable in class dev.nm.misc.Constants
-
\(\sqrt{\pi}\)
- RootedTree<V,E extends Arc<V>> - Interface in dev.nm.graph
-
A rooted tree is a directed graph, and has a root to measure distance from the
root.
- rotate(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- rotate(V) - Method in class dev.nm.graph.type.SparseTree
-
This method re-pivots the tree with a new root vertex.
- round(double, DoubleUtils.RoundingScheme) - Static method in class dev.nm.number.DoubleUtils
-
Round up or down a number to an integer.
- round(double, int) - Static method in class dev.nm.number.DoubleUtils
-
Round a number to the precision specified.
- round(Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers
-
- round(Vector) - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.IntegerConstraint
-
- round(Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers
-
- Row(S, String, double...) - Constructor for class dev.nm.stat.regression.linear.panel.PanelData.Row
-
- rowIndices() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
-
- rowMeans(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the row means.
- rowMeanVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the row mean vector of a given matrix.
- rows(Matrix, int[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Construct a sub-matrix from the rows of a matrix.
- rows(Matrix, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the rows of a matrix.
- rowSums(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the row sums.
- rowSumVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the row sum vector of a given matrix.
- RSS() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: sum of squared residuals, \(\sum \epsilon^2\).
- run(SubMatrixBlock, SubMatrixBlock, SubMatrixBlock) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.BlockWinogradAlgorithm
-
- run(SubMatrixBlock, SubMatrixBlock, SubMatrixBlock) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock.BlockAlgorithm
-
Runs the matrix multiplication task.
- run(T) - Method in interface dev.nm.misc.parallel.IterationBody
-
Execute a (parallel) task.
- run(int) - Method in interface dev.nm.misc.parallel.LoopBody
-
This method contains the code inside the for-loop, as in a native
for-loop like this:
- run() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Run the genetic algorithm.
- run(double[], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Run the analysis with single-period observations.
- run(double[][], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Run the analysis with multi-period observations.
- run() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
-
- run() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA
-
Runs the regression.
- RungeKutta - Class in dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta1
-
- RungeKutta10 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the tenth-order Runge-Kutta formula.
- RungeKutta10() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta10
-
- RungeKutta2 - Class in dev.nm.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 dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta2
-
- RungeKutta3 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the third-order Runge-Kutta formula.
- RungeKutta3() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta3
-
- RungeKutta4 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the fourth-order Runge-Kutta formula.
- RungeKutta4() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta4
-
- RungeKutta5 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the fifth-order Runge-Kutta formula.
- RungeKutta5() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta5
-
- RungeKutta6 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the sixth-order Runge-Kutta formula.
- RungeKutta6() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta6
-
- RungeKutta7 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the seventh-order Runge-Kutta formula.
- RungeKutta7() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta7
-
- RungeKutta8 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
-
This is the eighth-order Runge-Kutta formula.
- RungeKutta8() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta8
-
- RungeKuttaFehlberg - Class in dev.nm.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 dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
-
- RungeKuttaFehlberg(double, double, double) - Constructor for class dev.nm.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 dev.nm.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 dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaIntegrator
-
- RungeKuttaStepper - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
-
- RYDBERG_RINF - Static variable in class dev.nm.misc.PhysicalConstants
-
The Rydberg constant \(R_{\infty}\) in reciprocal meter (m-1).
- s() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets the value of s.
- S - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
-
This is the auxiliary helper to solve the dual problem.
- s - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
-
This is the auxiliary helper to solve the dual problem.
- s - Variable in exception dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
-
the pivot column
- S() - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Gets the original sample covariance matrix.
- S() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
-
Gets the sample covariance matrix S.
- S() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
-
Sample covariance matrix.
- s() - Method in class dev.nm.stat.descriptive.rank.Rank
-
\[ s = \sum(t_i^2 - t_i) \]
- S() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
-
Gets the covariance (or correlation) matrix.
- S() - Method in class dev.nm.stat.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 tech.nmfin.infantino2010.Infantino2010PCA.Signal
-
Predicts the accumulated H-period return at time t.
- sample() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
-
Get the sample kurtosis (biased estimator).
- sample() - Method in class dev.nm.stat.descriptive.moment.Skewness
-
Get the sample skewness (biased estimator).
- SampleAutoCorrelation - Class in dev.nm.stat.timeseries.linear.univariate.sample
-
This is the sample Auto-Correlation Function (ACF) for a univariate data set.
- SampleAutoCorrelation(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
Construct the sample ACF for a time series.
- SampleAutoCorrelation(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
Construct the sample ACF for a time series.
- SampleAutoCovariance - Class in dev.nm.stat.timeseries.linear.univariate.sample
-
This is the sample Auto-Covariance Function (ACVF) for a univariate data set.
- SampleAutoCovariance(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
-
Construct the sample ACVF for a time series.
- SampleAutoCovariance(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
-
Construct the sample ACVF for a time series.
- SampleAutoCovariance.Type - Enum in dev.nm.stat.timeseries.linear.univariate.sample
-
the available auto-covariance types
- SampleCovariance - Class in dev.nm.stat.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 dev.nm.stat.descriptive.covariance.SampleCovariance
-
Construct the covariance matrix of a matrix.
- SampleCovariance(Matrix, boolean) - Constructor for class dev.nm.stat.descriptive.covariance.SampleCovariance
-
Construct the covariance matrix of a matrix.
- SampleCovarianceEstimator() - Constructor for class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleCovarianceEstimator
-
- SampleMeanEstimator() - Constructor for class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleMeanEstimator
-
- SamplePartialAutoCorrelation - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
Construct the sample PACF for a time series.
- SamplePartialAutoCorrelation(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
Construct the sample PACF for a time series.
- scale() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow
-
Get the exponential of the coefficient.
- scale(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Scale each element in an array by a multiplier.
- scale() - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the scalings applied to each variable.
- scale() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
-
Gets the scalings applied to each variable.
- scale() - Method in class dev.nm.stat.factor.pca.PCAbySVD
-
- scaleColumn(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Scale a column:
A[, j] = c * A[, j]
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- scaled(double) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
-
Scale this matrix, A, by a constant.
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Multiply the elements in this
by a scalar, element-by-element.
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- scaled(MatrixAccess, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- scaled(MatrixAccess, double) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
c * A
- scaled(MatrixAccess, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- scaled(Real) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- scaled(Complex) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- scaled(F) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- scaled(Real) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- scaled(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Scales a vector, element-by-element.
- scaled(Vector, Real) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Scales a vector, element-by-element.
- scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- scaled(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Scale this vector by a constant, entry-by-entry.
- scaled(Real) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Scale this vector by a constant, entry-by-entry.
- scaled(F) - Method in interface dev.nm.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 dev.nm.analysis.function.polynomial.Polynomial
-
- scaled(double) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
- scaled(double[], double) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
-
- scaled(double[], double) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
-
Scale each entry of a double
array.
- scaled(double[], double) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
-
- scaled(double[], double) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
-
- scaledAlpha(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
-
Gets the scaled forward probabilities at time t.
- scaledBeta(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
-
Gets the scaled backward probabilities at time t.
- scaledBetas() - Method in class dev.nm.stat.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 dev.nm.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 dev.nm.analysis.function.polynomial.ScaledPolynomial
-
Construct a scaled polynomial, with a base of the scaling factor.
- ScaledPolynomial(Polynomial) - Constructor for class dev.nm.analysis.function.polynomial.ScaledPolynomial
-
Construct a scaled polynomial, with 2 as the base of the scaling factor.
- scaleRow(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Scale a row:
A[i, ] = c * A[i, ]
- ScientificNotation - Class in dev.nm.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 dev.nm.number.ScientificNotation
-
Construct the scientific notation of a number in this form: x = a * 10b.
- ScientificNotation(BigDecimal, int) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of a number in this form: x = a * 10b.
- ScientificNotation(BigDecimal) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of a number.
- ScientificNotation(BigInteger) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of an integer.
- ScientificNotation(long) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of a long
.
- ScientificNotation(double) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of a double
.
- scores() - Method in class dev.nm.stat.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 dev.nm.stat.factor.pca.PCA
-
Gets the scores of supplied data on the principal components.
- scoringRule() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
-
Gets the scoring rule.
- SDE - Class in dev.nm.stat.stochasticprocess.univariate.sde
-
This class represents a univariate, continuous-time Stochastic Differential Equation (SDE) of
the following form.
- SDE(Drift, Diffusion) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.SDE
-
Construct a univariate diffusion type stochastic differential equation.
- SDPDualProblem - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
-
Constructs a dual SDP problem.
- SDPDualProblem.EqualityConstraints - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
-
Constructs a primal SDP problem.
- sdPrincipalComponent(int) - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the standard deviation of the i-th principal component.
- sdPrincipalComponents() - Method in interface dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
-
This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3
version 4.
- search(Vector...) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution
-
- search(BBNode...) - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
-
- search(S...) - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod
-
Search for a solution that optimizes the objective function from the
given starting points.
- search(CentralPath...) - Method in class dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
-
- search(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- search(CentralPath) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- search() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
- search(PrimalDualSolution) - Method in class dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
Searches for a minimizer for the quadratic programming problem.
- search(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
- search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
Searches for a minimizer for the quadratic programming problem.
- search(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
Searches for a minimizer for the quadratic programming problem.
- search(QPSolution) - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- search(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- search(Vector...) - Method in class dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer.Solution
-
- search(Vector) - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
-
- search(Vector...) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- search(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
Perform a Nelder-Mead search from an initial simplex.
- search(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- search(double, double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- search(double, double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution
-
- search(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution
-
- search(double, double, double) - Method in class dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer.Solution
-
- search(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer.Solution
-
- search(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.GoldenMinimizer.Solution
-
- search(double, double, double) - Method in class dev.nm.solver.univariate.GridSearchMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- search(double, double) - Method in class dev.nm.solver.univariate.GridSearchMinimizer.Solution
-
- search(double, double, double) - Method in interface dev.nm.solver.univariate.UnivariateMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- search(double, double) - Method in interface dev.nm.solver.univariate.UnivariateMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- search(int) - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
-
Performs a search for parameters for a given id.
- search() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
-
Performs a search for parameters with no id.
- search(Ceta, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
- search(Ceta, double, double) - Method in interface tech.nmfin.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 tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
-
- search(Ceta, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
- sec(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the secant of an angle.
- sech(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the hyperbolic secant of a hyperbolic angle.
- seed(long...) - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1
-
Seed the random number generator to produce repeatable sequences.
- seed(long...) - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassSampler
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- seed(long...) - Method in class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
-
- seed(long...) - Method in class dev.nm.stat.evt.markovchain.ExtremeValueMC
-
- seed(long...) - Method in class dev.nm.stat.evt.timeseries.MARMASim
-
- seed(long...) - Method in class dev.nm.stat.hmm.HMMRNG
-
- seed(long...) - Method in class dev.nm.stat.markovchain.SimpleMC
-
- seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator
-
- seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG
-
Delegate to the underlying random long generator.
- seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRNG
-
Delegate to the underlying random number generator.
- seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRVG
-
Delegate to the underlying random vector generator.
- seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.BurnInRVG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.HypersphereRVG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.IID
-
- seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
-
- seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
-
- seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.MultinomialRVG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.NormalRVG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.ThinRVG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox
-
- SEED - Static variable in class dev.nm.stat.random.rng.RNGUtils
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.beta.Cheng1978
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969
-
Deprecated.
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.BinomialRNG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.BurnInRNG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.exp.Ziggurat2000Exp
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.InverseTransformSampling
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.LogNormalRNG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.BoxMuller
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.NormalRNG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.Zignor2005
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.poisson.Knuth1969
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.ThinRNG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
-
Seed the random number/vector/scenario generator to produce repeatable experiments.
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.MWC8222
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3
-
- seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
-
- seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
- seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
-
- seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
-
- seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
-
- seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.multivariate.GroupResampler
-
- seed(long...) - Method in interface dev.nm.stat.random.Seedable
-
Seed the random number/vector/scenario generator to produce repeatable experiments.
- seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG
-
- seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
- seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
- seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomWalk
-
- seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
-
- seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
- seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk
-
- seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
-
- seed(long...) - Method in class dev.nm.stat.test.distribution.pearson.AS159
-
- seed(long...) - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
-
- seed(long...) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
-
- seed(long...) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
- seed(long...) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
-
- Seedable - Interface in dev.nm.stat.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 dev.nm.number.DoubleUtils
-
Select the array elements which satisfy the boolean
test.
