A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
All Classes All Packages
All Classes All Packages
All Classes All Packages
A
- a - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
- 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() - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
-
Get a.
- a() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
- A - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
-
This is either [A] or [ A] [-C]
- A - Variable in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix
-
a random matrix constructed by AS159
- 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.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
Get the constraint coefficients.
- 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 class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEquality
- A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequality
- 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() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
- 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() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets
A
as in eq. - 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(int, double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
-
Compute an.
- A_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
Combine all A data as a matrix.
- A_l() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- A_l_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
Combine all linear blocks as a matrix.
- A_q(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
Gets \({A^q}_i\).
- A_q_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
A^q = [{A^q}_1, {A^q}_2, ...
- A_u() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- 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(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the absolute values.
- abs(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the absolute values of a vector, element-by-element.
- ABSOLUTE - tech.nmfin.returns.ReturnsCalculators
-
The return is defined as the difference between the values of the portfolio.
- 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
andx0
. - AbsoluteErrorPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the absolute error penalties.
- 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.
- 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.
- 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
-
Construct an instance with
AbsoluteTolerance.DEFAULT_TOLERANCE
. - 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
-
This abstract implementation implements
Function.dimensionOfRange()
by always returning 1, andFunction.dimensionOfDomain()
by returning the input argument for the dimension of domain. - 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
-
This abstract implementation implements
Function.dimensionOfDomain()
andFunction.dimensionOfRange()
by returning the input arguments at constructor. - 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
-
Parameters for
ACERFunction
. - 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 containsq
,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, int[]) - 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.
- 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(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- add(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- 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(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Add a constant to all entries in this vector.
- 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(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 class dev.nm.number.doublearray.CompositeDoubleArrayOperation
- 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(double, T) - Method in class dev.nm.misc.algorithm.Bins
-
Add a valued item to the bin.
- 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(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(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(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(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(DenseData) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Add up the elements in
this
andthat
, element-by-element. - add(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- 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(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- add(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- add(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- add(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
- add(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
\(this + that\)
- 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, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Adds two vectors, element-by-element.
- 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(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(Complex) - Method in class dev.nm.number.complex.Complex
- 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.
- add(SOCPLinearEquality) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
- add(SOCPLinearInequality) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
- add(G) - Method in interface dev.nm.algebra.structure.AbelianGroup
-
+ : G × G → G
- add(T) - Method in class dev.nm.misc.datastructure.IdentityHashSet
- addActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add an active constraint by index.
- addActive(int[]) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add active indices.
- addActive(Collection<Integer>) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add active indices.
- addAll(Collection<? extends T>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
- addCheck(PairingCheck) - Method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
- addColAt(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Adds a column at i.
- addColAt(int, Object) - 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(double...) - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
-
Update the statistic with more data.
- addData(double...) - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
-
Update the statistic with more data.
- addData(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...) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMedian
- addData(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
- addData(double[], double[]) - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
-
Add the given two samples.
- addData(double[], double[]) - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
-
Add the given two samples.
- addData(double[], 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.moment.weighted.WeightedMean
- addData(double[], double[]) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
- 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.
- addEdge(Arc<VertexTree<T>>) - Method in class dev.nm.graph.type.VertexTree
- 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(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
- 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(int) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add an inactive constraint by index.
- addInactive(int[]) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add inactive indices.
- addInactive(Collection<Integer>) - Method in class dev.nm.misc.algorithm.ActiveSet
-
Add inactive indices.
- addIterate(Vector) - Method in class dev.nm.misc.algorithm.iterative.monitor.VectorMonitor
- 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
- 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(double, double[]) - Method in class dev.nm.misc.datastructure.MathTable
-
Adds a row to the table.
- 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(PanelData.Row) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Inserts a row of data into the panel.
- addRow(S, T, double...) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Inserts a row of data into the panel.
- addRowAt(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
-
Adds a row at i.
- addRowAt(int, Object) - 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(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
- 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
- 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) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution
-
Construct an asymptotic distribution for the augmented Dickey-Fuller 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.
- ADFAsymptoticDistribution1 - Class in dev.nm.stat.test.timeseries.adf
-
Deprecated.use instead
ADFAsymptoticDistribution
- 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(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.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
-
This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the
JohansenAsymptoticDistribution.TrendType.CONSTANT
case. - 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
-
This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the
JohansenAsymptoticDistribution.TrendType.CONSTANT_TIME
case. - 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
-
This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the
JohansenAsymptoticDistribution.TrendType.NO_CONSTANT
case. - 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) - 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, 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, 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.
- 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.
- AFTER - dev.nm.interval.IntervalRelation
-
X takes place after Y.
- 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() - 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.
- 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
- 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 - Variable in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
-
α: the shape parameter
- alpha() - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the set of adjusting coefficients, by columns.
- alpha() - Method in class dev.nm.stat.regression.linear.panel.FixedEffectsModel
-
Gets the individual/subject specific terms.
- 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.
- alpha(double) - Method in class dev.nm.stat.test.distribution.AndersonDarlingPValue
-
Gets the p-value for a test statistic.
- 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.
- 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.
- ANALYTIC - dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method
-
The line search is done analytically.
- ANALYTICAL - dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
-
use the analytical gradient formulae in the references, eqs.
- 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(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(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) - 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
andthat
. - angle(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the angle between two vectors.
- 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.
- angle(H) - Method in interface dev.nm.algebra.structure.HilbertSpace
-
∠ : H × H → F
- 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) - Constructor for class dev.nm.stat.random.variancereduction.AntitheticVariates
-
Estimates \(E(f(X_1))\) and use AntitheticVariates.INVERSE as the default antithetic path.
- AntitheticVariates(UnivariateRealFunction, RandomNumberGenerator, UnivariateRealFunction) - Constructor for class dev.nm.stat.random.variancereduction.AntitheticVariates
-
Estimates \(E(f(X_1))\), where f is a function of a random variable.
- 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
- APPROXIMATELY_MEDIAN_UNBIASED - dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
default: the resulting quantile estimates are approximately median-unbiased regardless of the distribution of the sample
- APPROXIMATELY_UNBIASED_IF_DATA_IS_NORMAL - dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
the resulting quantile estimates are approximately unbiased for the expected order statistics if the sample is normally distributed
- 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 dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
-
Checks whether the constraints are satisfied with a solution vector x.
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
- areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
- 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.SOCPLinearSectorExposure
- 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
-
Checks if all matrices are SparseMatrix.
- areAllSparse(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if all vectors are SparseVector.
- 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[], 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.
- 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.
- areOrthogonormal(Vector[], double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if a set of vectors are orthogonormal.
- areOrthogonormal(Vector, Vector, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
-
Checks if two 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, 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(IntTimeTimeSeries, ARIMAModel) - 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[], 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(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, double[], int, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model with unit variance.
- ARIMAModel(double, double[], int, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
-
Construct a univariate ARIMA model.
- 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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
-
Construct an ARIMA model, using random standard Gaussian innovations.
- 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.
- 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[], 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(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, 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, double[], int, double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Construct a univariate ARIMAX model.
- 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, int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Constructs a forecaster for a time series assuming ARMA model.
- 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.
- 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(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.
- 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(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, InnovationsAlgorithm, ARMAForecastOneStep) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Makes the h-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(double[], ARMAModel) - 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(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.
- 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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
Constructs a model with the default tolerance and maximum number of iterations.
- ARMAGARCHFit(double[], int, int, int, int, double, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
-
Constructs a model.
- 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[]) - 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(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[], 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[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Construct a univariate ARMA model.
- ARMAModel(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Copy constructor.
- armaxMean(double[], double[], double[]) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Compute the univariate ARMAX conditional mean.
- armaxMean(Matrix, Matrix, Vector) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Compute the multivariate 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[]) - 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(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[], 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[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
-
Construct a univariate ARMAX model.
- 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[]) - 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(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, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model with unit variance.
- ARModel(double, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
-
Construct a univariate AR model.
- 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() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
- ARResamplerFactory(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
- ARTIFICIAL - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
the artificial variable, x0, pp.
- ARTIFICIAL - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
- ARTIFICIAL_COST - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
the artificial objective, z0, pp.
- ARTIFICIAL_COST - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
- AS_26 - dev.nm.stat.descriptive.rank.Rank.TiesMethod
-
Algorithm AS 26.
- 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
- assertEqual(T, T, String, String) - Static method in class dev.nm.misc.ArgumentAssertion
- 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
- assertLessThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
- assertNegative(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test if
Number
x
is negative. - 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, NOTDouble.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, NOTFloat.NaN
nor infinity). - assertNotGreaterThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
- assertNotInfinity(double, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Check if an argument is NOT a
Double.POSITIVE_INFINITY
norDouble.NEGATIVE_INFINITY
. - assertNotInfinity(float, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Check if an argument is NOT a
Float.POSITIVE_INFINITY
norFloat.NEGATIVE_INFINITY
. - assertNotLessThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
- assertNotNaN(double, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Check if an argument is NOT a
Double.NaN
. - assertNotNaN(float, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Check if an argument is NOT a
Float.NaN
. - assertNotNull(Object, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Check if
obj
is notnull
. - assertNull(Object, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Check if
obj
isnull
. - assertPositive(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
-
Test if
Number
x
is positive. - assertRange(T, T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
- 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
- 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. - ASYMPTOTIC - dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest.Type
-
The default is the asymptotic distribution of Fisher's exact test.
- 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.
- AUGMENTED_DICKEY_FULLER - dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1.Type
-
Deprecated.the augmented version of the Dickey-Fuller test
- 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[]) - 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[], 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.
- 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[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
Automatically selects and estimates the ARIMA model using default parameters.
- 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.
- 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 usesSimpleMatrixMathOperation
. - 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
- AVERAGE - dev.nm.stat.descriptive.rank.Rank.TiesMethod
- 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, double) - Constructor for class dev.nm.stat.factor.implicitmodelpca.AverageImplicitModelPCA
-
Constructs an explicit-implicit model for a time series of vectored observations
- AverageImplicitModelPCA(Matrix, int) - 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
- 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.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() - 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.SOCPDualProblem1
-
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() - 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 interface dev.nm.stat.random.variancereduction.ControlVariates.Estimator
-
Gets the optimal b.
- b(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets the implicit factor loading for the n-th subject.
- b(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets the factor loading for the n-th subject.
- B - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
the constraint values (the last column)
- B - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
- 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.operation.Pow
-
Get the double precision matrix.
- B() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets B, the implicit factor loading matrix.
- B() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets B, the factor loading matrix.
- 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 class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets
B
as in eq. - B() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
- B(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the Brownian motion value at the i-th time point.
- B(int, double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
-
Compute bn.
- 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.
- BACKWARD - dev.nm.analysis.differentiation.univariate.FiniteDifference.Type
-
backward difference
- 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.
- BARTLETT - dev.nm.stat.factor.factoranalysis.FactorAnalysis.ScoringRule
-
Bartlett's (1937) weighted least-squares scores
- base() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow
-
Get the radix or base of the coefficient.
- BASIC - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
the basic variables, i.e., constraints
- 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.
- BEFORE - dev.nm.interval.IntervalRelation
-
X takes place before Y.
- begin() - Method in class dev.nm.interval.Interval
-
Get the beginning of this interval.
- 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
-
Creates a new instance that uses the given
RandomNumberGenerator
to do the trial. - 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 - Variable in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
-
β: the shape parameter
- beta - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
- 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() - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the set of cointegrating factors, by columns.
- 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() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
- beta(int) - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the r-th cointegrating factor, counting from 1.
- Beta - Class in dev.nm.analysis.function.special.beta
-
The beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \]
- Beta() - Constructor for class dev.nm.analysis.function.special.beta.Beta
- 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: \[ I_x(p,q) = \frac{B(x;\,p,q)}{B(p,q)} = \frac{1}{B(p,q)} \int_0^x t^{p-1}\,(1-t)^{q-1}\,dt, p > 0, q > 0 \]
- 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: \[ x = I^{-1}_{(p,q)}(u), 0 \le u \le 1 \]
- 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(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
Construct a Biconjugate Gradient (BiCG) solver.
- 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.
- 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(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(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) .
- 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
-
Constructs a new instance which computes the partial derivatives using
PartialDerivativesByCenteredDifferencing
. - BicubicInterpolation(BicubicInterpolation.PartialDerivatives) - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
-
Constructs 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, 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.
- 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.
- 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
-
Convert the number to
BigDecimal
. - 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) - Constructor for class dev.nm.stat.random.rng.univariate.BinomialRNG
-
Construct a random number generator to sample 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.
- 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
- 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.
- 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
-
Construct an instance with
Constants.EPSILON
as the tolerance andInteger.MAX_VALUE
as the maximum number of iterations. - BisectionRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.BisectionRoot
-
Construct an instance with the tolerance for convergence and the maximum number of iterations.
- BivariateArrayGrid - Class in dev.nm.analysis.curvefit.interpolation.bivariate
-
Implementation of
BivariateGrid
, backed by arrays. - BivariateArrayGrid(double[][], double[], double[]) - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
-
Constructs 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
-
Constructs 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.
- BLACK - dev.nm.graph.algorithm.traversal.DFS.Node.Color
-
seen and done
- 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() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
-
Construct a univariate standard Brownian motion.
- BMSDE(double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
-
Construct a univariate 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
-
This is an extension to
GeneralizedSimulatedAnnealingMinimizer
, which allows adding box constraints to bound solutions. - 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
-
A box minimizer solves a
BoxOptimProblem
. - 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() - Constructor for class dev.nm.stat.random.rng.univariate.normal.BoxMuller
-
Construct a random number generator to sample from the standard Normal distribution.
- 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.
- 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, Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
Constructs an optimization problem with box constraints.
- BoxOptimProblem(RealScalarFunction, BoxConstraints) - 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
-
Deprecated.use
LjungBox
- 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.root.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.root.univariate.bracketsearch.BracketSearchMinimizer
-
Construct a univariate minimizer using a bracket search method.
- BracketSearchMinimizer.Solution - Class in dev.nm.root.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.
- BREAKDOWN - dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
-
Thrown when the iterative algorithm fails to proceed during its iterations, due to, for example, division-by-zero.
- BrentCetaMaximizer - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
-
Searches for the maximal point of C(η) by Brent's method.
- BrentCetaMaximizer() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
-
Constructs a maximizer using the default epsilon (for the Brent's search algorithm).
- 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).
- BrentMinimizer - Class in dev.nm.root.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.root.univariate.bracketsearch.BrentMinimizer
-
Construct a univariate minimizer using Brent's algorithm.
- BrentMinimizer.Solution - Class in dev.nm.root.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(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(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.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() - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
- build(double) - Method in class tech.nmfin.meanreversion.hvolatility.Kagi
-
Makes a KAGI construction for the given random process.
- 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.
- BY_INDEX - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
-
This
Comparator
sorts the vector entries by their indices.
C
- c - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
n=c/p
- 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.socp.problem.SOCPDualProblem
- c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets c.
- c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEquality
- c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequality
- 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() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
- c(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets ci.
- c(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
- 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() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets
C
as in eq. - c_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- c_l() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- c_l_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- c_q(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
Gets \(c^q_i\).
- c_q_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- c_u() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- 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) - 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, RealVectorFunction, RntoMatrix) - 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[], ConcurrentCachedRLG) - 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.
- 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>, ConcurrentCachedRLG) - 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.
- CATMULL_ROM - dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents
-
Catmull-Rom splines are a special case of Cardinal splines and are defined as: \[ (\frac{\partial y}{\partial x})_k = \frac{y_{k+1} - y_{k-1}}{x_{k+1} - x_{k-1}}.
- 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(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.
- 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(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of vectors by columns.
- cbind(List<Vector>) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines a list of vectors 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(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) - 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
- 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(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(Vector) - Method in class dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
- 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
- CENTRAL - dev.nm.analysis.differentiation.univariate.FiniteDifference.Type
-
central difference
- 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).
- CHARACTERISTIC_POLYNOMIAL - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
-
For a matrix of dimension 4 or smaller.
- 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) - Constructor for class dev.nm.stat.random.rng.univariate.beta.Cheng1978
-
Constructs a random number generator to sample from the beta distribution.
- Cheng1978(double, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.beta.Cheng1978
-
Constructs a random number generator to sample from the beta distribution.
- children(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
- 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
- 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) - Constructor for class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
-
Assess whether the two random variables in the contingency table are independent.
- 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.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) - 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, 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.
- 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
-
This is a parallelized version of
CholeskyBanachiewicz
. - 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.
- CIRCULAR - dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type
- clamped() - Static method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline
-
Constructs 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
-
Constructs 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
- CLOSED - dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type
-
The first and the last terms in the Euler-Maclaurin formula are included in the sum.
- 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) - 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.
- 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, int, Matrix) - Constructor for class dev.nm.stat.cointegration.CointegrationMLE
-
Perform the Johansen MLE procedure on a multivariate time series.
- 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 anint
array. - collection2LongArray(Collection<Long>) - Static method in class dev.nm.number.DoubleUtils
-
Convert a collection of
Long
s to along
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() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
-
Constructs a combined maximizer.
- CombinedCetaMaximizer(CetaMaximizer[]) - 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) - 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.
- 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, 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.
- compare(double, double, double) - Static method in class dev.nm.number.DoubleUtils
-
Compares two
double
s up to a precision. - 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(Number, double) - Method in class dev.nm.number.complex.Complex
- compare(Number, double) - Method in interface dev.nm.number.NumberUtils.Comparable
-
Compare
this
andthat
numbers up to a precision. - compare(Number, Number, double) - Static method in class dev.nm.number.NumberUtils
-
Compare two numbers.
- compare(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compare two
BigDecimal
s up to a precision. - comparePeriods(Period, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Compares two periods, assuming 30 days per month.
- comparePeriods(Period, Period, int) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Compares two periods, with the assumed number of days per month.
- 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.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) - Constructor for class dev.nm.number.complex.Complex
-
Construct a complex number from a real number.
- Complex(double, double) - Constructor for class dev.nm.number.complex.Complex
-
Construct a complex number from the real and imaginary parts.
- ComplexMatrix - Class in dev.nm.algebra.linear.matrix.generic.matrixtype
-
This is a
Complex
matrix. - ComplexMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
Construct a
Complex
matrix. - ComplexMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
Construct a
Complex
matrix. - ComplexMatrix(Complex[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
-
Construct a
Complex
matrix. - CompositeDoubleArrayOperation - Class in dev.nm.number.doublearray
-
It is desirable to have multiple implementations and switch between them for, e.g., performance reason.
- CompositeDoubleArrayOperation(int, DoubleArrayOperation, DoubleArrayOperation) - Constructor for class dev.nm.number.doublearray.CompositeDoubleArrayOperation
-
Construct a
CompositeDoubleArrayOperation
that chooses an implementation by array length. - CompositeDoubleArrayOperation(CompositeDoubleArrayOperation.ImplementationChooser) - Constructor for class dev.nm.number.doublearray.CompositeDoubleArrayOperation
-
Construct a
CompositeDoubleArrayOperation
by supplying the multiplexing criterion and the multipleDoubleArrayOperation
s. - 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 asLehmer
, 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
-
Constructs a linear congruential generator from some simpler and shorter modulus generators.
- compute() - Method in class tech.nmfin.meanreversion.daspremont2008.AhatEstimation
- compute(double[][], double[][], int) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EpsilonStatisticsCalculator
-
Compute the statistics.
- 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(Vector, Vector, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.BlockSplitPointSearch
-
Searches splitting points in the symmetric tridiagonal matrix.
- 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(double[]...) - Static method in class dev.nm.number.DoubleUtils
-
Concatenate an array of arrays into one array.
- 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(Vector...) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Concatenates an array of vectors into one vector.
- concat(LinearConstraints...) - Static method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
-
Concatenate collections of linear constraints into one collection.
- concat(Collection<Vector>) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Concatenates an array of vectors into one vector.
- 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) - 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.
- 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.
- ConcurrentCachedRNG - Class in dev.nm.stat.random.rng.concurrent.cache
-
This is a fast thread-safe wrapper for random number generators.
- 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.
- 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.
- ConcurrentCachedRVG - Class in dev.nm.stat.random.rng.concurrent.cache
-
This is a fast thread-safe wrapper for random vector generators.
- 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.
- 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.