- select(int[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils
-
Select the array elements which satisfy the boolean
test.
- select(GLMProblem, Matrix, int[]) - Method in interface dev.nm.stat.regression.linear.glm.modelselection.ForwardSelection.Step
-
- select(GLMProblem, Matrix, int[]) - Method in class dev.nm.stat.regression.linear.glm.modelselection.SelectionByAIC
-
- select(GLMProblem, Matrix, int[]) - Method in class dev.nm.stat.regression.linear.glm.modelselection.SelectionByZValue
-
- SelectionByAIC - Class in dev.nm.stat.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 dev.nm.stat.regression.linear.glm.modelselection.SelectionByAIC
-
- SelectionByZValue - Class in dev.nm.stat.regression.linear.glm.modelselection
-
In each step, the most significant factor is added, until all remaining factors are
insignificant.
- SelectionByZValue(double) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.SelectionByZValue
-
Creates an instance with the given significance level [0, 1].
- SemiImplicitExtrapolation - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation
-
- SemiImplicitExtrapolation(double, int) - Constructor for class dev.nm.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 dev.nm.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 dev.nm.number.DoubleUtils
-
Generates a sequence of double
s from from
up to
to
with increments inc
.
- seq(double, double, int) - Static method in class dev.nm.number.DoubleUtils
-
Generate a sequence of n
equi-spaced double
values, from
start
to end
(inclusive).
- seq(int, int, int) - Static method in class dev.nm.number.DoubleUtils
-
Generates a sequence of int
s from from
up to to
with increments inc
.
- seq(int, int) - Static method in class dev.nm.number.DoubleUtils
-
Generates a sequence of int
s from from
up to to
with increments 1.
- Sequence - Interface in dev.nm.analysis.sequence
-
A sequence is an ordered list of (real) numbers.
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- set(int, int, double) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixAccess
-
Set the matrix entry at [i,j] to a value.
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- set(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Deprecated.
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- set(int, int, F) - Method in interface dev.nm.algebra.linear.matrix.generic.GenericMatrixAccess
-
Set the matrix entry at [i,j] to a value.
- set(int, int, Complex) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- set(int, int, F) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- set(int, int, Real) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
-
Deprecated.
- set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- set(int, DenseVector) - Method in class dev.nm.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 dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
This method is overridden to always throw VectorAccessException
.
- set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
Deprecated.
- set(int, double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Change the value of an entry in this vector.
- set(int, int, double) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
- set(T, int...) - Method in class dev.nm.misc.datastructure.MultiDimensionalArray
-
- set(T, int...) - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection
-
Replaces the element at the specified position in this list with the specified element.
- set(int, int) - Method in class dev.nm.misc.datastructure.SortableArray
-
- set(T) - Method in class dev.nm.misc.parallel.Reference
-
- set(RealScalarFunction, EqualityConstraints) - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
-
Associate this variation to a particular general constrained minimization
problem.
- setCachingCriticalLine(boolean) - Method in class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
-
Sets the algorithm to compute and cache the whole critical line, so that
optimal weights can be computed as quick as a linear search for turning
points on the line.
- setColumn(int, double...) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Set the values for a column in the matrix, i.e., [*, j].
- setColumn(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Set the values for a column in the matrix, i.e., [*, j].
- setColumn(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Changes the matrix column values to a vector value.
- setContext(long) - Method in class dev.nm.stat.random.rng.concurrent.context.ThreadIDRNG
-
Sets the context of this thread.
- setDeltaT(double) - Method in class dev.nm.stat.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 dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution
-
Gives the domain values for the brute force search to try.
- setDt(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Set the current time differential.
- setDt(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
-
- setDt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Set the current time differential.
- setDt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
-
- setFt(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_BtDt
-
- setFt(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt
-
- setFt(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
-
Set the filtration for this function.
- setInitials(BBNode...) - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
-
- setInitials(S...) - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod
-
Supply the starting points for the search.
- setInitials(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- setInitials(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- setInitials(PrimalDualSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
- setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
No need to set initials for the dual problem.
- setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- setInitials(Vector...) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
Supply the starting points for the search.
- setInitials(Vector...) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- setInitials(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
- setInitials(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- setLicenseFile(File) - Static method in class dev.nm.misc.license.License
-
Overrides the default license file.
- setLicenseKey(String) - Static method in class dev.nm.misc.license.License
-
Sets the license key for this invocation.
- setParent(BFS.Node<V>) - Method in class dev.nm.graph.algorithm.traversal.BFS.Node
-
- setParent(V) - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
-
Sets the parent of the node.
- setPopulation(List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
- setPopulation(List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Set the current generation.
- setRiskAversionCoefficient(double) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Sets the risk aversion coefficient, effectively moving along the
efficient frontier.
- setRow(int, double...) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Set the values for a row in the matrix, i.e., [i, *].
- setRow(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
Set the values for a row in the matrix, i.e., [i, *].
- setRow(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Changes the matrix row values to a vector value.
- setVisitTime(int) - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
-
- setXt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Set the current value of the stochastic process.
- setXt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Set the current value of the stochastic process.
- setZt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Set the current value of the Gaussian innovation.
- setZt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
-
- setZt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Set the current value of the Gaussian innovation.
- setZt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
-
- ShapiroWilk - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Construct a Shapiro-Wilk distribution.
- shellsort(double...) - Static method in class dev.nm.number.DoubleUtils
-
Sort an array using Shell sort.
- ShortestPath<V> - Interface in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized
-
- SHR0 - Class in dev.nm.stat.random.rng.univariate.uniform
-
SHR0 is a simple uniform random number generator.
- SHR0() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.SHR0
-
- SHR3 - Class in dev.nm.stat.random.rng.univariate.uniform
-
SHR3 is a 3-shift-register generator with period 2^32-1.
- SHR3() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.SHR3
-
- shutdown() - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Shuts down the executor gracefully.
- SiegelTukey - Class in dev.nm.stat.test.rank
-
The Siegel-Tukey test tests for differences in scale (variability) between two groups.
- SiegelTukey(double[], double[], double, boolean) - Constructor for class dev.nm.stat.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 dev.nm.stat.test.rank.SiegelTukey
-
Perform the Siegel-Tukey test to test for differences in scale (variability) between two
groups.
- SiegelTukey(double[], double[]) - Constructor for class dev.nm.stat.test.rank.SiegelTukey
-
Perform the Siegel-Tukey test to test for differences in scale (variability) between two
groups.
- sigma() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- sigma(double, double) - Method in class dev.nm.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 dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- Sigma() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
-
- Sigma() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPRiskConstraint
-
- sigma - Variable in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
-
the standard deviation
- sigma() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
-
Get the diffusion matrix: \(\sigma(t, X_t, Z_t, ...)\).
- sigma() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
-
- sigma() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUProcess
-
Get the volatility.
- sigma() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the white noise covariance matrix.
- sigma() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the white noise covariance matrix.
- sigma() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the white noise variance.
- sigma - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
-
- sigma() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
Gets the diffusion volatility.
- sigma2(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Computes the conditional variance based on the past information.
- sigma2(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
-
Computes the conditional variance based on the past information.
- sigma2(double[], double[]) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Compute the conditional variance based on the past information.
- sigma2 - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- sigma2() - Method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel
-
Gets the conditional variances of residuals over time.
- sigma2() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit
-
- sigma2() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit
-
- sigma_ij(int, int) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
-
Deprecated.
- sigma_ij(int, int) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
-
Get the Ft adapted function the D[i,j] entry in the diffusion matrix.
- sign() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Gets the sign of the permutation matrix which is also the determinant.
- significand() - Method in class dev.nm.number.ScientificNotation
-
Get the significand.
- signum(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the signs of values.
- SimilarMatrix - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.SimilarMatrix
-
Constructs the similar matrix B = P-1AP.
- simObs(int) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Simulates a sequence of observations per the model specification.
- SimpleAnnealingFunction - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction
-
- SimpleAR1Fit - Class in tech.nmfin.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 tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit
-
- SimpleAR1Moments - Class in tech.nmfin.portfoliooptimization.lai2010.fit
-
- SimpleAR1Moments() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Moments
-
- SimpleArc<V> - Class in dev.nm.graph.type
-
A simple arc has two vertices: head and tail.
- SimpleArc(V, V, double) - Constructor for class dev.nm.graph.type.SimpleArc
-
Construct a simple arc.
- SimpleArc(V, V) - Constructor for class dev.nm.graph.type.SimpleArc
-
Construct a simple arc.
- SimpleCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
-
- SimpleCellFactory - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
-
- SimpleCellFactory(double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
-
Construct an instance of a SimpleCellFactory
.
- SimpleCellFactory.SimpleCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
-
A SimpleCell
implements the two genetic operations.
- SimpleDoubleArrayOperation - Class in dev.nm.number.doublearray
-
This is a simple, single-threaded implementation of the array math operations.
- SimpleDoubleArrayOperation() - Constructor for class dev.nm.number.doublearray.SimpleDoubleArrayOperation
-
- SimpleEdge<V> - Class in dev.nm.graph.type
-
A simple edge has two vertices.
- SimpleEdge(V, V, double) - Constructor for class dev.nm.graph.type.SimpleEdge
-
Construct a simple edge.
- SimpleEdge(V, V) - Constructor for class dev.nm.graph.type.SimpleEdge
-
Construct a simple edge.
- SimpleGARCHFit - Class in tech.nmfin.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 tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit
-
- SimpleGARCHMoments1 - Class in tech.nmfin.portfoliooptimization.lai2010.fit
-
Estimates the moments by GARCH model.
- SimpleGARCHMoments1() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments1
-
- SimpleGARCHMoments2 - Class in tech.nmfin.portfoliooptimization.lai2010.fit
-
Estimates the moments by GARCH model.
- SimpleGARCHMoments2(GARCHResamplerFactory2) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments2
-
- SimpleGridMinimizer - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
-
This minimizer is a simple global optimization method.
- SimpleGridMinimizer(SimpleGridMinimizer.NewCellFactoryCtor, RandomLongGenerator, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
Construct a SimpleGridMinimizer
to solve unconstrained minimization problems.
- SimpleGridMinimizer(RandomLongGenerator, double, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
Construct a SimpleGridMinimizer
to solve unconstrained minimization problems.
- SimpleGridMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
Construct a SimpleGridMinimizer
to solve unconstrained minimization problems.
- SimpleGridMinimizer.NewCellFactoryCtor - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
-
This factory constructs a new SimpleCellFactory
for each minimization problem.
- SimpleGridMinimizer.Solution - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
-
This is the solution to a minimization problem using SimpleGridMinimizer
.
- SimpleMatrixMathOperation - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation
-
This is a generic, single-threaded implementation of matrix math operations.
- SimpleMatrixMathOperation() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- SimpleMC - Class in dev.nm.stat.markovchain
-
This is a time-homogeneous Markov chain with a finite state space.
- SimpleMC(Vector, Matrix) - Constructor for class dev.nm.stat.markovchain.SimpleMC
-
Constructs a time-homogeneous Markov chain with a finite state space.
- SimpleTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
-
Abstract class for the common case where \(T^V_t = T^A_t\).
- SimpleTemperatureFunction() - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
-
- SimpleTimeSeries - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime
-
This simple univariate time series simply wraps a double[]
to form a time series.
- SimpleTimeSeries(double[]) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of SimpleTimeSeries
.
- SimplexCuttingPlaneMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer
-
Construct a cutting-plane minimizer to solve an MILP problem.
- SimplexCuttingPlaneMinimizer.CutterFactory - Interface in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
-
This factory constructs a new Cutter
for each MILP problem.
- SimplexCuttingPlaneMinimizer.CutterFactory.Cutter - Interface in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
-
the pivot
- SimplexTable - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a canonical linear programming problem.
- SimplexTable(LPCanonicalProblem1) - Constructor for class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a general linear programming problem.
- SimplexTable(LPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Construct a simplex table from a general linear programming problem.
- SimplexTable(SimplexTable) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
-
Copy constructor.
- SimplexTable.Label - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
- SimplexTable.LabelType - Enum in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
- simplify(PolygonalChain) - Method in class dev.nm.geometry.polyline.RamerDouglasPeucker
-
Simplify the given polygonal chain.