- ConcurrentStandardNormalRNG - Class in dev.nm.stat.random.rng.univariate.normal
- ConcurrentStandardNormalRNG() - Constructor for class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
- ConcurrentStandardNormalRNG(RandomStandardNormalGenerator) - 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
- conditionalForLoop(boolean, int, int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Runs a parallel for-loop only if
conditionToParallelize
istrue
. - conditionalForLoop(boolean, int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Calls
conditionalForLoop
withincrement
of 1. - conditionalMean(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
-
Compute the univariate AR1 conditional mean, given the last lag.
- 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(Matrix, Matrix) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Compute the multivariate ARMA conditional mean, given all the lags.
- 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) - 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, 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(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.
- 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.
- 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(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(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.
- 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(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(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.
- 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(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
Construct a Conjugate Gradient (CG) solver.
- 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.
- 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(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(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.
- CONSTANT - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
-
This is trend type III: constant, no linear trend:
- CONSTANT - dev.nm.stat.test.timeseries.adf.TrendType
-
test for a unit root with drift
- CONSTANT_RESTRICTED_TIME - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
-
This is trend type IV: constant, restricted linear trend:
- CONSTANT_TIME - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
-
This is trend type V: constant, linear trend:
- CONSTANT_TIME - dev.nm.stat.test.timeseries.adf.TrendType
-
test for a unit root with drift and deterministic time trend
- 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
-
Deprecated.This implementation is slow. Use
ConstantSigma1
instead. - 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) - 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, 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.
- 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
-
A constrained minimizer solves a constrained optimization problem, namely,
ConstrainedOptimProblem
. - 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 - 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.
- Constraints - Interface in dev.nm.solver.multivariate.constrained.constraint
-
A set of constraints for a (real-valued) optimization problem is a set of functions.
- 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(LocalDateTimeInterval) - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
- contains(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet
- contains(LocalDate) - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
- contains(LocalDateTime) - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
-
Whether this time interval contains the given time.
- contains(V) - Method in class dev.nm.graph.type.SparseGraph
-
Check if this graph contains a vertex.
- 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) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
-
Construct a continued fraction.
- 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.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
-
Estimates \(E(f(X_1))\), where f is a function of a random variable.
- 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
-
This exception is thrown by
IterativeLinearSystemSolver.solve(dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem, dev.nm.misc.algorithm.iterative.monitor.IterationMonitor)
when the iterative algorithm detects a breakdown or fails to converge. - 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(double) - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
- cor(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
- 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) - Constructor for class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
- CorrelationCheck(Matrix, double, 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 - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
the cost function (the last row)
- 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) - 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.
- 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.
- 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) - 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, double[]) - 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() - 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() - 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
- 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 - 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.
- 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) - Constructor for class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
-
Estimate the covariance matrix directly by using LASSO.
- CovarianceSelectionLASSO(CovarianceSelectionProblem, double) - 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(CovarianceSelectionProblem) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Copy constructor.
- CovarianceSelectionProblem(MultivariateTimeSeries, double) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Constructs a covariance selection problem from a multivariate time series.
- CovarianceSelectionProblem(MultivariateTimeSeries, double, boolean) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Constructs a covariance selection problem from a multivariate time series.
- 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) - 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, 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
-
Construct an instance with
CubicHermite.Tangents.CATMULL_ROM
as the method for computing tangents. - 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
-
Constructs an instance with default end conditions which fits natural splines, meaning that the second derivative at both ends are zero.
- 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.
- cumsum(Vector[]) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets the cumulative sums.
- 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) /]
- 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
- d() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
-
Gets 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 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.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
Computes D.
- D() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets
D
as in eq. - D() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
- 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) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM
-
Copy constructor.
- Dai2011HMM(Dai2011HMM.ModelParam) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM
- 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
LocalDateTime
and value can be any type. - DateTimeGenericTimeSeries(LocalDateTime[], 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 aDateTime
-indexed time series. - DateTimeTimeSeries - Class in dev.nm.stat.timeseries.datastructure.univariate
-
This is a time series has its
double
values indexed byLocalDateTime
. - DateTimeTimeSeries(LocalDateTime[], double[]) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.DateTimeTimeSeries
-
Construct a time series from
LocalDateTime
anddouble
. - DateTimeTimeSeries(List<LocalDateTime>, List<Double>) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.DateTimeTimeSeries
-
Construct a time series from
LocalDateTime
anddouble
. - dayCount() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
- db(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE
-
\[ \frac{d\sigma}{dt} \]
- dB(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get a Brownian motion increment.
- dB(double) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
-
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.
- 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 thisMatrix
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 justthis
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 - dev.nm.number.DoubleUtils.RoundingScheme
-
This rounding scheme is the same as in
Math.round(float)
. - 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_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
- DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
- DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
- DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
- DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
- DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
- 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(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(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() - Constructor for class dev.nm.solver.multivariate.initialization.DefaultSimplex
-
Construct a simplex builder.
- DefaultSimplex(double) - 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 - Variable in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
- DeflationCriterion - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
Determines whether a sub-diagonal entry is sufficiently small to be neglected.
- 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.
- DELETED - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
the deleted rows and columns
- 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) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Construct a storage.
- 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.
- DenseMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense
-
This class implements the standard, dense,
double
based matrix representation. - 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(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a 0 matrix of dimension nRows * nCols.
- 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.
- DenseMatrix(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
-
Constructs a column matrix from a vector.
- 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(double...) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a
double[]
. - DenseVector(int) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector.
- DenseVector(int[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a
int[]
. - DenseVector(int, double) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by repeating a value.
- DenseVector(Matrix) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector from a column or row matrix.
- DenseVector(DenseVector) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Copy constructor.
- DenseVector(Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Casts any vector to a
DenseVector
. - DenseVector(Double[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a
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(List<Double>) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
-
Constructs a vector, initialized by a
List<Double>
. - 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(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) - 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(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
- 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(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(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(Vector) - Method in class dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
- 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
- DEOptim - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
-
Differential Evolution (DE) is a global optimization method.
- 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(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(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.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
-
A
DEOptimCellFactory
produces DEOptimCellFactory.DeOptimCells. - 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(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
- 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
- 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.root.univariate - package dev.nm.root.univariate
- dev.nm.root.univariate.bracketsearch - package dev.nm.root.univariate.bracketsearch
- 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.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() - 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.
- 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
- 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() - 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.
- 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.
- 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) - 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(UnivariateRealFunction, Dfdx.Method) - Constructor for class dev.nm.analysis.differentiation.univariate.Dfdx
-
Construct 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(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Gets the diagonal of a matrix.
- 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.
- 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
- DICKEY_FULLER - dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1.Type
-
Deprecated.the original version of the Dickey-Fuller test, developed in 1979
- diff(double[]) - Static method in class dev.nm.number.DoubleUtils
-
Gets the first differences of an array.
- diff(double[][]) - Static method in class dev.nm.number.DoubleUtils
-
Gets the first differences of an array of vectors.
- diff(double[][], int, int) - Static method in class dev.nm.number.DoubleUtils
-
Gets the lagged and iterated differences of vectors.
- diff(double[], int, int) - Static method in class dev.nm.number.DoubleUtils
-
Gets the lagged and iterated differences.
- diff(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
-
Construct an instance of
MultivariateGenericTimeTimeSeries
by taking the first differenced
times. - diff(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of
MultivariateSimpleTimeSeries
by taking the first differenced
times. - diff(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Construct an instance of
GenericTimeTimeSeries
by taking the first differenced
times. - diff(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of
SimpleTimeSeries
by taking the first differenced
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() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE
-
Get the diffusion coefficient: \(\sigma(t, X_t, Z_t, ...)\).
- Diffusion - Interface in dev.nm.stat.stochasticprocess.univariate.sde.coefficients
-
This class represents the diffusion term, σ, of a univariate SDE.
- 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.problem.SOCPDualProblem1
- dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1.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.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
-
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.SOCPLinearBlackList
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
- dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
- 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.SOCPLinearSectorExposure
- 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.SOCPLinearBlackList
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
- dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
- 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.SOCPLinearSectorExposure
- 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[]) - Constructor for class dev.nm.stat.distribution.multivariate.DirichletDistribution
-
Constructs an instance of Dirichlet distribution.
- DirichletDistribution(double[], 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 - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
- Discretization(double, double, double) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid.Discretization
-
Constructs a discretization of an interval.
- 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(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(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- 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(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
bythat
, entry-by-entry. - 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(Complex) - Method in class dev.nm.number.complex.Complex
-
Compute the quotient of this complex number divided by another complex number.
- divide(Real) - Method in class dev.nm.number.Real
- divide(Real, int) - Method in class dev.nm.number.Real
-
/ : R × R → R
- divide(F) - Method in interface dev.nm.algebra.structure.Field
-
/ : F × F → F
- 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) - 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, double[], RandomStandardNormalGenerator) - 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, 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.
- 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.
- dotProduct(double[], double[]) - Static method in class dev.nm.analysis.function.FunctionOps
-
\(x_1 \cdot x_2\)
- dotProduct(long[], long[]) - Static method in class dev.nm.analysis.function.FunctionOps
-
\(x_1 \cdot x_2\)
- doubleArray2intArray(double...) - Static method in class dev.nm.number.DoubleUtils
-
Convert a
double
array to anint
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
andint
. - DoubleUtils.ifelse - Interface in dev.nm.number
-
Return a value with the same shape as
test
which is filled with elements selected from eitheryes
orno
depending on whether the element of test istrue
orfalse
. - 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
- DOWN - dev.nm.number.DoubleUtils.RoundingScheme
-
Always round down.
- DOWN - tech.nmfin.meanreversion.hvolatility.Kagi.Trend
- 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
ande
values to high relative accuracy. - dReturns() - Method in class tech.nmfin.signal.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() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE
-
Get the drift: \(\mu(t,X_t,Z_t,...)\).
- Drift - Interface in dev.nm.stat.stochasticprocess.univariate.sde.coefficients
-
This class represents the drift term, μ, of a univariate SDE.
- 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 leadingnItems
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 leadingnItems
entries. - drop(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Construct an instance of
GenericTimeTimeSeries
by dropping the leadingnItems
entries. - drop(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of
SimpleTimeSeries
by dropping the leadingnItems
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 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
- dt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the current time differential.
- 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(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the i-th time increment.
- 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. - DURING - dev.nm.interval.IntervalRelation
-
X during Y.
- DURING_INVERSE - dev.nm.interval.IntervalRelation
-
Y during X.
- 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() - 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
- 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
- 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() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Gets the first order derivative function.
- 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
- 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
- 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() - Method in class dev.nm.graph.type.SparseGraph
- edges() - 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
- edges(V) - Method in interface dev.nm.graph.Graph
-
Gets the set of all edges associated with a vertex in this graph.
- edges(V) - Method in class dev.nm.graph.type.SparseGraph
- edges(V) - Method in class dev.nm.graph.type.SparseTree
- eigen() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
-
Gets the eigenvalue decomposition of the correlation (or covariance) matrix.
- 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, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Use
Eigen.Method.QR
method by default. - 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, 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 - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test
-
the EIGEN test
- 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) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Constructs an Elliott's Kalman filter model.
- Elliott2005DLM(double, double, double, double, double, RandomStandardNormalGenerator) - 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[], double, double, double, double, int) - Constructor for class tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter
- ElliottOnlineFilter(double[], Elliott2005DLM, 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[]) - Constructor for class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
Construct an empirical distribution from a sample using the default quantile type
Quantile.QuantileType.APPROXIMATELY_MEDIAN_UNBIASED
. - EmpiricalDistribution(double[], Quantile.QuantileType) - Constructor for class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
Construct an empirical distribution from a sample.
- end() - Method in class dev.nm.interval.Interval
-
Get the end of this interval.
- end() - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
- 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
-
Deprecated.Not supported yet.
- entropy() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
- entropy() - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
Deprecated.Not supported yet.
- entropy() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
-
Deprecated.Not supported yet.
- 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(double, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.Realization.Entry
-
Construct an instance of
TimeSeries.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.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
- Entry(int, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.IntTimeTimeSeries.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(MatrixCoordinate, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
Construct a sparse entry in a sparse matrix.
- Entry(T, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
-
Construct an instance of
Entry
. - Entry(T, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
-
Construct an instance of
TimeSeries.Entry
. - epsilon - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer
-
the convergence tolerance
- epsilon - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
- 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() - 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 - Static variable in class dev.nm.misc.Constants
-
the default epsilon used in this library
- 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 2D arrays,
double[][]
, are close enough, hence equal, entry-by-entry. - 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
double
s are close enough, hence equal. - 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. - EQUAL - dev.nm.interval.IntervalRelation
-
X is equal to Y.
- EQUAL - dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
-
two-sample; two-sided
- EQUALITY - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
the equality constraints
- EqualityConstraints - Interface in dev.nm.solver.multivariate.constrained.constraint
-
The domain of an optimization problem may be restricted by equality constraints.
- 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, \(\max_y \mathbf{b'y} \textrm{ s.t.,} \\ \mathbf{\hat{A}_i'y + s_i = \hat{c}_i} \\ s_i \in K_i, i = 1, 2, ..., q\).
- EqualityConstraints(Vector, Matrix[], Vector[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1.EqualityConstraints
-
Constructs the equality constraints for a dual SOCP problem, \[ \max_y \mathbf{b'y} \textrm{ s.t.,} \\ \mathbf{{A^q}_i'y + s_i = c^q_i}, s_i \in K_i, i = 1, 2, ..., q \\ \mathbf{{A^{\ell}}^T y + z^{\ell} = c^{\ell}} \\ \mathbf{{A^{u}}^T y = c^{u}} \\ \]
- 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, \(\sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0}\).
- 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(SparseMatrix, SparseMatrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Checks if two SparseMatrixs are equal.
- 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.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(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(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(Object) - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
- equals(Object) - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
- 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
- equals(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Check if two
BigDecimal
s are equal up to a precision. - 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, 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.
- 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.
- 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.
- 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 dev.nm.stat.covariance.nlshrink.LedoitWolf2016
- 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() - Method in class dev.nm.stat.covariance.nlshrink.quest.QuEST
- evaluate(double) - Method in class dev.nm.analysis.curvefit.interpolation.LinearInterpolator
- evaluate(double) - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
- evaluate(double) - Method in class dev.nm.analysis.differentiation.Ridders
-
Evaluate f'(x), where f is a
UnivariateRealFunction
. - 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) - Method in class dev.nm.analysis.function.matrix.R1toConstantMatrix
- evaluate(double) - Method in class dev.nm.analysis.function.matrix.R1toMatrix
-
Evaluate f(x) = A.
- evaluate(double) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.RealScalarSubFunction
-
Evaluate the function f at x.
- evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
-
Evaluate y = f(x).
- 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(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) - 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) - 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(double) - Method in interface dev.nm.analysis.sequence.Summation.Term
-
Evaluate the term for an index.
- 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(double) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
- 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(double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
-
Get the i-th auto-correlation matrix.
- evaluate(double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
-
Get the i-th auto-covariance matrix.
- evaluate(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
-
Get the i-th auto-correlation.
- 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) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.NegCetaFunction
- evaluate(double, double) - Method in class dev.nm.analysis.differentiation.univariate.DBeta
-
Evaluate \({\partial \over \partial x} \mathrm{B}(x, y)\).
- 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(double, double) - Method in class dev.nm.analysis.function.matrix.R2toMatrix
-
Evaluate f(x1, x2) = A.
- evaluate(double, double) - Method in interface dev.nm.analysis.function.rn2r1.BivariateRealFunction
-
Evaluate y = f(x1,x2).
- evaluate(double, double) - Method in class dev.nm.analysis.function.special.beta.Beta
-
Evaluate B(x,y).
- evaluate(double, double) - Method in class dev.nm.analysis.function.special.beta.LogBeta
-
Compute
log(B(x,y))
. - 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, double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
- 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(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
- evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
- evaluate(double, double) - Method in interface tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMomentsEstimator
- evaluate(double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.NPEBPortfolioMomentsEstimator
- evaluate(double, double, double) - Method in interface dev.nm.analysis.function.rn2r1.TrivariateRealFunction
-
Evaluate y = f(x1,x2,x3).
- evaluate(double, Vector) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.problem.DerivativeFunction
-
Computes the derivative at the given point, x.
- 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(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
Compute the auto-correlation for lag
k
. - evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
-
Compute the auto-covariance for lag
k
. - evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
Compute the partial auto-correlation for lag
k
. - evaluate(D) - Method in interface dev.nm.analysis.function.Function
-
Evaluate the function f at x, where x is from the domain.
- 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.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) - Method in class dev.nm.analysis.differentiation.Ridders
-
Evaluate the function f at x, where x is from the domain.
- evaluate(Vector) - Method in class dev.nm.analysis.function.matrix.R1toMatrix
- evaluate(Vector) - Method in class dev.nm.analysis.function.matrix.R2toMatrix
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.AbstractTrivariateRealFunction
- 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(Vector) - Method in class dev.nm.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2rm.AbstractR1RnFunction
- evaluate(Vector) - Method in class dev.nm.analysis.function.rn2rm.RealVectorSubFunction
- evaluate(Vector) - Method in class dev.nm.analysis.function.special.beta.MultinomialBetaFunction
- evaluate(Vector) - Method in class dev.nm.analysis.function.special.Rastrigin
- 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(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(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
- evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
- evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
- evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
- evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
- 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.SOCPLinearSectorExposure
- 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
- 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, 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(Vector, Vector) - Method in interface dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings.ProposalDensityFunction
-
Evaluates the density at the given points.
- evaluate(Complex) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(Constraints, Vector) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
-
Evaluates the constraints.
- 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(Ft) - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.FtAdaptedFunction
-
Evaluate this function, f, at time t.
- evaluate(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction
- evaluate(Realization[]) - Method in interface dev.nm.stat.cointegration.JohansenAsymptoticDistribution.F
-
F(B).
- evaluate(Number) - Method in class dev.nm.analysis.function.polynomial.Polynomial
-
Evaluate this polynomial at x.
- evaluate(BigDecimal) - Method in class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
-
Evaluate z.
- evaluate(X) - Method in interface dev.nm.stat.distribution.discrete.ProbabilityMassFunction
-
Compute the probability mass for a discrete realization x.
- 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.
- EXACT - dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest.Type
-
This is the exact distribution for Fisher's exact test.
- 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 thisMutex
instance. - 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. - 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 astasks
. - 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).
- 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).
- exp(double) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute ex.
- exp(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the exponentials of values.
- exp(double, int) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Compute ex.
- exp(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the exponential of a vector, element-by-element.
- exp(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Exponential of a complex number.
- 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.
- 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(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(RandomRealizationGenerator, 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, double) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
- ExplicitImplicitModelPCA(Matrix, Matrix, int) - 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
- f - Variable in class dev.nm.analysis.function.SubFunction
-
the original, unrestricted function
- f - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
- f - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
- f - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
- f - Variable in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
-
the integrand
- 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.problem.SOCPDualProblem1
- 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() - 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() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
Get the objective function.
- f() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
- f() - Method in interface dev.nm.solver.problem.OptimProblem
-
Get the objective function.
- f() - Method in interface dev.nm.stat.regression.linear.panel.PanelData.Transformation
-
Gets the transformation.
- 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) - 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.elliptic.dim2.PoissonEquation2D
-
The forcing term.
- 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, 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(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets the implicit factor values at time t.
- f(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
-
Gets the factor values at time t.
- F - Class in dev.nm.stat.test.variance
-
The F-test tests whether two normal populations have the same variance.
- F - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
the scaling factor
- F - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
unique dis_G_list
- 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() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
-
Get the differential, \(y^{(n)} = F\).
- F() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
-
Gets the sample constant correlation matrix F.
- F() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets F, the implicit factor value matrix.
- 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.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_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) - 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(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, 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.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() - 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(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.
- 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.root.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.root.univariate.bracketsearch.FibonaccMinimizer
-
Construct a univariate minimizer using the Fibonacci method.
- FibonaccMinimizer.Solution - Class in dev.nm.root.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(double[]) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Filter the observations without control variable.
- filtering(double[], double[]) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
-
Filter the observations.
- filtering(MultivariateIntTimeTimeSeries) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Filter the observations without control variable.
- filtering(MultivariateIntTimeTimeSeries, MultivariateIntTimeTimeSeries) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
-
Filter the observations.
- 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).