- Simpson - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
-
Construct an integrator that implements Simpson's rule.
- simStates(int) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Simulates a sequence of states per the model specification.
- SimulatedAnnealingMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
-
Constructs a new instance.
- SimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
-
- sin(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the sine of a vector, element-by-element.
- sin(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Sine of a complex number.
- sinc(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the unnormalized sinc function of an angle.
- SingularValueByDQDS - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds
-
Computes all the singular values of a bidiagonal matrix.
- SingularValueByDQDS(BidiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.SingularValueByDQDS
-
Computes all the singular values of a bidiagonal matrix.
- sinh(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Hyperbolic sine of a complex number.
- size() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Get the number of distinct eigenvalues.
- size() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
-
- size() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Gets the number of variables in the linear system.
- size() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- size() - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
-
- size() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- size() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- size() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- size() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Get the length of this vector.
- size(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
-
- size(int) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
-
Get the size of the grid in the given dimension xi.
- size(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
-
- size() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
-
- size() - Method in interface dev.nm.analysis.function.tuple.OrderedPairs
-
Get the number of points.
- size() - Method in class dev.nm.analysis.function.tuple.PartialFunction
-
- size() - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs
-
- size() - Method in class dev.nm.interval.Intervals
-
Get the number of disjoint intervals.
- size() - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- size(int) - Method in class dev.nm.misc.datastructure.MultiDimensionalArray
-
- size(int) - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection
-
Returns the size of the collection along the given dimension.
- size() - Method in interface dev.nm.solver.multivariate.constrained.constraint.Constraints
-
Get the number of constraints.
- size() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
-
- size() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
- size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
-
- size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
-
- size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Gets the number of constraints.
- size() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
-
- size() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Get T, the number of hidden states or observations.
- size() - Method in class dev.nm.stat.dlm.univariate.DLMSeries
-
- size() - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Get T, the number of hidden states or observations.
- size - Variable in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
-
the size
- size() - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Get the number of rows in the panel.
- size() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid
-
- size() - Method in interface dev.nm.stat.stochasticprocess.timegrid.TimeGrid
-
Get the number of time points.
- size() - Method in class dev.nm.stat.stochasticprocess.timegrid.UnitGrid
-
- size() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the length of the history.
- size() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries
-
- size() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- size() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- size() - Method in interface dev.nm.stat.timeseries.datastructure.TimeSeries
-
Get the length of the time series.
- size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
-
- sizeX() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
-
- sizeX() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
-
Define the size of the grid along the x-axis.
- sizeX() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
- sizeY() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
-
- sizeY() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
-
Define the size of the grid along the y-axis.
- sizeY() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
- Sk - Variable in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
-
This is the approximate inverse of the Hessian matrix.
- skew() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
Gets the skewness of this distribution.
- skew() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
-
- skew() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
Gets the skewness of this distribution.
- skew() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.TDistribution
-
Gets the skewness of this distribution.
- skew() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
- skew() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
-
- skew() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
- skew() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
- skew() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
-
- skew() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
- skew() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
- skew() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- skew() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- skew() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- skew() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- skew() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Deprecated.
- skew() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Deprecated.
- Skewness - Class in dev.nm.stat.descriptive.moment
-
Skewness is a measure of the asymmetry of the probability distribution.
- Skewness() - Constructor for class dev.nm.stat.descriptive.moment.Skewness
-
Construct an empty Skewness
calculator.
- Skewness(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Skewness
-
Construct a Skewness
calculator,
initialized with a sample.
- Skewness(Skewness) - Constructor for class dev.nm.stat.descriptive.moment.Skewness
-
Copy constructor.
- SmallestSubscriptRule - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule
-
- SOCPDualProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
This is the Dual Second Order Conic Programming problem.
- SOCPDualProblem(Vector, Matrix[], Vector[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Constructs a dual SOCP problem.
- SOCPDualProblem(SOCPDualProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Copy constructor.
- SOCPDualProblem.EqualityConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
- SOCPGeneralConstraint - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
This represents the SOCP general constraint of this form.
- SOCPGeneralConstraint(Matrix, Vector, Vector, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Constructs a SOCP general constraint.
- SOCPGeneralConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
This represents a set of SOCP general constraints of this form.
- SOCPGeneralConstraints() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Constructs a set of SOCP general constraints.
- SOCPGeneralConstraints(List<SOCPGeneralConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Constructs a set of SOCP general constraints.
- SOCPGeneralConstraints(SOCPGeneralConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
-
Constructs a set of SOCP general constraints.
- SOCPGeneralProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
Many convex programming problems can be represented in the following form.
- SOCPGeneralProblem(SOCPGeneralProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
-
Copy constructor.
- SOCPGeneralProblem(Vector, SOCPGeneralConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
-
Construct a general Second Order Conic Programming problem.
- SOCPGeneralProblem(Vector, List<SOCPGeneralConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
-
Construct a general Second Order Conic Programming problem.
- SOCPMaximumLoan - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
Transforms a maximum loan constraint into the compact SOCP form.
- SOCPMaximumLoan(Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
Constructs a maximum loan constraint.
- SOCPMaximumLoan(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
Constructs a maximum loan constraint.
- SOCPNoTradingList1 - Class in tech.nmfin.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 tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
Constructs a black list constraint.
- SOCPNoTradingList1(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
Constructs a black list constraint.
- SOCPNoTradingList1(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
Constructs a black list constraint.
- SOCPNoTradingList2 - Class in tech.nmfin.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 tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
Constructs a black list constraint.
- SOCPNoTradingList2(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
Constructs a black list constraint.
- SOCPPortfolioConstraint - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
- SOCPPortfolioConstraint.ConstraintViolationException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Exception thrown when a constraint is violated.
- SOCPPortfolioConstraint.Variable - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
the variables involved in SOCPGeneralConstraints
- SOCPPortfolioObjectiveFunction - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Constructs the objective function for portfolio optimization.
- SOCPPortfolioObjectiveFunction(Matrix, double[], SOCPRiskConstraint, SOCPPortfolioConstraint) - Constructor for class dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Constructs an SOCP problem for portfolio optimization.
- SOCPPortfolioProblem(SOCPPortfolioObjectiveFunction, SOCPPortfolioConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Constructs an SOCP problem for portfolio optimization.
- SOCPPortfolioProblem(SOCPPortfolioObjectiveFunction, Collection<SOCPPortfolioConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
-
Constructs an SOCP problem for portfolio optimization.
- SOCPRiskConstraint - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
- SOCPRiskConstraint() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPRiskConstraint
-
- SOCPSectorExposure - Class in tech.nmfin.portfoliooptimization.socp.constraints.ybar
-
Transforms a sector exposure constraint into the compact SOCP form.
- SOCPSectorExposure(Vector, Vector[], Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
Constructs a sector exposure constraint.
- SOCPSectorExposure(Vector, Vector[], Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
Constructs a sector exposure constraint.
- SOCPSectorNeutrality - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
Transforms a sector neutral constraint into the compact SOCP form.
- SOCPSectorNeutrality(Vector, Vector[], double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
Constructs a sector neutral constraint.
- SOCPSectorNeutrality(Vector, Vector[]) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
Constructs a sector neutral constraint.
- SOCPSelfFinancing - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
Transforms a self financing constraint into the compact SOCP form.
- SOCPSelfFinancing(Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
Constructs a zero value constraint.
- SOCPSelfFinancing(Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
Constructs a zero value constraint.
- SOCPZeroValue - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
Transforms a zero value constraint into the compact SOCP form.
- SOCPZeroValue(Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
-
Constructs a zero value constraint.
- SOCPZeroValue(Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
-
Constructs a zero value constraint.
- solution() - Method in interface dev.nm.misc.algorithm.bb.BBNode
-
the solution to the sub-problem associated with this node
- Solution(PrimalDualPathFollowingMinimizer, SDPDualProblem, double, double) - Constructor for class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- Solution(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
- solution() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
- Solution(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
-
- Solution(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- Solution(UnivariateRealFunction) - Constructor for class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
- solve(UpperTriangularMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
Solve Ux = b.
- solve(UpperTriangularMatrix, Vector, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
- solve(UpperTriangularMatrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
- solve(UpperTriangularMatrix, Matrix, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
-
- solve(LowerTriangularMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
Solve Lx = b.
- solve(LowerTriangularMatrix, Vector, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
- solve(LowerTriangularMatrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
- solve(LowerTriangularMatrix, Matrix, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
-
- solve(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver
-
Get a particular solution for the linear system, Ax = b.
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver
-
Solve Ax = b.
- solve(Matrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver
-
Solves AX = B.
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverByQR
-
In the ordinary least square sense, solve
Ax = y
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverBySVD
-
In the ordinary least square sense, solve
Ax = y
- solve(TridiagonalMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ThomasAlgorithm
-
Solves a tridiagonal matrix equation.
- solve(LSProblem, IterationMonitor<Vector>) - Method in interface dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
- solve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
-
Return the input vector x.
- solve(Vector) - Method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner
-
Solve Mv = x, where M is the preconditioner matrix.
- solve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
-
Solve Mz = x using this SSOR preconditioner.
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
- solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
- solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.BurlischStoerExtrapolation
-
Perform the extrapolation.
- solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation
-
- solve(ODE1stOrderWith2ndDerivative) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation
-
- solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.AdamsBashforthMoulton
-
Solve an ODE using the Adams-Bashforth-Moulton method.
- solve(ODE1stOrder) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolver
-
Solves the given ODE problem.
- solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta
-
Solves a first order ODE.
- solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
-
Solve the given ODE using Runge-Kutta-Fehlberg method.
- solve(PoissonEquation2D, int, int) - Method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D
-
Solves a 1 dimensional convection-diffusion equation.
- solve(HeatEquation1D, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D
-
Solves the given one-dimensional heat equation.
- solve(HeatEquation2D, int, int, int) - Method in class dev.nm.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 dev.nm.analysis.function.polynomial.root.CubicRoot
-
Solve \(ax^3 + bx^2 + cx + d = 0\).
- solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.jenkinstraub.JenkinsTraubReal
-
Solve a polynomial equation.
- solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.LinearRoot
-
Solve ax + b = 0.
- solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.PolyRoot
-
Get the roots/zeros of a polynomial.
- solve(Polynomial) - Method in interface dev.nm.analysis.function.polynomial.root.PolyRootSolver
-
- solve(Polynomial, double) - Method in class dev.nm.analysis.function.polynomial.root.QuadraticRoot
-
Solve \(ax^2 + bx + c = 0\).
- solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuadraticRoot
-
- solve(double, double, double, double, double) - Method in interface dev.nm.analysis.function.polynomial.root.QuarticRoot.QuarticSolver
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(Polynomial) - Method in class dev.nm.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 dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
-
- solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFormula
-
- solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFormula
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(RealVectorFunction, Vector) - Method in class dev.nm.analysis.root.multivariate.NewtonSystemRoot
-
Searches for a root, x such that f(x) = 0.
- solve(RealScalarFunction[], Vector) - Method in class dev.nm.analysis.root.multivariate.NewtonSystemRoot
-
Searches for a root, x such that f(x) = 0.
- solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.BisectionRoot
-
- solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.BrentRoot
-
- solve(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.root.univariate.BrentRoot
-
- solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.HalleyRoot
-
- solve(UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.HalleyRoot
-
- solve(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, double) - Method in class dev.nm.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 dev.nm.analysis.root.univariate.NewtonRoot
-
- solve(UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.NewtonRoot
-
- solve(UnivariateRealFunction, UnivariateRealFunction, double) - Method in class dev.nm.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 dev.nm.analysis.root.univariate.Uniroot
-
Search for a root, x, in the interval [lower, upper] such that f(x) = 0.
- solve(SDPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
-
- solve(SDPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
-
- solve(SDPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
-
- solve(SOCPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer
-
- solve(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
- solve(LPCanonicalProblem1) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
-
- solve(LPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
-
- solve(LPRevisedSimplexSolver.Problem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
-
- solve(SimplexTable) - Method in interface dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
- solve(LPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
-
- solve(QuadraticFunction, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
-
Solves an unconstrained quadratic programming problem of this form.