- FINISH - dev.nm.interval.IntervalRelation
-
X finishes Y
- FINISH_INVERSE - dev.nm.interval.IntervalRelation
-
Y finishes X.
- 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.
- FINITE_DIFFERENCE - dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents
-
The simplest choice is the three-point difference, not requiring constant interval lengths.
- FINITE_DIFFERENCE - dev.nm.analysis.differentiation.univariate.Dfdx.Method
-
Finite difference: approximate a derivative using grid points.
- 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
- first() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
- FIRST - dev.nm.stat.descriptive.rank.Rank.TiesMethod
- 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(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
-
Construct a multivariate minimizer using the First-Order method.
- 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.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) - Constructor for class dev.nm.stat.test.distribution.pearson.FisherExactDistribution
-
Construct the distribution for Fisher's exact test.
- FisherExactDistribution(int[], int[], int, RandomLongGenerator) - Constructor for class dev.nm.stat.test.distribution.pearson.FisherExactDistribution
-
Construct the distribution for Fisher's exact test.
- fit - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
- 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(double[], double[], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.LinearFit
-
Fit the ACER function with OLS.
- 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(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(EmpiricalACER, double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
-
Fit ACER function with empirical ACER estimates.
- 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
- 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() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit
- fittedValues() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit
- fittedValues(OLSResiduals[]) - Static method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel
- 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[]) - 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[], PanelData.Transformation[]) - Constructor for class dev.nm.stat.regression.linear.panel.FixedEffectsModel
-
Constructs a "within" fixed effects model from a panel of data.
- fixedSizeIntervals(LocalDateTime, LocalDateTime, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Creates a list of intervals between
start
andend
every so often. - 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;
- flag_s() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- flag_u() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- FletcherLineSearch - Class in dev.nm.solver.multivariate.unconstrained.c2.linesearch
-
This is Fletcher's inexact line search 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.
- 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.
- FletcherPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
-
This penalty function sums up the squared costs penalties.
- FletcherPenalty(LessThanConstraints) - 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, double[]) - 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(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.
- 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(Object[], Object[], double[][]) - Constructor for class dev.nm.misc.datastructure.FlexibleTable
-
Constructs a flexible table that can shrink or grow.
- 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 - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the best minimum found so far
- fmin - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
- Fmin(double, int) - Static method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
-
Compute the F critical value.
- fminLast - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
- fnext - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the next guess of the minimum
- foreach(double[], UnivariateRealFunction) - Static method in class dev.nm.number.DoubleUtils
-
Apply a univariate function f to each element in an array.
- 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(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(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(Vector, DoubleUnaryOperator) - 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(Iterable<T>, IterationBody<T>) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Runs a "foreach" loop in parallel.
- 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. - 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.
- 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. - 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.
- foreachVector(Vector[], RealScalarFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Applies a
RealScalarFunction
on each input vector. - 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>, RealScalarFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Applies a
RealScalarFunction
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, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Runs a for-loop in parallel.
- forLoop(int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
-
Calls
forLoop
withincrement
of 1. - forward(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
-
Perform a forward sweep.
- FORWARD - dev.nm.analysis.differentiation.univariate.FiniteDifference.Type
-
forward difference
- 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
- FRECHET - dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
-
Frechet distribution.
- 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.
- FREE - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
the free variables
- 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.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.
- Ft() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
-
Compute all function values at all time points.
- 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
-
This is the
RuntimeException
thrown when it fails to evaluate an expression. - 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
- g - Variable in class dev.nm.graph.algorithm.traversal.TraversalFromRoots
- g() - Method in interface dev.nm.analysis.differentiation.differentiability.C1
-
Get the gradient function, g, of a real valued function f.
- g() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
- 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.elliptic.dim2.PoissonEquation2D
-
The boundary value function.
- 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(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 - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
- gamma() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
-
Gets the estimated sequence of gamma (arc length).
- 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 - 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(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 - 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() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
-
Gets Γ, the explicit factor loading matrix.
- 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: \[ P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0 \]
- 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. Whens <= 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) - 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, 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.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() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
- GARCHResamplerFactory(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
- GARCHResamplerFactory2 - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
- GARCHResamplerFactory2() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
- GARCHResamplerFactory2(RandomLongGenerator) - 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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
-
Simulate an GARCH model.
- 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.
- 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() - Constructor for class dev.nm.analysis.function.special.gaussian.Gaussian
-
Construct an instance of the standard Gaussian function: \(f(x) = e^{-{\frac{(x)^2}{2}}}\)
- Gaussian(double, double, double) - Constructor for class dev.nm.analysis.function.special.gaussian.Gaussian
-
Construct an instance of the Gaussian function.
- GAUSSIAN_JORDAN_ELIMINATION - dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel.Method
-
use Gauss-Jordan elimination; cheap but subject to numerical stability (rounding errors)
- 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) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Run the Gaussian elimination algorithm with partial pivoting.
- GaussianElimination(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Run the Gaussian elimination algorithm.
- GaussianElimination4SquareMatrix - Class in dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination
-
This is a wrapper for
GaussianElimination
but applies only to square matrices. - GaussianElimination4SquareMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
-
Run the Gaussian elimination algorithm on a square matrix.
- GaussianElimination4SquareMatrix(Matrix, double) - 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(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.
- GaussianProposalFunction(Matrix, 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) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Run the Gauss-Jordan elimination algorithm.
- GaussJordanElimination(Matrix, boolean, double) - 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() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
- 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(RealScalarFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
-
Construct an instance of constraints from an array of real-valued functions.
- 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.
- GeneralEqualityConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.general
-
This is the collection of equality constraints for an optimization problem.
- 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.
- 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.
- 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(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.
- 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.
- 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(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.
- 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.
- 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) - Constructor for class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
-
Solves a generalized linear problem using the Iterative Re-weighted Least Squares algorithm.
- GeneralizedLinearModel(GLMProblem, GLMFitting) - Constructor for class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
-
Constructs a
GeneralizedLinearModel
instance. - 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(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.
- 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.
- 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
-
Tsallis and Stariolo (1996) proposed this variant of
SimulatedAnnealingMinimizer
(SA). - 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(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.
- 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.
- 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() - Method in class dev.nm.misc.parallel.Reference
- 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.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- 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) - 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(int) - Method in class dev.nm.interval.Intervals
-
Get the i-th interval.
- get(int) - Method in class dev.nm.misc.datastructure.MathTable.Row
-
Gets the value in the row by column index.
- get(int) - Method in class dev.nm.misc.datastructure.SortableArray
- get(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
- get(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
- 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(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...) - 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, 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, 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, int) - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
Get the value of a table entry.
- get(int, int) - Method in class dev.nm.misc.datastructure.FlexibleTable
- 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.
- get(String) - Method in class dev.nm.misc.datastructure.MathTable.Row
-
Gets the value in the row by column name.
- get(String) - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
- get(T) - Method in class dev.nm.combinatorics.Ties
-
Get the number of occurrences of an object.
- get(T) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
-
Get the value at time
t
. - 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() - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Get the orthogonal basis.
- 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.
- 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() - Method in class dev.nm.stat.evt.cluster.Clusters
-
Get the list of clusters.
- getClusters(double[]) - Method in class dev.nm.stat.evt.cluster.ClusterAnalyzer
-
Count clusters from the given observations.
- 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) - 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
- 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 toendRow
row, inclusively. - 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
-
Gets the ConstrainedOptimSubProblem representation of the sub-problem.
- 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
- getConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1.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, LocalDateTimeInterval) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.CovarianceEstimator
- getCovariances(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - 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. - 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>) - Static method in class dev.nm.graph.GraphUtils
- getDisjointGraphs(UnDiGraph<V, E>, GraphUtils.GraphFactory<G>) - Static method in class dev.nm.graph.GraphUtils
-
Separate an undirected graph into disjointed connected graphs.
- 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.
- 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.
- 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() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
- getEigenVector() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Get an eigenvector.
- getEigenVector(Vector, int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
-
Get an eigenvector from an initial guess.
- 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.SymmetricQRAlgorithm
-
Gets the eigenvectors of A, which are the columns of Q.
- getEigenVectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
- 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
-
Exports the non-zero values in the matrix as a list of
SparseMatrix.Entry
s. - 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.problem.SOCPDualProblem1
- 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() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting
-
Calculate the LARS fitting estimators.
- 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.
- 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() - 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() - 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.
- 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(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.
- 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.
- 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[]) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
-
Fit an OU process by using MLE.
- 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 interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFitting
-
Get the fitted OU process.
- getFittedOU(double[], double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
- 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() - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1
-
Generate a set of initial points for optimization.
- getInitials() - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
-
Generate a set of initial points for optimization.
- 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(Vector...) - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
- 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.
- 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.
- 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.problem.SOCPDualProblem1
- 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() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
- 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(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
-
Deprecated.Not supported yet.
- 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, LocalDateTimeInterval) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.MeanEstimator
- getMeans(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - 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
- 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(int) - Method in class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable
-
Get the observations to compute an empirical distribution.
- getObservations(HmmInnovation[], int) - Static method in class dev.nm.stat.markovchain.MCUtils
-
Get all observations that occur in a particular state.
- getOffsetVectors(Vector, Vector, int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Given the reference vector
v0
, the deltadv
, 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() - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
-
Gets the optimal risk aversion coefficient w.r.t.
- getOptimalRiskAversionCoefficient(double, double, double) - 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() - 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, LocalDateTimeInterval) - Method in class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm
-
Computes the optimal weights for the products using returns.
- getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in class tech.nmfin.portfoliooptimization.TopNOptimizationAlgorithm
- getOrderedNodes() - Method in class dev.nm.graph.algorithm.traversal.BFS
- 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.
- getOrderedNodes(Collection<V>) - Method in class dev.nm.graph.algorithm.traversal.BottomUp
-
Gets the list of visited nodes, in the order of being visited.
- 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.Row>) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the values from a panel data.
- getPanelValuesByHeaders(List<PanelData.Row>, String[]) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the values from a panel data.
- getPanelValuesByHeaders(List<PanelData.Row>, String[], PanelData.Transformation[]) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the (transformed) values from a panel data.
- getPanelValuesByTime(List<PanelData.Row>) - Method in class dev.nm.stat.regression.linear.panel.PanelData
-
Gets the (transformed) values from a panel data.
- getPanelValuesByTime(List<PanelData.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.