- solve(QuadraticFunction) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
-
Solves an unconstrained quadratic programming problem of this form.
- solve(QuadraticFunction, LinearEqualityConstraints, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
-
Solves a quadratic programming problem subject to equality constraints.
- solve(QuadraticFunction, LinearEqualityConstraints) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
-
Solves a quadratic programming problem subject to equality constraints.
- solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer
-
- solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer
-
- solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer
-
- solve(BoxOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
-
- solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
-
- solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
-
- solve(RealScalarFunction, EqualityConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
-
Minimize a function subject to only equality constraints.
- solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
- solve(RealScalarFunction, RealVectorFunction, GreaterThanConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
-
Minimize a function subject to only inequality constraints.
- solve(RealScalarFunction, GreaterThanConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
-
Minimize a function subject to only inequality constraints.
- solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
-
- solve(BruteForceIPProblem) - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
-
- solve(ILPProblem) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer
-
- solve(ILPProblem) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer
-
- solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
-
- solve(ConstrainedOptimSubProblem) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
-
- solve(ConstrainedOptimProblem, Map<Integer, Double>) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
-
Solves a constrained sub-problem by specifying the fixing explicitly.
- solve(OptimProblem) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
-
- solve(OptimProblem) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
- solve(MinMaxProblem<T>) - Method in class dev.nm.solver.multivariate.minmax.LeastPth
-
- solve(OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
-
- solve(Function<Vector, R>) - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ConjugateGradientMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.FletcherReevesMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.linesearch.FletcherLineSearch
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.HuangMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent
-
- solve(RealVectorFunction, RntoMatrix) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
-
Solve the minimization problem to minimize F = vf' * vf.
- solve(RealVectorFunction) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
-
Solve the minimization problem to minimize F = vf' * vf.
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
Solve a minimization problem with a C2 objective function.
- solve(OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.DoubleBruteForceMinimizer
-
- solve(P) - Method in interface dev.nm.solver.Optimizer
-
- solve(UnivariateRealFunction) - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer
-
Minimize a univariate function.
- solve(C2OptimProblem) - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.univariate.bracketsearch.GoldenMinimizer
-
- solve(C2OptimProblem) - Method in class dev.nm.solver.univariate.GridSearchMinimizer
-
- solve(UnivariateRealFunction) - Method in class dev.nm.solver.univariate.GridSearchMinimizer
-
Minimizes a univariate function.
- solve(Ceta) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
- solve(Ceta) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
-
- solve(Ceta) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
-
- solve() - Method in class tech.nmfin.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).
- SomeIntegers(IPProblem) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers
-
Construct the integral constraint from an Integer Programming problem.
- SORSweep - Class in dev.nm.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 dev.nm.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 dev.nm.misc.datastructure
-
These arrays can be sorted according to the dictionary order.
- SortableArray(int) - Constructor for class dev.nm.misc.datastructure.SortableArray
-
- SortableArray(int...) - Constructor for class dev.nm.misc.datastructure.SortableArray
-
- SortableArray(List<Integer>) - Constructor for class dev.nm.misc.datastructure.SortableArray
-
- SortedOrderedPairs - Class in dev.nm.analysis.function.tuple
-
The ordered pairs are first sorted by abscissa, then by ordinate.
- SortedOrderedPairs(OrderedPairs) - Constructor for class dev.nm.analysis.function.tuple.SortedOrderedPairs
-
- SortedOrderedPairs(double[], double[]) - Constructor for class dev.nm.analysis.function.tuple.SortedOrderedPairs
-
- sortInColumnOrder(List<SparseMatrix.Entry>, int) - Static method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.graph.type
-
This class implements the sparse directed acyclic graph representation.
- SparseDAGraph(boolean) - Constructor for class dev.nm.graph.type.SparseDAGraph
-
Construct an empty directed acyclic graph.
- SparseDAGraph() - Constructor for class dev.nm.graph.type.SparseDAGraph
-
Construct an empty directed acyclic graph.
- SparseDAGraph(DAGraph<V, E>) - Constructor for class dev.nm.graph.type.SparseDAGraph
-
(Copy) construct a directed acyclic graph from another directed acyclic graph.
- SparseDiGraph<V,E extends Arc<V>> - Class in dev.nm.graph.type
-
This class implements the sparse directed graph representation.
- SparseDiGraph() - Constructor for class dev.nm.graph.type.SparseDiGraph
-
Construct an empty graph.
- SparseDiGraph(DiGraph<V, E>) - Constructor for class dev.nm.graph.type.SparseDiGraph
-
(Copy) construct a graph from another graph.
- SparseGraph<V,E extends HyperEdge<V>> - Class in dev.nm.graph.type
-
This class implements the sparse graph representation.
- SparseGraph() - Constructor for class dev.nm.graph.type.SparseGraph
-
Construct an empty graph.
- SparseGraph(Graph<V, E>) - Constructor for class dev.nm.graph.type.SparseGraph
-
(Copy) construct a graph from another graph.
- SparseMatrix - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
A sparse matrix stores only non-zero values.
- SparseMatrix.Entry - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
This is a (non-zero) entry in a sparse matrix.
- SparseMatrix.Entry.TopLeftFirstComparator - Enum in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
- SparseMatrixUtils - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
- SparseStructure - Interface in dev.nm.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 dev.nm.graph.type
-
This class implements the sparse tree representation.
- SparseTree(V) - Constructor for class dev.nm.graph.type.SparseTree
-
Construct a tree with a root.
- SparseUnDiGraph<V,E extends UndirectedEdge<V>> - Class in dev.nm.graph.type
-
This class implements the sparse undirected graph representation.
- SparseUnDiGraph() - Constructor for class dev.nm.graph.type.SparseUnDiGraph
-
Construct an empty graph.
- SparseUnDiGraph(UnDiGraph<V, E>) - Constructor for class dev.nm.graph.type.SparseUnDiGraph
-
(Copy) construct a graph from another graph.
- SparseVector - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
A sparse vector stores only non-zero values.
- SparseVector(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector.
- SparseVector(int, int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector.
- SparseVector(int, List<SparseVector.Entry>) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector.
- SparseVector(double...) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector from a double[]
.
- SparseVector(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector from a vector.
- SparseVector(SparseVector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Copy constructor.
- SparseVector.Entry - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
- SparseVector.Entry.Comparator - Enum in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
This wrapper class overrides the
Iterator.remove()
method to throw an exception when called.
- SpearmanRankCorrelation - Class in dev.nm.stat.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 dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
-
Construct a Spearman rank calculator initialized with two samples.
- spectralDensity(double) - Method in interface dev.nm.stat.evt.evd.bivariate.BivariateEVD
-
The density \(h\) of the spectral measure \(H\) on the interval (0,1).
- spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
-
- spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
-
- spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
-
- spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
-
- spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
-
- spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
-
- spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
-
- spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
-
- spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
-
- Spectrum - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
-
A spectrum is the set of eigenvalues of a matrix.
- spread - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- spread() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
S = A - bB
- SQPActiveSetMinimizer - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
-
Construct an SQP Active Set minimizer to solve general minimization
problems with inequality constraints.
- SQPActiveSetMinimizer.Solution - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset
-
This factory constructs a new instance of SQPASVariation
for each
SQP problem.
- SQPActiveSetOnlyEqualityConstraint1Minimizer - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
-
This factory constructs a new instance of SQPASEVariation
for each SQP problem.
- SQPActiveSetOnlyEqualityConstraint2Minimizer - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
-
- SQPActiveSetOnlyEqualityConstraint2Minimizer(double, int) - Constructor for class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint2Minimizer
-
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
- SQPActiveSetOnlyInequalityConstraintMinimizer - Class in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Construct a variation.
- SQPASEVariation1() - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Construct a variation.
- SQPASEVariation2 - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
-
Construct a variation.
- SQPASEVariation2() - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
-
Construct a variation.
- SQPASVariation - Interface in dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
-
Construct a variation.
- sqrt(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the square roots of a vector, element-by-element.
- sqrt(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Square root of a complex number.
- sqrt(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the square roots of values.
- squared(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the squares of a vector, element-by-element.
- squareQ() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
Get the square Q matrix.
- squareQ() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
- squareQ() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
-
- squareQ() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get the square Q matrix.
- SSORPreconditioner - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
-
Construct an SSOR preconditioner with a symmetric coefficient matrix.
- StandardCumulativeNormal - Interface in dev.nm.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 dev.nm.stat.descriptive.moment.Variance
-
Get the standard deviation of the sample,
which is the square root of the variance.
- standardError() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
-
Get the standard errors of the fitted parameters.
- StandardInterval - Class in dev.nm.analysis.integration.univariate.riemann.substitution
-
This transformation is for mapping integral region from [a, b] to [-1, 1].
- StandardInterval(double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
-
Construct a StandardInterval
substitution rule.
- standardized() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
standard residual = residual / v1 / sqrt(RSS / (n-m))
- StandardNormalRNG - Class in dev.nm.stat.random.rng.univariate.normal
-
An alias for
Zignor2005
to provide a default implementation for sampling from the
standard Normal distribution.
- StandardNormalRNG() - Constructor for class dev.nm.stat.random.rng.univariate.normal.StandardNormalRNG
-
- standarizedInnovations() - Method in interface tech.nmfin.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 tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit
-
- standarizedInnovations() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit
-
- state - Variable in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim.Innovation
-
the simulated state
- state - Variable in class dev.nm.stat.dlm.univariate.DLMSim.Innovation
-
the simulated state
- state() - Method in class dev.nm.stat.hmm.HmmInnovation
-
Get the hidden state.
- state() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Gets the current system configuration.
- state() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
-
UP trend is -ve; DOWN trend is +ve.
- StateEquation - Class in dev.nm.stat.dlm.univariate
-
This is the state equation in a controlled dynamic linear model.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Construct a state equation.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Construct a state equation.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Construct a state equation without control variables.
- StateEquation(double, double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Construct a time-invariant state equation.
- StateEquation(double, double) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Construct a time-invariant state equation without control variables.
- StateEquation(StateEquation) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Copy constructor.
- Statistic - Interface in dev.nm.stat.descriptive
-
A statistic (singular) is a single measure of some attribute of a sample (e.g., its arithmetic mean value).
- StatisticFactory - Interface in dev.nm.stat.descriptive
-
- statistics() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
-
Get the test statistics of the factor analysis.
- statistics() - Method in class dev.nm.stat.test.distribution.AndersonDarling
-
- statistics() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples
-
- statistics() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov1Sample
-
- statistics() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov2Samples
-
- statistics() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
-
- statistics() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera
-
- statistics() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors
-
- statistics() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk
-
- statistics() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
-
- statistics() - Method in class dev.nm.stat.test.HypothesisTest
-
Get the test statistics.
- statistics() - Method in class dev.nm.stat.test.mean.OneWayANOVA
-
- statistics() - Method in class dev.nm.stat.test.mean.T
-
- statistics() - Method in class dev.nm.stat.test.rank.KruskalWallis
-
- statistics() - Method in class dev.nm.stat.test.rank.SiegelTukey
-
- statistics() - Method in class dev.nm.stat.test.rank.VanDerWaerden
-
- statistics() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
- statistics() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
- statistics() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.BreuschPagan
-
- statistics() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
- statistics() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.White
-
- statistics() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
-
- statistics() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
-
Deprecated.
- statistics() - Method in class dev.nm.stat.test.variance.Bartlett
-
- statistics() - Method in class dev.nm.stat.test.variance.BrownForsythe
-
- statistics() - Method in class dev.nm.stat.test.variance.F
-
- statistics() - Method in class dev.nm.stat.test.variance.Levene
-
- statisticsAlternative() - Method in class dev.nm.stat.test.distribution.AndersonDarling
-
Gets the alternative Anderson-Darling statistic (adjusted for ties).
- stderr() - Method in class dev.nm.stat.regression.linear.LMBeta
-
Gets the standard errors of the coefficients β^.
- stderr() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the standard error of the residuals.
- stderr() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Get the asymptotic standard errors of the estimators.
- stderr() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
Get the asymptotic standard errors of the estimated parameters,
φ and θ.
- stdev() - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
-
Gets the standard deviations of the elements.
- stdev(int) - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
-
Gets the standard deviation of the i-th element.
- stdev() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
-
- stdev(double) - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
Gets the stdev of the last % portion in the spread.
- stdev() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
Gets the stdev of the spread.
- stdinno - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
-
- SteepestDescentImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- SteepestDescentMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
Construct a multivariate minimizer using a steepest descent method.
- SteepestDescentMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
Construct a multivariate minimizer using a steepest descent method.
- SteepestDescentMinimizer.SteepestDescentImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
-
This is an implementation of the steepest descent method.
- SteepestDescentSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
Construct a Steepest Descent method (SDM) solver.
- SteepestDescentSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
Construct a Steepest Descent method (SDM) solver.
- STEFAN_BOLTZMANN_SIGMA - Static variable in class dev.nm.misc.PhysicalConstants
-
The Stegan-Boltzmann constant \(\sigma\) in (W m-2 K-4).
- step() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta1
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta10
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta2
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta3
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta4
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta5
- step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta6
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta7
-
- step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta8
-
- step(DerivativeFunction, Vector, double, double, double) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaStepper
-
- step() - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
-
- step() - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod
-
Do the next iteration.
- step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
-
- step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
Do the next iteration.
- step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
-
- step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
-
- step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
-
- step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
-
- step() - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
-
- step() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Run a step in genetic algorithm: produce the next generation of chromosome pool.
- step() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
-
- step() - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
-
- step() - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
-
- step() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Performs one step of the leap-frogging algorithm.
- StepFunction - Class in dev.nm.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 dev.nm.analysis.function.rn2r1.univariate.StepFunction
-
Construct an empty step function.
- StepFunction(OrderedPairs, double) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.StepFunction
-
Construct a step function from a collection ordered pairs.
- StepFunction(OrderedPairs) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.StepFunction
-
Construct a step function from a collection ordered pairs.
- steps(int) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Performs n steps of the leap-frogging algorithm.
- StopCondition - Interface in dev.nm.misc.algorithm.stopcondition
-
Defines when an algorithm stops (the iterations).
- StringUtils - Class in dev.nm.misc
-
Utility methods for string manipulation.
- studentized() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
studentized residual = standardized * sqrt((n-m-1) / (n-m-standardized^2))
- SturmCount - Class in dev.nm.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 dev.nm.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 dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
-
Constructs a covariates subset.
- subarray(double[], int[]) - Static method in class dev.nm.number.DoubleUtils
-
Get a sub-array of the original array with the given indices.
- subarray(int[], int[]) - Static method in class dev.nm.number.DoubleUtils
-
Get a sub-array of the original array with the given indices.
- subDiagonal(Matrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the sub-diagonal of a matrix as a vector.
- subDiagonal(SparseMatrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the sub-diagonal of a sparse matrix as a sparse vector.
- SubFunction<R> - Class in dev.nm.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 dev.nm.analysis.function.SubFunction
-
Constructs a sub-function.
- subject() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
-
Gets the subject of the row.
- subMatrix(Matrix, int, int, int, int) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the four corners of a sparse matrix.
- subMatrix(Matrix, List<Integer>, List<Integer>) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the intersections of rows and columns of a
matrix.
- subMatrix(Matrix, int[], int[]) - Static method in class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a sub-matrix from the intersections of rows and columns of a sparse matrix.
- SubMatrixBlock - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
-
Sub-matrix block representation for block algorithm.
- SubMatrixRef - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
Constructs a sub-matrix reference.
- SubMatrixRef(Matrix, int, int, int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
Constructs a sub-matrix reference.
- SubMatrixRef(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
Constructs a reference to the whole matrix.
- SubProblemMinimizer - Class in dev.nm.solver.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 dev.nm.solver.multivariate.constrained.SubProblemMinimizer
-
- SubProblemMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
-
- SubProblemMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
-
- SubProblemMinimizer.ConstrainedMinimizerFactory<U extends ConstrainedMinimizer<ConstrainedOptimProblem,IterativeSolution<Vector>>> - Interface in dev.nm.solver.multivariate.constrained
-
This factory constructs a new instance of
ConstrainedMinimizer to solve a real valued minimization
problem.
- SubProblemMinimizer.IterativeSolution<Vector> - Interface in dev.nm.solver.multivariate.constrained
-
- SubstitutionRule - Interface in dev.nm.analysis.integration.univariate.riemann.substitution
-
A substitution rule specifies \(x(t)\) and \(\frac{\mathrm{d} x}{\mathrm{d} t}\).
- subTree(V) - Method in interface dev.nm.graph.RootedTree
-
Gets a sub-tree starting from a vertex.
- subTree(V) - Method in class dev.nm.graph.type.SparseTree
-
- subTree(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- subVector(Vector, int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets a sub-vector from a vector.
- subVector(SparseVector, int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets a sub-vector from a sparse vector.
- subVector(Vector, List<Integer>) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets a sub-vector from a vector according to a given array
of ordered indices (repetition allowed).
- subVector(Vector, int[]) - Static method in class dev.nm.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 dev.nm.algebra.linear.vector.doubles
-
Represents a sub-vector backed by the referenced vector, without data
copying.
- SubVectorRef(Vector, int, int) - Constructor for class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- SuccessiveOverrelaxationSolver - Class in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the sum of all elements in the given matrix.
- sum(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the sum of all vector elements.
- sum(int, int) - Method in class dev.nm.analysis.sequence.Summation
-
Sum up the terms from from
to to
with the increment 1.
- sum(int, int, int) - Method in class dev.nm.analysis.sequence.Summation
-
Sum up the terms from from
to to
with the increment inc
.
- sum(double, double, double) - Method in class dev.nm.analysis.sequence.Summation
-
Sum up the terms from from
to to
with the increment inc
.
- sum(double[]) - Method in class dev.nm.analysis.sequence.Summation
-
Partial summation of the selected terms.
- sum(BigDecimal...) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Sum up the BigDecimal
numbers.
- sum(double...) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Sum up big numbers.
- sum(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the sum of the values.
- sum(int...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the sum of the values.
- sum2(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the sum of squares of the values.
- sum_BtDt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_BtDt
-
Get Σ(Bt)*(dt).
- sum_BtDt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt
-
Get Σ(Bt)*(dt).
- sum_tBtDt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt
-
Get Σ(t-0.5)*(Bt)*(dt).
- Summation - Class in dev.nm.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 dev.nm.analysis.sequence.Summation
-
Construct a summation series.
- Summation(Summation.Term) - Constructor for class dev.nm.analysis.sequence.Summation
-
Construct a finite summation series.
- Summation.Term - Interface in dev.nm.analysis.sequence
-
Define the terms in a summation series.
- SumOfPenalties - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the costs from a set of constituent penalty functions.
- SumOfPenalties(PenaltyFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.SumOfPenalties
-
Construct a sum-of-penalties penalty function from a set of penalty functions.
- SumOfPoweredWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification
-
Defines portfolio diversification as
\[
D(w) = \sum_i w_i^P
\]
- SumOfPoweredWeights(double) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfPoweredWeights
-
- SumOfSquaredWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification
-
Defines portfolio diversification as
\[
D(w) = \sum_i w_i^2
\]
- SumOfSquaredWeights() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfSquaredWeights
-
- sumOfWeights() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
-
- SumOfWLogW - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification
-
Defines portfolio diversification as
\[
D(w) = \sum_i w_i \ln(w_i)
\]
- SumOfWLogW() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfWLogW
-
- sumsOfPowersOfDifferences(int, double, double...) - Static method in class dev.nm.stat.descriptive.moment.Moments
-
Compute the power
-th moment of an array of data
with respect to a mean
.
- sumToInfinity(int) - Method in class dev.nm.analysis.sequence.Summation
-
Sum up the terms from from
to infinity with increment 1 until the series converges.
- sumToInfinity(double, double) - Method in class dev.nm.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 tech.nmfin.infantino2010.Infantino2010PCA
-
Sums up, for each column, the last nRows
rows.
- superDiagonal(Matrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the super-diagonal of a matrix as a vector.
- superDiagonal(SparseMatrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the super-diagonal of a sparse matrix as a sparse vector.
- supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
-
- supportsInterval(double, double) - Method in interface dev.nm.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 dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
-
- supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
-
- supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
-
- SVD - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
-
Runs the SVD decomposition on a matrix.
- SVD(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
-
Runs the SVD decomposition on a matrix.
- SVD(Matrix, boolean, double, SVD.Method) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
-
Runs the SVD decomposition on a matrix.
- svd() - Method in class dev.nm.stat.factor.pca.PCAbySVD
-
Gets the Singular Value Decomposition (SVD) of matrix X.
- SVD.Method - Enum in dev.nm.algebra.linear.matrix.doubles.factorization.svd
-
- SVDbyMR3 - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
-
Creates a singular value decomposition for a matrix A.
- SVDDecomposition - Interface in dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.operation.SVEC
-
Construct the SVEC of a matrix.
- svecA() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
-
- svecA() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
-
Computes A^ in "Toh, Todd, Tütüncü, Section 3.1".
- svecA() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
- swap(FlexibleTable, int, int) - Method in class dev.nm.solver.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 dev.nm.solver.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 dev.nm.solver.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Swaps two columns of a permutation matrix.
- swapColumn(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Swap columns:
- swapInPlace(FlexibleTable, int, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange
-
- swapRow(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Swaps two rows of a permutation matrix.
- swapRow(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Swap rows:
- symbol1 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- symbol1() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- symbol2 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- symbol2() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- SymmetricEigenByMR3 - Class in dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix
-
- SymmetricKronecker - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
Compute the symmetric Kronecker product of two matrices.
- SymmetricKronecker(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SymmetricKronecker
-
Compute the symmetric Kronecker product of two matrices.
- SymmetricMatrix - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Construct a symmetric matrix of dimension dim * dim.
- SymmetricMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Construct a symmetric matrix from a 2D double[][]
array.
- SymmetricMatrix(Matrix) - Constructor for class dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Cast an (almost) symmetric matrix into SymmetricMatrix.
- SymmetricMatrix(SymmetricMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Copy constructor.
- SymmetricQRAlgorithm - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Runs the QR algorithm on a symmetric matrix.
- SymmetricQRAlgorithm(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Runs the QR algorithm on a symmetric matrix.
- SymmetricQRAlgorithm(Matrix, double, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Runs the QR algorithm on a symmetric matrix.
- SymmetricSuccessiveOverrelaxationSolver - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
Construct a SSOR solver with the extrapolation factor ω.
- SymmetricSVD - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Calculates the SVD of A.
- SymmetricSVD(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Calculates the SVD of A.
- SymmetricTridiagonalDecomposition - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition
-
Runs the tridiagonal decomposition for a square, symmetric
matrix.
- SYNC_RNORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
-
- SYNC_UNIFORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
-
- synchronizedRLG(RandomLongGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils
-
- synchronizedRNG(RandomNumberGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils
-
- synchronizedRVG(RandomVectorGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils
-
- SynchronizedStatistic - Class in dev.nm.stat.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 dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition
-
Gets the symmetric tridiagonal T matrix.
- T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization
-
Gets T, such that T = Q * A * Q.
- T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
- T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the transformation matrix, T, such that T * A = U.
- T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Get the transformation matrix, T, such that T * A = U.
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- T() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the transformation matrix, T, such that T * A = U.
- t() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
-
Get the transpose of this matrix.
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
The transpose of a diagonal matrix is the same as itself.
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
t(A)
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
The transpose of a symmetric matrix is the same as itself.
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
The transpose of a permutation matric is the same as its inverse.
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
-
- T() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Get the transformed matrix T.
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
-
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- T() - Method in class dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
-
Gets the value on the time axis at index k
.
- t(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the value on the time-axis at index k
.
- t() - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Gets the penalization parameter t for L1 regularization.
- t - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
distinct population eigenvalues
- t() - Method in class dev.nm.stat.descriptive.rank.Rank
-
/[ t = \sum(t_i^3 - t_i) \]
- t() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSOProblem
-
Get the penalization parameter for the constrained form of LASSO.
- t() - Method in class dev.nm.stat.regression.linear.LMBeta
-
Gets the t- or z- value of the regression coefficients β^.
- t() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
-
Get the current time.
- T() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid
-
Get the end time.
- t() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
-
Get the current time.
- T - Class in dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.test.mean.T
-
Construct a two sample location test of the null hypothesis that the means of two normally
distributed populations are equal.