- 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.
- getPortfolioConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
-
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(int) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
- getPossiblePairs(List<String>, List<String>) - 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(int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Get the i-th
EigenProperty
. - getProperty(Number) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
-
Get the
EigenProperty
by eigenvalue. - 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
-
Deprecated.Not supported yet.
- getRegime(Matrix) - Method in class tech.nmfin.signal.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() - Method in exception dev.nm.misc.parallel.MultipleExecutionException
-
Get the results obtained so far.
- 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.
- 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(int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
- 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
- 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) - 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(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 toendCol
column, inclusively. - getRow(S, T) - 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.signal.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
-
Get a
Statistic
. - 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
-
Gets the sub-problem in the form of ConstrainedOptimProblem.
- 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
-
Gets the specified
Tolerance
instance. - 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() - 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
- getValue(String) - Static method in class dev.nm.misc.license.License
- 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() - 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.
- getVariables() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
-
Get the list of all variables and their beginning indices starting from 0.
- getVariables(SOCPConstraints) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
-
Gets the variables involved in SOCPGeneralConstraints.
- 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(GLMBinomial) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
Construct a Binomial family.
- GLMFamily(GLMExponentialDistribution, LinkFunction) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
-
Construct an instance of
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() - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
-
Construct a
GlobalSearchByLocalMinimizer
to solve unconstrained minimization problems. - 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. - GOLDEN_RATIO - Static variable in class dev.nm.misc.Constants
-
the Golden ratio
- GoldenMinimizer - Class in dev.nm.root.univariate.bracketsearch
-
This is the golden section univariate minimization algorithm.
- GoldenMinimizer(double, int) - Constructor for class dev.nm.root.univariate.bracketsearch.GoldenMinimizer
-
Construct a univariate minimizer using the Golden method.
- GoldenMinimizer.Solution - Class in dev.nm.root.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.
- GOLUB_KAHAN - dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD.Method
-
Golub-Kahan, for higher precision.
- 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(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.
- 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.
- 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) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
-
Run the Gram-Schmidt process to orthogonalize a matrix.
- 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.
- 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).
- GRAY - dev.nm.graph.algorithm.traversal.DFS.Node.Color
-
on the current path
- GREATER - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side
-
compute Dn+; check whether the cdf of a sample lies above the null hypothesis
- GREATER - dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
-
two-sample; one-sided
- 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
-
Constructs a maximizer using the default grid size for even grid search.
- 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.root.univariate
-
This performs a grid search to find the minimum of a univariate function.
- GridSearchMinimizer(double) - Constructor for class dev.nm.root.univariate.GridSearchMinimizer
-
Constructs an instance with a given grid size for even grid.
- GridSearchMinimizer(int) - Constructor for class dev.nm.root.univariate.GridSearchMinimizer
-
Constructs an instance with a given number of grid points for even grid.
- GridSearchMinimizer(GridSearchMinimizer.GridDefinition) - Constructor for class dev.nm.root.univariate.GridSearchMinimizer
-
Constructs an instance with a user-defined grid.
- GridSearchMinimizer.GridDefinition - Interface in dev.nm.root.univariate
- GridSearchMinimizer.Solution - Class in dev.nm.root.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) - 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, 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.
- 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.
- GUMBEL - dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
-
Gumbel distribution.
- 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.
H
- h() - Method in interface dev.nm.analysis.integration.univariate.riemann.IterativeIntegrator
-
Get the discretization size for the current iteration.
- h() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
- h() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
- H - Variable in class tech.nmfin.signal.infantino2010.Infantino2010PCA
- H() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition
-
Gets the H matrix.
- H() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Get the Householder matrix H = I - 2 * v * v'.
- H() - Method in interface dev.nm.analysis.differentiation.differentiability.C2
-
Get the Hessian matrix function, H, of a real valued function f.
- H() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
- H() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
- H() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
- H(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Gets H(t), the covariance matrix of ut.
- H(int) - Method in class dev.nm.stat.dlm.univariate.StateEquation
-
Get H(t), the variance of ut.
- H(LeapFrogging.DynamicsState, RealScalarFunction, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
-
Evaluates a system's total energy at a given state.
- Hadi() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics
-
Hadi's influence measure.
- HalleyRoot - Class in dev.nm.analysis.root.univariate
-
Halley's method is an iterative root finding method for a univariate function with a continuous second derivative, i.e., a C2 function.
- HalleyRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.HalleyRoot
-
Construct an instance of Halley's root finding algorithm.
- HarveyGodfrey - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
-
The Harvey-Godfrey test tests for conditional heteroskedasticity.
- HarveyGodfrey(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.HarveyGodfrey
-
Perform the Harvey-Godfrey test to test for heteroskedasticity in a linear regression model.
- hasDuplicate(double[], double) - Static method in class dev.nm.number.DoubleUtils
-
Check if a
double
array contains any duplicates. - hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
- hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- hashCode() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
- hashCode() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
- hashCode() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- hashCode() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
- hashCode() - Method in class dev.nm.analysis.function.polynomial.Polynomial
- hashCode() - Method in class dev.nm.analysis.function.tuple.Pair
- hashCode() - Method in class dev.nm.interval.Interval
- hashCode() - Method in class dev.nm.interval.Intervals
- hashCode() - Method in class dev.nm.misc.datastructure.FlexibleTable
- hashCode() - Method in class dev.nm.misc.datastructure.IdentityHashSet
- hashCode() - Method in class dev.nm.misc.datastructure.SortableArray
- hashCode() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
- hashCode() - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
- hashCode() - Method in class dev.nm.number.complex.Complex
- hashCode() - Method in class dev.nm.number.Real
- hashCode() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
- hashCode() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
- hashCode() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
- hashCode() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
- hashCode() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
- hashCode() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
- hashCode() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
- hashCode() - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
- hasNext() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
- hasZero(double[], double) - Static method in class dev.nm.number.DoubleUtils
-
Check if a
double
array has any 0. - hav(double) - Static method in class dev.nm.geometry.TrigMath
-
Returns the haversed sine or haversine of an angle.
- HConstruction<T extends Comparable<? super T>> - Class in tech.nmfin.meanreversion.hvolatility
-
A construction of extreme and trade points based on H discretization, ignoring changes smaller than H.
- head() - Method in interface dev.nm.graph.Arc
- head() - Method in class dev.nm.graph.type.SimpleArc
- header() - Method in interface dev.nm.stat.regression.linear.panel.PanelData.Transformation
-
Gets the name of the column to apply the transformation to.
- HeatEquation1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
-
A one-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the following form.
- HeatEquation1D(double, double, double, UnivariateRealFunction, double, UnivariateRealFunction, double, UnivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
-
Constructs a heat equation problem.
- HeatEquation2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2
-
A two-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the following form.
- HeatEquation2D(double, double, double, double, BivariateRealFunction, TrivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
-
Constructs a two-dimensional heat equation problem.
- height() - Method in interface dev.nm.graph.RootedTree
-
Gets the maximum depth in this tree.
- height() - Method in class dev.nm.graph.type.SparseTree
- height() - Method in class dev.nm.graph.type.VertexTree
- HermitePolynomials - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
-
A Hermite polynomial is defined by the recurrence relation below.
- HermitePolynomials() - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermitePolynomials
- HermiteRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
- HermiteRule(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
-
Create a Hermite rule of the given order.
- Hessenberg - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
An upper Hessenberg matrix is a square matrix which has zero entries below the first sub-diagonal.
- Hessenberg() - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Construct a Hessenberg utility class with the default deflation criterion.
- Hessenberg(DeflationCriterion) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Construct a Hessenberg utility class.
- HessenbergDecomposition - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
Given a square matrix A, we find Q such that Q' * A * Q = H where H is a Hessenberg matrix.
- HessenbergDecomposition(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition
-
Runs the Hessenberg decomposition for a square matrix.
- HessenbergDeflationSearch - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
Given a Hessenberg matrix, this class searches the largest unreduced Hessenberg sub-matrix.
- HessenbergDeflationSearch(boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDeflationSearch
- HessenbergDeflationSearch(DeflationCriterion, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDeflationSearch
- Hessian - Class in dev.nm.analysis.differentiation.multivariate
-
The Hessian matrix is the square matrix of the second-order partial derivatives of a multivariate function.
- Hessian(RealScalarFunction, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Hessian
-
Construct the Hessian matrix for a multivariate function f at point x.
- Hessian() - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction
- HessianFunction - Class in dev.nm.analysis.differentiation.multivariate
-
The Hessian function, H(x), evaluates the Hessian of a real scalar function f at a point x.
- HessianFunction(RealScalarFunction) - Constructor for class dev.nm.analysis.differentiation.multivariate.HessianFunction
-
Construct the Hessian function of a real scalar function f.
- Heteroskedasticity - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
-
A heteroskedasticity test tests, for a linear regression model, whether the estimated variance of the residuals from a regression is dependent on the values of the independent variables (regressors).
- Heteroskedasticity(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
Construct a heteroskedasticity test.
- hHat() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the projection matrix, H-hat.
- HiddenMarkovModel - Class in dev.nm.stat.hmm
- HiddenMarkovModel(Vector, Matrix, RandomNumberGenerator[]) - Constructor for class dev.nm.stat.hmm.HiddenMarkovModel
- HiddenMarkovModel(HMMRNG) - Constructor for class dev.nm.stat.hmm.HiddenMarkovModel
- HilbertMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype
-
A Hilbert matrix, H, is a symmetric matrix with entries being the unit fractions H[i][j] = 1 / (i + j -1)
- HilbertMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix
-
Constructs a Hilbert matrix.
- HilbertSpace<H,F extends Field<F> & Comparable<F>> - Interface in dev.nm.algebra.structure
-
A Hilbert space is an inner product space, an abstract vector space in which distances and angles can be measured.
- HmmInnovation - Class in dev.nm.stat.hmm
-
An HMM innovation consists of a state and an observation in the state.
- HmmInnovation(int, double) - Constructor for class dev.nm.stat.hmm.HmmInnovation
-
Construct an HMM innovation.
- HMMRNG - Class in dev.nm.stat.hmm
-
In a (discrete) hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible.
- HMMRNG(Vector, Matrix, RandomNumberGenerator[]) - Constructor for class dev.nm.stat.hmm.HMMRNG
-
Constructs a hidden Markov model.