- T() - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal
-
- T - Variable in class tech.nmfin.infantino2010.Infantino2010PCA
-
- ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
Get the lower limit of the integral.
- ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
-
- ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
- ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
- ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
- ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
-
- ta() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule
-
Get the lower limit of the integral.
- Table - Interface in dev.nm.misc.datastructure
-
A table is a means of arranging data in rows and columns.
- table - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
-
- tail() - Method in interface dev.nm.graph.Arc
-
Get the tail of this arc.
- tail() - Method in class dev.nm.graph.type.SimpleArc
-
- tallR() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
- tallR() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
- tallR() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
-
- tallR() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get the tall R matrix.
- tan(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Tangent of a complex number.
- tangentAt(SortedOrderedPairs, int) - Method in interface dev.nm.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 dev.nm.number.complex.ElementaryFunction
-
Hyperbolic tangent of a complex number.
- tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
-
Gets estimated population eigenvalues in ascending order.
- tau - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
population eigenvalues in ascending order
- TauEstimator - Class in dev.nm.stat.covariance.nlshrink
-
Non-linear shrinkage estimator of population eigenvalues.
- TauEstimator(int, int, Vector, int) - Constructor for class dev.nm.stat.covariance.nlshrink.TauEstimator
-
- TauEstimator(int, int, Vector) - Constructor for class dev.nm.stat.covariance.nlshrink.TauEstimator
-
- tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
-
Get the upper limit of the integral.
- tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
-
- tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
-
- tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
-
- tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
-
- tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
-
- tb() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule
-
Get the upper limit of the integral.
- TDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.TDistribution
-
Construct a Student's t distribution.
- tech.nmfin.infantino2010 - package tech.nmfin.infantino2010
-
- tech.nmfin.meanreversion.cointegration - package tech.nmfin.meanreversion.cointegration
-
- tech.nmfin.meanreversion.cointegration.check - package tech.nmfin.meanreversion.cointegration.check
-
- tech.nmfin.meanreversion.daspremont2008 - package tech.nmfin.meanreversion.daspremont2008
-
- tech.nmfin.meanreversion.elliott2005 - package tech.nmfin.meanreversion.elliott2005
-
- tech.nmfin.meanreversion.hvolatility - package tech.nmfin.meanreversion.hvolatility
-
- tech.nmfin.meanreversion.volarb - package tech.nmfin.meanreversion.volarb
-
- tech.nmfin.portfoliooptimization - package tech.nmfin.portfoliooptimization
-
- tech.nmfin.portfoliooptimization.clm - package tech.nmfin.portfoliooptimization.clm
-
- tech.nmfin.portfoliooptimization.corvalan2005 - package tech.nmfin.portfoliooptimization.corvalan2005
-
- tech.nmfin.portfoliooptimization.corvalan2005.constraint - package tech.nmfin.portfoliooptimization.corvalan2005.constraint
-
- tech.nmfin.portfoliooptimization.corvalan2005.diversification - package tech.nmfin.portfoliooptimization.corvalan2005.diversification
-
- tech.nmfin.portfoliooptimization.lai2010 - package tech.nmfin.portfoliooptimization.lai2010
-
- tech.nmfin.portfoliooptimization.lai2010.ceta - package tech.nmfin.portfoliooptimization.lai2010.ceta
-
- tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer - package tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
-
- tech.nmfin.portfoliooptimization.lai2010.ceta.npeb - package tech.nmfin.portfoliooptimization.lai2010.ceta.npeb
-
- tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler - package tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
-
- tech.nmfin.portfoliooptimization.lai2010.fit - package tech.nmfin.portfoliooptimization.lai2010.fit
-
- tech.nmfin.portfoliooptimization.lai2010.optimizer - package tech.nmfin.portfoliooptimization.lai2010.optimizer
-
- tech.nmfin.portfoliooptimization.markowitz - package tech.nmfin.portfoliooptimization.markowitz
-
- tech.nmfin.portfoliooptimization.markowitz.constraints - package tech.nmfin.portfoliooptimization.markowitz.constraints
-
- tech.nmfin.portfoliooptimization.nmsaam - package tech.nmfin.portfoliooptimization.nmsaam
-
- tech.nmfin.portfoliooptimization.socp.constraints - package tech.nmfin.portfoliooptimization.socp.constraints
-
- tech.nmfin.portfoliooptimization.socp.constraints.ybar - package tech.nmfin.portfoliooptimization.socp.constraints.ybar
-
- tech.nmfin.returns - package tech.nmfin.returns
-
- tech.nmfin.returns.moments - package tech.nmfin.returns.moments
-
- tech.nmfin.trend.dai2011 - package tech.nmfin.trend.dai2011
-
- tech.nmfin.trend.kst1995 - package tech.nmfin.trend.kst1995
-
- temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.BoltzTemperatureFunction
-
- temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.ExpTemperatureFunction
-
Matlab's default: @temperatureexp (default) - T = T0 * 0.95^k.
- temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.FastTemperatureFunction
-
Matlab: @temperaturefast - The temperature is equal to InitialTemperature / k.
- temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
-
Gets the temperature at time t.
- TemperatureFunction - Interface in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
-
A temperature function defines a temperature schedule used in simulated annealing.
- TemperedAcceptanceProbabilityFunction - Interface in dev.nm.solver.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 dev.nm.number.DoubleUtils.ifelse
-
Decide whether x satisfies the boolean
test.
- theta - Variable in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution.Lambda
-
the scale parameter
- theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
-
- theta(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
-
The canonical parameter of the distribution in terms of the mean μ.
- theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
-
- theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
-
- theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
-
- theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
-
- theta() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
-
- theta() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUProcess
-
Get the mean reversion rate.
- theta() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get all the MA coefficients.
- theta(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
-
Get the coefficients of the linear predictor.
- theta(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
-
Get the coefficients of the linear predictor.
- theta(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
-
Get the coefficients of the linear predictor.
- theta() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get all the MA coefficients.
- theta(int, int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
-
Gets the coefficients of the linear predictor.
- thetaPolynomial() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the polynomial (1 + θ).
- ThinRNG - Class in dev.nm.stat.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 dev.nm.stat.random.rng.univariate.ThinRNG
-
Constructs a thinned RNG.
- ThinRVG - Class in dev.nm.stat.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 dev.nm.stat.random.rng.multivariate.ThinRVG
-
Constructs a thinned RVG.
- ThomasAlgorithm - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
-
Thomas algorithm is an efficient algorithm to solve a linear tridiagonal matrix equation.
- ThomasAlgorithm() - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.ThomasAlgorithm
-
- ThreadIDRLG - Class in dev.nm.stat.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 dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
-
Constructs a per-context repeatable RLG.
- ThreadIDRLG(int) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
-
Constructs a per-context repeatable RLG.
- ThreadIDRLG() - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
-
Constructs a per-context repeatable RLG.
- ThreadIDRLG(long) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
-
Constructs a per-context repeatable RLG.
- ThreadIDRNG - Class in dev.nm.stat.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 dev.nm.stat.random.rng.concurrent.context.ThreadIDRNG
-
Constructs a per-context repeatable RNG.
- threshold(double) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets a suggested trading threshold based on an OU process.
- throwIfDifferentDimension(Table, Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Throws if
A1.nRows() != A2.nRows()
Or
A1.nCols() != A2.nCols()
- throwIfIncompatible4Multiplication(Table, Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Throws if
A1.nCols() != A2.nRows()
- throwIfIncompatible4Multiplication(Table, Vector) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Throws if
A.nCols() != v.size()
- throwIfInvalidColumn(Table, int) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Throws if accessing an out of range column.
- throwIfInvalidIndex(Vector, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if an index is a valid index.
- throwIfInvalidRow(Table, int) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Throws if accessing an out of range row.
- throwIfNotEqualSize(Vector, Vector) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if the input vectors have the same size.
- throwIfNotNull(RuntimeException) - Static method in class dev.nm.misc.ExceptionUtils
-
This is a wrapper method that throws a RuntimeException
if it is not null
.
- Ties<T> - Class in dev.nm.combinatorics
-
Count the number of occurrences of each distinctive value.
- Ties(List<T>) - Constructor for class dev.nm.combinatorics.Ties
-
Count the number of occurrences of each distinctive value.
- time() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
-
Gets the time of the row.
- time() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get the current time.
- time(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the i-th time point.
- time() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
-
Get the current time.
- time(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Get the i-th timestamp.
- time(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Get the i-th time.
- time() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
Gets the forecast time.
- TimeGrid - Interface in dev.nm.stat.stochasticprocess.timegrid
-
Specify the time points in a grid or axis.
- TimeInterval - Class in dev.nm.misc.datastructure.time
-
This is a time interval.
- TimeInterval(DateTime, DateTime) - Constructor for class dev.nm.misc.datastructure.time.TimeInterval
-
Construct a time interval from two time points.
- TimeIntervals - Class in dev.nm.misc.datastructure.time
-
- TimeIntervals() - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Construct an empty collection of time interval.
- TimeIntervals(DateTime, DateTime) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Construct a collection consisting of one time interval.
- TimeIntervals(Interval<DateTime>) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Construct a collection consisting of one time interval.
- TimeIntervals(Interval<DateTime>...) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Construct a collection of time intervals.
- TimeIntervals(Intervals<DateTime>) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Copy constructor.
- times() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the entire time grid.
- times() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
-
- TimeSeries<T extends Comparable<? super T>,V,E extends TimeSeries.Entry<T,V>> - Interface in dev.nm.stat.timeseries.datastructure
-
A time series is a serially indexed collection of items.
- TimeSeries.Entry<T,V> - Interface in dev.nm.stat.timeseries.datastructure
-
A time series is composed of a sequence of Entry
s.
- timestamps() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Get all the timestamps.
- timestamps() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Get all the timestamps.
- to1DArray(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get all matrix entries in the form of an 1D double[]
.
- to2DArray(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get all matrix entries in the form of a 2D double[][]
array.
- toArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- toArray() - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
-
- toArray() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- toArray() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- toArray() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- toArray() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Cast this vector into a 1D double[]
.
- toArray() - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- toArray(T[]) - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- toArray() - Method in class dev.nm.misc.datastructure.MathTable.Row
-
Converts the row to a double[]
, excluding the index.
- toArray() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
Get the sorted sample.
- toArray() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Convert this multivariate time series into an array of vectors.
- toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
-
- toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
-
- toArray() - Method in interface dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries
-
Convert this time series into an array, discarding the timestamps.
- toBigDecimal() - Method in class dev.nm.number.Real
-
Convert this number to a BigDecimal
.
- toColumns(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get an array of all column vectors from a matrix.
- toContext(HouseholderInPlace.Householder) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace.Householder
-
- toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- toDense() - Method in interface dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- toDouble() - Method in class dev.nm.number.complex.Complex
-
Cast the complex number to a
Double
if it is a real number.
- toGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralLessThanConstraints
-
- toGreaterThanConstraints() - Method in interface dev.nm.solver.multivariate.constrained.constraint.LessThanConstraints
-
Convert the less-than or equal-to constraints to greater-than or equal-to constraints.
- toGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
- toGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearLessThanConstraints
-
- Tolerance - Interface in dev.nm.misc.algorithm.iterative.tolerance
-
The tolerance criteria for an iterative algorithm to stop.
- toLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralGreaterThanConstraints
-
- toLessThanConstraints() - Method in interface dev.nm.solver.multivariate.constrained.constraint.GreaterThanConstraints
-
Convert the greater-than or equal-to constraints to less-than or equal-to constraints.
- toLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
- toLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
-
- toMatrix() - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Gets a copy of the flexible table in the form of a matrix.
- toMatrix() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- toMatrix() - Method in interface dev.nm.stat.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 dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- toMatrix(UnivariateTimeSeries<?, ?>) - Static method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeriesUtils
-
Cast a time series into a column matrix, discarding the timestamps.
- TopNOptimizationAlgorithm - Class in tech.nmfin.portfoliooptimization
-
- TopNOptimizationAlgorithm(PortfolioOptimizationAlgorithm, int, double) - Constructor for class tech.nmfin.portfoliooptimization.TopNOptimizationAlgorithm
-
- topologicalOrder(V) - Method in interface dev.nm.graph.DAGraph
-
Get the topological order of a vertex.
- topologicalOrder(V) - Method in class dev.nm.graph.type.SparseDAGraph
-
- topologicalOrder(V) - Method in class dev.nm.graph.type.SparseTree
-
- topologicalOrder(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
-
- toPrimitive(Double[]) - Static method in class dev.nm.number.DoubleUtils
-
Convert a Double
array to a primitive double
array.
- toRows(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get an array of all row vectors from a matrix.
- toStop(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration.StoppingCriterion
-
Check whether we stop with the current eigenvector.
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
- toString(SparseMatrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
- toString(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the String
representation of a matrix.
- toString() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
- toString() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
-
- toString() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
- toString() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
- toString() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
-
- toString() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
-
- toString() - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
- toString() - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction
-
- toString() - Method in class dev.nm.analysis.function.tuple.Triple
-
- toString() - Method in class dev.nm.graph.algorithm.traversal.BFS.Node
-
- toString() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node
-
- toString() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
-
- toString() - Method in class dev.nm.graph.community.EdgeBetweeness
-
- toString() - Method in class dev.nm.graph.community.GirvanNewman
-
- toString() - Method in class dev.nm.graph.type.SimpleArc
-
- toString() - Method in class dev.nm.graph.type.SimpleEdge
-
- toString() - Method in class dev.nm.graph.type.SparseDiGraph
-
- toString() - Method in class dev.nm.graph.type.SparseGraph
-
- toString() - Method in class dev.nm.graph.type.VertexTree
-
- toString() - Method in class dev.nm.interval.Interval
-
- toString() - Method in class dev.nm.interval.Intervals
-
- toString() - Method in class dev.nm.misc.algorithm.ActiveSet
-
- toString() - Method in class dev.nm.misc.datastructure.FlexibleTable
-
- toString() - Method in class dev.nm.misc.datastructure.IdentityHashSet
-
- toString() - Method in class dev.nm.misc.datastructure.SortableArray
-
- toString() - Method in class dev.nm.number.complex.Complex
-
- toString(double...) - Static method in class dev.nm.number.DoubleUtils
-
Print out numbers to a string.
- toString(double[][]) - Static method in class dev.nm.number.DoubleUtils
-
Print out a 2D array, double[][]
to a string.
- toString() - Method in class dev.nm.number.Real
-
- toString() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
-
- toString() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
- toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
- toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
-
- toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
- toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
-
- toString() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
- toString() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
- toString() - Method in class dev.nm.stat.descriptive.covariance.Covariance
-
- toString() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
-
- toString() - Method in class dev.nm.stat.descriptive.moment.Mean
-
- toString() - Method in class dev.nm.stat.descriptive.moment.Moments
-
- toString() - Method in class dev.nm.stat.descriptive.moment.Skewness
-
- toString() - Method in class dev.nm.stat.descriptive.moment.Variance
-
- toString() - Method in class dev.nm.stat.descriptive.rank.Max
-
- toString() - Method in class dev.nm.stat.descriptive.rank.Min
-
- toString() - Method in class dev.nm.stat.evt.cluster.Clusters.Cluster
-
- toString() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
-
- toString() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
-
- toString() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
-
- toString() - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
- toString() - Method in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix
-
- toString() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
- toString() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
- toString() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
- toString() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
- toString() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
- toString() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
- toString() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
- toString() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
- toString() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
- toString() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
-
- toString() - Method in class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
-
- toString() - Method in class tech.nmfin.meanreversion.volarb.MRModelRanged
-
- toString() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
-
- toString() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
- toString() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
- toVector(UnivariateTimeSeries<?, ?>) - Static method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeriesUtils
-
Cast a time series into a vector, discarding the timestamps.
- tr(Matrix) - Static method in class dev.nm.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 dev.nm.graph.algorithm.traversal.BFS
-
- track(V, int) - Method in class dev.nm.graph.algorithm.traversal.DFS
-
- track(V, int) - Method in class dev.nm.graph.algorithm.traversal.TraversalFromRoots
-
Runs the traversal algorithm on a graph from a designated root.
- trades() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
-
- TradingPair - Class in tech.nmfin.meanreversion.cointegration
-
- TradingPair(String, String, Vector, Vector, double) - Constructor for class tech.nmfin.meanreversion.cointegration.TradingPair
-
Constructs a related pair for trading, e.g., cointegrated pair.
- train(int[], DiscreteHMM) - Static method in class dev.nm.stat.hmm.discrete.BaumWelch
-
Constructs a trained (discrete) hidden Markov model, one iteration.
- train(MixtureHMM, double[]) - Static method in class dev.nm.stat.hmm.mixture.MixtureHMMEM
-
Constructs a trained mixture hidden Markov model, one iteration.
- transform(double[]) - Method in interface dev.nm.dsp.univariate.operation.system.doubles.Filter
-
Transforms the input signal into the output signal.
- transform(double[]) - Method in class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage
-
- transform(double[]) - Method in class dev.nm.dsp.univariate.operation.system.doubles.MovingAverageByExtension
-
- transform(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer
-
- transpose(MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
-
- transpose(MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
Get the transpose of A.
- transpose(MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- transposeSolve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
-
Return the input vector x.
- transposeSolve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
-
Pt = P-1 for Jacobi preconditioner.
- transposeSolve(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner
-
Solve Mtv = x, where M is the preconditioner
matrix.
- transposeSolve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
-
Mtx = M-1x as M is symmetric.
- Trapezoidal - Class in dev.nm.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 dev.nm.analysis.integration.univariate.riemann.newtoncotes.Trapezoidal
-
Construct an integrator that implements the Trapezoidal rule.
- TraversalFromRoots<V> - Class in dev.nm.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 dev.nm.graph.algorithm.traversal.TraversalFromRoots
-
Constructs a traversal order of a graph.
- traverse(V, int) - Method in class dev.nm.graph.algorithm.traversal.TraversalFromRoots
-
Runs the traversal algorithm on a graph from a designated root.
- Tree<V,E extends HyperEdge<V>> - Interface in dev.nm.graph
-
A tree is an undirected graph in which any two vertices are connected by exactly one simple path.
- trees() - Method in interface dev.nm.graph.Forest
-
Get the disjoint set of trees.
- trend() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
-
- TrendType - Enum in dev.nm.stat.test.timeseries.adf
-
These are the three versions of the Augmented Dickey-Fuller (ADF) test.
- TriangularDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.TriangularDistribution
-
Constructs an instance of a Triangular distribution.
- TridiagonalDeflationSearch - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
This class locates deflation in a tridiagonal matrix.
- TridiagonalDeflationSearch(boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
-
- TridiagonalDeflationSearch(DeflationCriterion, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
-
- TriDiagonalization - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization
-
Runs the tri-diagonalization process for a symmetric matrix.
- TridiagonalMatrix - Class in dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
Constructs a 0 tri-diagonal matrix of dimension dim * dim.
- TridiagonalMatrix(double[][]) - Constructor for class dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
-
Copy constructor performing a deep copy.
- Trigamma - Class in dev.nm.analysis.function.special.gamma
-
The trigamma function is defined as the logarithmic derivative of the digamma function.
- Trigamma() - Constructor for class dev.nm.analysis.function.special.gamma.Trigamma
-
- TrigMath - Class in dev.nm.geometry
-
A collection of trigonometric functions complementary to those in Java's
Math
class.
- Triple - Class in dev.nm.analysis.function.tuple
-
A triple is a tuple of length three.
- Triple(double, double, double) - Constructor for class dev.nm.analysis.function.tuple.Triple
-
Creates a new triple with the given values.
- TrivariateRealFunction - Interface in dev.nm.analysis.function.rn2r1
-
A trivariate real function takes three real arguments and outputs one real value.
- TruncatedNormalDistribution - Class in dev.nm.stat.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 dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
Construct a truncated Normal distribution.
- TruncatedNormalDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
Construct a truncated standard Normal distribution.
- TSS() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the diagnostic measure: total sum of squares, \(\sum (y-y_mean)^2 \).
- TurningPoint - Class in tech.nmfin.portfoliooptimization.clm
-
Represents a turning point on a Markowitz critical line.
- Twiddle<T> - Class in dev.nm.combinatorics
-
Generates all combinations of M elements drawn without replacement from a set of N
elements.
- Twiddle(Collection<T>, int) - Constructor for class dev.nm.combinatorics.Twiddle
-
- type - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
the label type
- V() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization
-
Gets V, where
V' = Vk * ...
- V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
-
- V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
-
Gets V, where
V' = Vk * ...
- V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
-
- V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
- V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
-
- V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
-
- V() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the V matrix as in SVD decomposition.
- V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Returns the matrix V as in A=UDV'.
- v - Variable in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
The defining vector which is perpendicular to the Householder hyperplane.
- v() - Method in class dev.nm.analysis.function.polynomial.QuadraticMonomial
-
Get v as in (x2 + ux + v).
- v() - Method in class dev.nm.solver.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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
-
- V(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Gets V(t), the covariance matrix of vt.
- V(int) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
-
Get V(t), the variance of vt.
- V() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
-
Gets the correlation (or covariance) matrix used by the PCA.
- V() - Method in class tech.nmfin.returns.moments.ReturnsMoments
-
Gets the second moment matrix.
- validate(String) - Static method in class dev.nm.misc.license.Package
-
- validateAll(String...) - Static method in class dev.nm.misc.license.Package
-
- validateAny(String...) - Static method in class dev.nm.misc.license.Package
-
- validateVersion(String, String) - Static method in class dev.nm.misc.license.Package
-
Check if a package is licensed up to a specified version.
- value - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
the entry value
- value() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
Gets the value of this entry.
- value() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.InnerProduct
-
Get the value of the inner product.
- value() - Method in class dev.nm.analysis.integration.univariate.Lebesgue
-
Get the integral value.
- value(UndirectedEdge<V>) - Method in class dev.nm.graph.community.EdgeBetweeness
-
Gets the edge-betweeness of an edge.
- value(UndirectedEdge<V>) - Method in class dev.nm.graph.community.GirvanNewman
-
Get the edge-betweeness of an edge.
- value() - Method in interface dev.nm.misc.algorithm.bb.BBNode
-
the value of this node
- value() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
-
- value() - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
-
- value() - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
-
- value() - Method in class dev.nm.stat.descriptive.covariance.Covariance
-
- value() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
-
- value() - Method in class dev.nm.stat.descriptive.moment.Mean
-
- value() - Method in class dev.nm.stat.descriptive.moment.Moments
-
- value() - Method in class dev.nm.stat.descriptive.moment.Skewness
-
- value() - Method in class dev.nm.stat.descriptive.moment.Variance
-
- value() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
-
- value() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
-
- value() - Method in class dev.nm.stat.descriptive.rank.Max
-
- value() - Method in class dev.nm.stat.descriptive.rank.Min
-
- value(double) - Method in class dev.nm.stat.descriptive.rank.Quantile
-
Compute the sample value corresponding to a quantile.
- value() - Method in class dev.nm.stat.descriptive.rank.Quantile
-
- value() - Method in interface dev.nm.stat.descriptive.Statistic
-
Get the value of the statistic.
- value() - Method in class dev.nm.stat.descriptive.SynchronizedStatistic
-
- value(double[]) - Method in class dev.nm.stat.evt.exi.ExtremalIndexByClusterSizeReciprocal
-
- value(double[]) - Method in class dev.nm.stat.evt.exi.ExtremalIndexByFerroSeegers
-
- value(double[]) - Method in interface dev.nm.stat.evt.exi.ExtremalIndexEstimation
-
Estimate the extremal index with the given observations from a distribution.
- value() - Method in enum dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
-
Gets the value of this Mersenne exponent.
- value() - Method in class dev.nm.stat.random.sampler.resampler.BootstrapEstimator
-
Gets the estimator value (the mean).
- value(double[], double[], double[]) - Method in class dev.nm.stat.regression.WeightedRSS
-
Computes the weighted RSS for a set of observations.
- value(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
-
Integrate the function with respect to a given filtration.
- value() - Method in class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Get the expectation (the mean).
- value(double[]) - Method in interface tech.nmfin.portfoliooptimization.corvalan2005.diversification.DiversificationMeasure
-
Evaluates the level of portfolio diversification given portfolio weights.