- HMMRNG(HMMRNG) - Constructor for class dev.nm.stat.hmm.HMMRNG
-
Copy constructor.
- holdingTime(double) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets a suggested holding time based on an OU process.
- holdingTimeByThreshold(double) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
-
Gets a suggested holding time based on an OU process.
- HomogeneousPathFollowingMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
-
This implementation solves a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.
- HomogeneousPathFollowingMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
-
Constructs a Homogeneous Self-Dual Path-Following minimizer to solve semi-definite programming problems.
- HomogeneousPathFollowingMinimizer(double, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
-
Constructs a Homogeneous Self-Dual Path-Following minimizer to solve semi-definite programming problems.
- HomogeneousPathFollowingMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
-
This is the solution to a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.
- HornerScheme - Class in dev.nm.analysis.function.polynomial
-
Horner scheme is an algorithm for the efficient evaluation of polynomials in monomial form.
- HornerScheme(Polynomial, double) - Constructor for class dev.nm.analysis.function.polynomial.HornerScheme
-
Evaluate a polynomial at x.
- Householder4SubVector - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
-
Faster implementation of Householder reflection for sub-vectors at a given index.
- Householder4SubVector(int, int, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
- Householder4SubVector(int, HouseholderContext) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
- Householder4SubVector(int, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
- Householder4SubVector(int, Vector, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
- Householder4ZeroGenerator - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
-
Faster implementation of Householder reflection for zero generator vector.
- Householder4ZeroGenerator(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4ZeroGenerator
- HouseholderContext - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
-
This is the context information about a Householder transformation.
- HouseholderContext(Vector, double, Vector, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
-
Constructs a Householder context information.
- HouseholderInPlace - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
-
Maintains the matrix to be transformed by a sequence of Householder reflections.
- HouseholderInPlace(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Creates an instance that transforms an identity matrix with the given dimension.
- HouseholderInPlace(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Creates an instance that transforms the given matrix A.
- HouseholderInPlace(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Creates an instance that transforms the given matrix A.
- HouseholderInPlace.Householder - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
- HouseholderQR - Class in dev.nm.algebra.linear.matrix.doubles.factorization.qr
-
Successive Householder reflections gradually transform a matrix A to the upper triangular form.
- HouseholderQR(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
Runs the Householder reflection process to orthogonalize a matrix.
- HouseholderQR(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
Runs the Householder reflection process to orthogonalize a matrix.
- HouseholderReflection - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
-
A Householder transformation in the 3-dimensional space is the reflection of a vector in the plane.
- HouseholderReflection(HouseholderContext) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
- HouseholderReflection(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Construct a Householder matrix from the vector that defines the hyperplane orthogonal to the vector.
- HouseholderReflection(Vector, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
- Hp - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
-
This is the symmetrization operator as defined in equation (6) in the reference.
- Hp() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.Hp
-
Constructs the symmetrization operator using an identity matrix.
- Hp(Matrix) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.Hp
-
Constructs a symmetrization operator.
- HuangImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.HuangMinimizer.HuangImpl
- HuangMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
-
Huang's updating formula is a family of formulas which encompasses the rank-one, DFP, BFGS as well as some other formulas.
- HuangMinimizer(double, double, double, double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.HuangMinimizer
-
Construct a multivariate minimizer using Huang's method.
- HuangMinimizer.HuangImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
-
an implementation of Huang's formula.
- HybridMCMC - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
-
This class implements a hybrid MCMC algorithm.
- HybridMCMC(RealScalarFunction, RealVectorFunction, Vector, double, int, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.HybridMCMC
-
Constructs a new instance with the given parameters.
- HybridMCMCProposalFunction - Class in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
- HybridMCMCProposalFunction(Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.HybridMCMCProposalFunction
-
Constructs a hybrid MC proposal function.
- HyperEdge<V> - Interface in dev.nm.graph
-
A hyper-edge connects a set of vertices of any size.
- HypersphereRVG - Class in dev.nm.stat.random.rng.multivariate
-
Generates uniformly distributed points on the surface of a hypersphere.
- HypersphereRVG(int) - Constructor for class dev.nm.stat.random.rng.multivariate.HypersphereRVG
-
Constructs a hypersphere RVG to generate random uniform points.
- HypersphereRVG(int, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.HypersphereRVG
-
Constructs a hypersphere RVG to generate random uniform points.
- HypothesisTest - Class in dev.nm.stat.test
-
A statistical hypothesis test is a method of making decisions using experimental data.
- HypothesisTest(double[]...) - Constructor for class dev.nm.stat.test.HypothesisTest
-
Construct an instance of
HypothesisTest
from the samples.
I
- i - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
the row index
- i() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets the value of i.
- 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 eitheryes
orno
depending on whether the element of test istrue
orfalse
. - 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() - 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(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.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) - 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(Matrix, int) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA
-
Constructs an implicit-model that will have K implicit factors.
- 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
-
Uses importance sample to do Monte Carlo integration.
- IN_EXACT_LINE_SEARCH - dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method
-
The line search is done using Fletcher's inexact line search method,
FletcherLineSearch
. - inactiveSize() - Method in class dev.nm.misc.algorithm.ActiveSet
-
Get the number of inactive indices.
- incomingArcs(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
- 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
- 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 - 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
- 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.
- Infantino2010PCA - Class in tech.nmfin.signal.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.signal.infantino2010.Infantino2010PCA
- Infantino2010PCA.Signal - Class in tech.nmfin.signal.infantino2010
- Infantino2010Regime - Class in tech.nmfin.signal.infantino2010
-
Detects the current regime (mean reversion or momentum) by cross-sectional volatility.
- Infantino2010Regime(int) - Constructor for class tech.nmfin.signal.infantino2010.Infantino2010Regime
- Infantino2010Regime.Regime - Enum in tech.nmfin.signal.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.root.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.root.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(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- innerProduct(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- innerProduct(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- 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(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the inner or dot product of two vectors.
- innerProduct(H) - Method in interface dev.nm.algebra.structure.HilbertSpace
-
<⋅,⋅> : H × H → F
- 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.
- 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. - instantPanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
- intArray2doubleArray(int...) - Static method in class dev.nm.number.DoubleUtils
-
Convert an
int
array to adouble
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) - 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, long) - 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
-
Constructs 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(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.
- Intervals(T, T) - Constructor for class dev.nm.interval.Intervals
-
Construct a set that contains only one interval [begin, end].
- 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
-
Deprecated.Invalid operation.
- 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() - 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(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 - 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 - Static variable in class dev.nm.stat.random.variancereduction.AntitheticVariates
- INVERSE_FINE_STRUCTURE_ALPHA1 - Static variable in class dev.nm.misc.PhysicalConstants
-
The inverse fine-structure constant \(\alpha^{-1}\) (dimensionless).
- INVERSE_OF_EMPIRICAL_CDF - dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
the inverse of empirical distribution function
- INVERSE_OF_EMPIRICAL_CDF_WITH_AVERAGING_AT_DISCONTINUITIES - dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
the inverse of empirical distribution function with averaging at discontinuities
- 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) - 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, 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.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) - Constructor for class dev.nm.stat.random.rng.univariate.InverseTransformSampling
-
Construct a random number generator to sample from a distribution.
- InverseTransformSampling(ProbabilityDistribution, RandomLongGenerator) - 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() - Constructor for class dev.nm.stat.random.rng.univariate.exp.InverseTransformSamplingExpRNG
-
Constructs a random number generator to sample from the standard exponential distribution using the inverse transform sampling method.
- InverseTransformSamplingExpRNG(double) - Constructor for class dev.nm.stat.random.rng.univariate.exp.InverseTransformSamplingExpRNG
-
Constructs a random number generator to sample from the exponential distribution using the inverse transform sampling method.
- InverseTransformSamplingGammaRNG - Class in dev.nm.stat.random.rng.univariate.gamma
-
Deprecated.There exist much more efficient algorithms.
- 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.
- 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.
- 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) - 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.
- 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, 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.
- 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[]) - Constructor for class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
-
Construct a constrained optimization problem with integral constraints.
- IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[], double) - 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
andY
satisfies a certain Allen's interval relation. - isAcyclic() - Method in class dev.nm.graph.type.SparseDAGraph
-
Runs validity check to ensure that this DA graph is indeed acyclic.
- isAcyclic(UnDiGraph<V, UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils
-
Check if an undirected graph is 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.root.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 - Variable in class dev.nm.graph.algorithm.traversal.DFS.Node
- isCyclic() - Method in class dev.nm.graph.algorithm.traversal.DFS
-
Checks if the graph is cyclic.
- 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) - Method in class dev.nm.analysis.function.SubFunction
-
Checks whether a particular index corresponds a fixed variable/value.
- isFixedIndex(int, Map<Integer, Double>) - Static 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 aNaN
. - isInKernel(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Deprecated.Not supported yet.
- 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
-
Deprecated.Not supported yet.
- isMinFound() - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the convergence criterion
- isMinFound() - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
-
the convergence criterion
- isMinFound() - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer.Solution
-
This algorithm stops only after a pre-specified number of iterations.
- isMinFound() - Method in class dev.nm.root.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 anNaN
. - 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∞
orNaN
. - 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(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
- 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.
- 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() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearEqualityConstraints
-
Check if we can reduce the number of linear equalities.
- 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.
- 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) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
-
Checks if the constraints are satisfied.
- isSatisfied(Constraints, Vector, double) - 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
-
Deprecated.Not supported yet.
- 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(LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Checks 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(double, double) - Static method in class dev.nm.number.DoubleUtils
-
Check if
d
is zero. - 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.
- iter - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the current iteration count
- iter - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
- 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_1a
- iterate(PrimalDualSolution, Vector, Vector, double) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.SDPT3v4_1b
- 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
-
This
IterationMonitor
stores all states generated during iterations. - 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(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer
-
Construct a multivariate maximizer to maximize an objective function.
- IterativeC2Maximizer(T) - 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.misc.datastructure.time.LocalDateInterval
- iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
- iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
- iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
- 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.
J
- j - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
-
the column index
- j() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets the value of j.
- Jacobian - Class in dev.nm.analysis.differentiation.multivariate
-
The Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function.
- Jacobian(RealScalarFunction[], Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Jacobian
-
Construct the Jacobian matrix for a multivariate function f at point x.