- value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.ProductOfWeights
-
- value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfPoweredWeights
-
- value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfSquaredWeights
-
- value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfWLogW
-
- value() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
-
- ValueArray(int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
-
- valueOf(String) - Static method in enum dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.analysis.differentiation.univariate.Dfdx.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.analysis.differentiation.univariate.FiniteDifference.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.analysis.function.special.gamma.LogGamma.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.dsp.univariate.operation.system.doubles.MovingAverage.Side
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.graph.algorithm.traversal.DFS.Node.Color
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.interval.IntervalRelation
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.number.DoubleUtils.RoundingScheme
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.solver.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 dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.descriptive.rank.Rank.TiesMethod
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.factor.factoranalysis.FactorAnalysis.ScoringRule
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1.Type
-
Deprecated.
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.test.timeseries.adf.TrendType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.test.variance.Levene.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance.Type
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum tech.nmfin.infantino2010.Infantino2010Regime.Regime
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum tech.nmfin.meanreversion.hvolatility.Kagi.Trend
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum tech.nmfin.returns.ReturnsCalculators
-
Returns the enum constant of this type with the specified name.
- values() - Static method in enum dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
-
- values() - Static method in enum dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.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 dev.nm.combinatorics.Ties
-
Get the numbers of occurrences of the objects.
- values() - Static method in enum dev.nm.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 dev.nm.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 dev.nm.interval.IntervalRelation
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum dev.nm.number.DoubleUtils.RoundingScheme
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum dev.nm.solver.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 dev.nm.solver.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 dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.regression.linear.panel.PanelData.Row
-
Gets the values of the row.
- values() - Static method in enum dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum tech.nmfin.infantino2010.Infantino2010Regime.Regime
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum tech.nmfin.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 tech.nmfin.returns.ReturnsCalculators
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- VanDerWaerden - Class in dev.nm.stat.test.rank
-
The Van der Waerden test tests for the equality of all population distribution functions.
- VanDerWaerden(double[]...) - Constructor for class dev.nm.stat.test.rank.VanDerWaerden
-
Perform the Van Der Waerden test to test for the equality of all population distribution functions.
- VanDerWaerden1969 - Class in dev.nm.stat.random.rng.univariate.beta
-
- VanDerWaerden1969(RandomGammaGenerator, RandomGammaGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969
-
Deprecated.
Construct a random number generator to sample from the beta distribution.
- VanDerWaerden1969(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969
-
Deprecated.
Construct a random number generator to sample from the beta distribution.
- var() - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
-
Gets the variance of the elements.
- var(int) - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
-
Gets the variance of the i-th element.
- var() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
Gets the variance of the prediction.
- var() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Gets the mean squared error of the h-step ahead prediction.
- var() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
-
Get the variance of the white noise.
- var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Gets the mean squared error of the h-step ahead prediction.
- var(int) - Method in class dev.nm.stat.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 dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Gets the mean squared error of the one-step ahead prediction.
- var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
-
- var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Compute the unconditional variance of the GARCH model.
- var(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
-
Gets the mean squared error for prediction errors at time n for \(\hat{x}_{n+1}\),
i.e.,
\(E(x_{n+1} - \hat{x}_{n+1})^2\).
- var - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
-
- VARFit - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
-
This class construct a VAR model by estimating the coefficients using OLS regression.
- VARFit(MultivariateIntTimeTimeSeries, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARFit
-
Estimate a VAR model from a multivariate time series.
- Variable(String, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
-
Constructs a named variable.
- variance() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
-
Get the (unbiased) variance.
- variance() - Method in class dev.nm.stat.descriptive.moment.Skewness
-
Get the (unbiased) variance.
- Variance - Class in dev.nm.stat.descriptive.moment
-
The variance of a sample is the average squared deviations from the sample mean.
- Variance() - Constructor for class dev.nm.stat.descriptive.moment.Variance
-
Construct an empty Variance
calculator.
- Variance(double[], boolean) - Constructor for class dev.nm.stat.descriptive.moment.Variance
-
Construct a Variance
calculator,
initialized with a sample.
- Variance(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Variance
-
Construct an unbiased Variance
calculator.
- Variance(Variance) - Constructor for class dev.nm.stat.descriptive.moment.Variance
-
Copy constructor.
- variance() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
Gets the variance of this distribution.
- variance() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
-
- variance() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
Gets the variance of this distribution.
- variance() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.TDistribution
-
Gets the variance of this distribution.
- variance() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
- variance() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
-
- variance() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
-
- variance() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
-
\[
\frac{\sigma^2}{(1-\xi)^2(1-2\xi)}
\]
for \(\xi < 1/2\).
- variance() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
-
- variance() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
-
- variance() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
- variance() - Method in interface dev.nm.stat.random.Estimator
-
Gets the variance of the estimator.
- variance() - Method in class dev.nm.stat.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 dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
-
- variance(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
-
The variance function of the distribution in terms of the mean μ.
- variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
-
- variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
-
- variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
-
- variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
-
- variance() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation
-
Compute the variance of the integral.
- variance() - Method in class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Get the variance.
- variance() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- variance() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- variance() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- variance() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- variance() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
- variance() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
- VariancebtX - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
Computes \(b'Xb\).
- VariancebtX(Vector, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.VariancebtX
-
Computes \(b'Xb\).
- variation() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
-
Gets the H variation/fluctuation.
- VARIMAModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model.
- VARIMAModel(Vector, Matrix[], int, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model with unit variance.
- VARIMAModel(Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model with zero-intercept (mu).
- VARIMAModel(Matrix[], int, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model with unit variance and zero-intercept (mu).
- VARIMAModel(ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate model from a univariate ARIMA model.
- VARIMAModel(VARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Copy constructor.
- VARIMASim - Class in dev.nm.stat.timeseries.linear.multivariate.arima
-
This class simulates a multivariate ARIMA process.
- VARIMASim(VARIMAModel, Vector[], Vector[], RandomVectorGenerator) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
-
Construct a multivariate ARIMA model.
- VARIMASim(VARIMAModel, RandomVectorGenerator) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
-
Construct a multivariate ARIMA model.
- VARIMASim(VARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
-
Construct a multivariate ARIMA model, using random standard Gaussian innovations.
- VARIMAXModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model.
- VARIMAXModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model with unit variance.
- VARIMAXModel(Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model with zero-intercept (mu).
- VARIMAXModel(Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model with unit variance and zero-intercept (mu).
- VARIMAXModel(VARIMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Copy constructor.
- VARIMAXModel(ARIMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model from a univariate ARIMAX model.
- VARLinearRepresentation - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
-
Construct the linear representation of an ARMA model.
- VARLinearRepresentation(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
-
- VARMAAutoCorrelation - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
-
Compute the auto-correlation function for a vector ARMA model.
- VARMAAutoCovariance - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
-
Compute the auto-covariance function for a vector ARMA model.
- VARMAForecastOneStep - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
-
Construct an instance of InnovationAlgorithm
for a multivariate ARMA time series.
- VARMAModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
-
A multivariate ARMA model, Xt, takes this form.
- VARMAModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model.
- VARMAModel(Vector, Matrix[], Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model with unit variance.
- VARMAModel(Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model with zero-intercept (mu).
- VARMAModel(Matrix[], Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model with unit variance and zero-intercept (mu).
- VARMAModel(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate model from a univariate ARMA model.
- VARMAModel(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Copy constructor.
- VARMAXModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate ARMAX model.
- VARMAXModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate ARMAX model with unit variance.
- VARMAXModel(Matrix[], Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate ARMAX model with zero-intercept (mu).
- VARMAXModel(Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate ARMAX model with unit variance and zero-intercept (mu).
- VARMAXModel(ARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate model from a univariate ARMAX model.
- VARMAXModel(VARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Copy constructor.
- VARModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a VAR model.
- VARModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model.
- VARModel(Vector, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model with unit variance.
- VARModel(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model with zero-intercept (mu).
- VARModel(Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model with unit variance and zero-intercept (mu).
- VARModel(ARModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a multivariate model from a univariate AR model.
- VARModel(VARModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Copy constructor.
- VARXModel - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model.
- VARXModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model with unit variance.
- VARXModel(Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model with zero-mean.
- VARXModel(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model with unit variance and zero-mean.
- VARXModel(VECMTransitory) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX(p) from a transitory VECM(p).
- VARXModel(VECMLongrun) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX(p) from a long-run VECM(p).
- VARXModel(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Copy constructor.
- VECM - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Construct a VECM(p) model.
- VECM(VECM) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Copy constructor.
- VECMLongrun - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Construct a long-run VECM(p) model.
- VECMLongrun(Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Construct a long-run VECM(p) model with zero-intercept (mu).
- VECMLongrun(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Construct a long-run VECM(p) from a VARX(p).
- VECMLongrun(VECMLongrun) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
-
Copy constructor.
- VECMTransitory - Class in dev.nm.stat.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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a transitory VECM(p) model.
- VECMTransitory(Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a transitory VECM(p) model with zero-intercept (mu).
- VECMTransitory(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a transitory VECM(p) from a VARX(p).
- VECMTransitory(VECMTransitory) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Copy constructor.
- Vector - Interface in dev.nm.algebra.linear.vector.doubles
-
An Euclidean vector is a geometric object that has both a magnitude/length and a direction.
- VectorAccessException - Exception in dev.nm.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 dev.nm.algebra.linear.vector.VectorAccessException
-
Constructs an instance of VectorAccessException
for out-of-range access.
- VectorAccessException(String) - Constructor for exception dev.nm.algebra.linear.vector.VectorAccessException
-
Constructs an instance of VectorAccessException
.
- VectorFactory - Class in dev.nm.algebra.linear.vector.doubles.operation
-
These are the utility functions that create new instances of vectors from existing ones.
- VectorMathOperation - Class in dev.nm.algebra.linear.vector.doubles.dense
-
This is a generic implementation of the math operations of
double
based
Vector
.
- VectorMathOperation() - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
- VectorMonitor - Class in dev.nm.misc.algorithm.iterative.monitor
-
- VectorMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.VectorMonitor
-
- VectorSizeMismatch - Exception in dev.nm.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 dev.nm.algebra.linear.vector.VectorSizeMismatch
-
Constructs an instance of SizeMismatch
.
- VectorSpace<V,F extends Field<F>> - Interface in dev.nm.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 dev.nm.geometry.TrigMath
-
Returns the versed sine or versine of an angle.
- vertex() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
-
Gets the node.
- VertexTree<T> - Class in dev.nm.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 dev.nm.graph.type.VertexTree
-
- vertices() - Method in interface dev.nm.geometry.polyline.PolygonalChain
-
Get a list of the vertices defining the chain.
- vertices() - Method in class dev.nm.geometry.polyline.PolygonalChainByArray
-
- vertices() - Method in interface dev.nm.graph.Graph
-
Gets the set of all vertices in this graph.
- vertices() - Method in interface dev.nm.graph.HyperEdge
-
Gets the set of vertices associated with the edge.
- vertices() - Method in class dev.nm.graph.type.SimpleArc
-
- vertices() - Method in class dev.nm.graph.type.SimpleEdge
-
- vertices() - Method in class dev.nm.graph.type.SparseDiGraph
-
Gets the set of all vertices in this graph, sorted by the number of parents.
- vertices() - Method in class dev.nm.graph.type.SparseGraph
-
- vertices() - Method in class dev.nm.graph.type.SparseTree
-
- vertices() - Method in class dev.nm.graph.type.VertexTree
-
- visitingTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction
-
- visitingTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
-
- visitingTemperature(int) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.TemperatureFunction
-
Gets the visiting temperature \(T^V_t\) at time t.
- visitTime() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
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Gets the first visit time of this node.
- Viterbi - Class in dev.nm.stat.hmm
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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 dev.nm.stat.hmm.Viterbi
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Constructs an Viterbi algorithm for an HMM.
- VMAInvertibility - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
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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 dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
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Construct the inverse representation of an ARMA model.
- VMAInvertibility(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
- VMAModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a multivariate MA model.
- VMAModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model.
- VMAModel(Vector, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model with unit variance.
- VMAModel(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model with zero-mean.
- VMAModel(Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model with unit variance and zero-mean.
- VMAModel(MAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
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Construct a multivariate MA model from a univariate MA model.
- VMAModel(VMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
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Copy constructor.
- volatility() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
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Gets the H volatility.
- Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
-
- Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
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- Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
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Gets the inverse (or transpose) of accumulated Householder right-reflections applied to A.