- Jacobian(RealVectorFunction, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Jacobian
-
Construct the Jacobian matrix for a multivariate function f at point x.
- Jacobian(List<RealScalarFunction>, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Jacobian
-
Construct the Jacobian matrix for a multivariate function f at point x.
- JacobianFunction - Class in dev.nm.analysis.differentiation.multivariate
-
The Jacobian function, J(x), evaluates the Jacobian of a real vector-valued function f at a point x.
- JacobianFunction(RealVectorFunction) - Constructor for class dev.nm.analysis.differentiation.multivariate.JacobianFunction
-
Construct the Jacobian function of a real scalar function f.
- JacobiPreconditioner - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
-
The Jacobi (or diagonal) preconditioner is one of the simplest forms of preconditioning, such that the preconditioner is the diagonal of the coefficient matrix, i.e., P = diag(A).
- JacobiPreconditioner(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
-
Construct a Jacobi preconditioner.
- JacobiSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
-
The Jacobi method solves sequentially n equations in a linear system Ax = b in isolation in each iteration.
- JacobiSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
-
Construct a Jacobi solver.
- JarqueBera - Class in dev.nm.stat.test.distribution.normality
-
The Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the sample kurtosis and skewness.
- JarqueBera(double[]) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBera
-
Perform the Jarque-Bera test to test for the departure from normality, using the asymptotic chi-square distribution.
- JarqueBera(double[], boolean) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBera
-
Perform the Jarque-Bera test to test for the departure from normality.
- JarqueBeraDistribution - Class in dev.nm.stat.test.distribution.normality
-
Jarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality.
- JarqueBeraDistribution(int, int) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBeraDistribution
-
Construct a Jarque-Bera distribution using Monte Carlo simulation.
- JarqueBeraDistribution(int, int, StandardNormalRNG) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBeraDistribution
-
Construct a Jarque-Bera distribution using Monte Carlo simulation.
- JenkinsTraubReal - Class in dev.nm.analysis.function.polynomial.root.jenkinstraub
-
The Jenkins-Traub algorithm is a fast globally convergent iterative method for solving for polynomial roots.
- JenkinsTraubReal() - Constructor for class dev.nm.analysis.function.polynomial.root.jenkinstraub.JenkinsTraubReal
- JohansenAsymptoticDistribution - Class in dev.nm.stat.cointegration
-
Johansen provides the asymptotic distributions of the two hypothesis testings (Eigen and Trace tests), each for 5 different trend types.
- JohansenAsymptoticDistribution(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int) - Constructor for class dev.nm.stat.cointegration.JohansenAsymptoticDistribution
-
Construct the asymptotic distribution of a Johansen test.
- JohansenAsymptoticDistribution(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int, int, int) - Constructor for class dev.nm.stat.cointegration.JohansenAsymptoticDistribution
-
Construct the asymptotic distribution of a Johansen test.
- JohansenAsymptoticDistribution(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int, int, int, long) - Constructor for class dev.nm.stat.cointegration.JohansenAsymptoticDistribution
-
Construct the asymptotic distribution of a Johansen test.
- JohansenAsymptoticDistribution.F - Interface in dev.nm.stat.cointegration
-
This is a filtration function.
- JohansenAsymptoticDistribution.Test - Enum in dev.nm.stat.cointegration
-
the available types of Johansen cointegration tests
- JohansenAsymptoticDistribution.TrendType - Enum in dev.nm.stat.cointegration
-
the available types of trends
- JohansenTest - Class in dev.nm.stat.cointegration
-
The maximum number of cointegrating relations among a multivariate time series is the rank of the Π matrix.
- JohansenTest(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int) - Constructor for class dev.nm.stat.cointegration.JohansenTest
-
Construct an instance of
JohansenTest
. - JohansenTest(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int, int, int) - Constructor for class dev.nm.stat.cointegration.JohansenTest
-
Construct an instance of
JohansenTest
. - JordanExchange - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
-
Jordan Exchange swaps the r-th entering variable (row) with the s-th leaving variable (column) in a matrix A.
- JordanExchange() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange
K
- k - Variable in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution.Lambda
-
the shape parameter
- k - Variable in class tech.nmfin.signal.infantino2010.Infantino2010PCA
- k(Vector, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
-
Evaluates the standard kinetic energy, k = p^2 / 2m.
- K - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
number of distinct population eigenvalues bigger than 0; the length of t
- K() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
-
Gets the H property.
- K(ARMAModel) - Static method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
- Kagi<T extends Comparable<? super T>> - Class in tech.nmfin.meanreversion.hvolatility
-
KAGI construction of a random process.
- Kagi(double[]) - Constructor for class tech.nmfin.meanreversion.hvolatility.Kagi
- Kagi(UnivariateTimeSeries<T, ? extends UnivariateTimeSeries.Entry<T>>) - Constructor for class tech.nmfin.meanreversion.hvolatility.Kagi
- Kagi.Trend - Enum in tech.nmfin.meanreversion.hvolatility
- KagiModel - Class in tech.nmfin.meanreversion.hvolatility
-
Maintains the states of a KAGI model.
- KagiModel(double) - Constructor for class tech.nmfin.meanreversion.hvolatility.KagiModel
- KendallRankCorrelation - Class in dev.nm.stat.descriptive.correlation
-
The Kendall rank correlation coefficient, commonly referred to as Kendall's tau (τ) coefficient, is a statistic used to measure the association between two measured quantities.
- KendallRankCorrelation(double[], double[]) - Constructor for class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
-
Construct a Kendall rank calculator initialized with two samples.
- Kernel - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
-
The kernel or null space (also nullspace) of a matrix A is the set of all vectors x for which Ax = 0.
- Kernel(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Construct the kernel of a matrix.
- Kernel(Matrix, Kernel.Method, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Construct the kernel of a matrix.
- Kernel.Method - Enum in dev.nm.algebra.linear.matrix.doubles.linearsystem
-
These are the available methods to compute kernel basis.
- keySet() - Method in class dev.nm.combinatorics.Counter
-
Get the set of numbers the counter has seen.
- KnightSatchellTran1995 - Class in tech.nmfin.trend.kst1995
-
Implements the Knight-Satchell-Tran model of financial asset returns.
- KnightSatchellTran1995(double, double, double, double, double, double, double) - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
Constructs an instance of the Knight-Satchell-Tran model of returns.
- KnightSatchellTran1995(double, double, double, double, double, double, double, RandomStandardNormalGenerator, RandomLongGenerator) - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
Constructs an instance of the Knight-Satchell-Tran model of returns.
- KnightSatchellTran1995(KnightSatchellTran1995) - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995
-
Copy constructor.
- KnightSatchellTran1995MLE - Class in tech.nmfin.trend.kst1995
-
Fits a KST model from returns.
- KnightSatchellTran1995MLE() - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995MLE
- Knuth1969 - Class in dev.nm.stat.random.rng.univariate.poisson
-
This is a random number generator that generates random deviates according to the Poisson distribution.
- Knuth1969(double) - Constructor for class dev.nm.stat.random.rng.univariate.poisson.Knuth1969
-
Constructs a random number generator to sample from the Poisson distribution.
- Knuth1969(double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.poisson.Knuth1969
-
Constructs a random number generator to sample from the Poisson distribution.
- KolmogorovDistribution - Class in dev.nm.stat.test.distribution.kolmogorov
-
The Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic.
- KolmogorovDistribution(int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Construct a Kolmogorov distribution for a sample size n.
- KolmogorovDistribution(int, int, boolean) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Construct a Kolmogorov distribution for a sample size n.
- KolmogorovOneSidedDistribution - Class in dev.nm.stat.test.distribution.kolmogorov
-
Compute the probability that F(x) is dominated by the upper confidence contour, for all x: Pn(ε) = Pr{F(x) < min{Fn(x) + ε, 1}}
- KolmogorovOneSidedDistribution(int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Construct a one-sided Kolmogorov distribution.
- KolmogorovOneSidedDistribution(int, int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Construct a one-sided Kolmogorov distribution.
- KolmogorovSmirnov - Class in dev.nm.stat.test.distribution.kolmogorov
-
The Kolmogorov-Smirnov test (KS test) compares a sample with a reference probability distribution (one-sample KS test), or to compare two samples (two-sample KS test).
- KolmogorovSmirnov.Side - Enum in dev.nm.stat.test.distribution.kolmogorov
-
the available types of the Kolmogorov-Smirnov statistic
- KolmogorovSmirnov.Type - Enum in dev.nm.stat.test.distribution.kolmogorov
-
the available types of the Kolmogorov-Smirnov tests
- KolmogorovSmirnov1Sample - Class in dev.nm.stat.test.distribution.kolmogorov
-
The one-sample Kolmogorov-Smirnov test (one-sample KS test) compares a sample with a reference probability distribution.
- KolmogorovSmirnov1Sample(double[], ProbabilityDistribution, KolmogorovSmirnov.Side) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov1Sample
-
Construct a one-sample Kolmogorov-Smirnov test.
- KolmogorovSmirnov2Samples - Class in dev.nm.stat.test.distribution.kolmogorov
-
The two-sample Kolmogorov-Smirnov test (two-sample KS test) tests for the equality of the distributions of two samples.
- KolmogorovSmirnov2Samples(double[], double[], KolmogorovSmirnov.Side) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov2Samples
-
Construct a two-sample Kolmogorov-Smirnov test.
- KolmogorovTwoSamplesDistribution - Class in dev.nm.stat.test.distribution.kolmogorov
-
Compute the p-values for the generalized (conditionally distribution-free) Smirnov homogeneity test.
- KolmogorovTwoSamplesDistribution(double[], double[], KolmogorovTwoSamplesDistribution.Side) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Construct a two-sample Kolmogorov distribution.
- KolmogorovTwoSamplesDistribution(int, int, double[], KolmogorovTwoSamplesDistribution.Side, int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Construct a two-sample Kolmogorov distribution.
- KolmogorovTwoSamplesDistribution(int, int, KolmogorovTwoSamplesDistribution.Side, double[]) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Construct a two-sample Kolmogorov distribution.
- KolmogorovTwoSamplesDistribution(int, int, KolmogorovTwoSamplesDistribution.Side, int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Construct a two-sample Kolmogorov distribution, assuming that there is no tie in the samples.
- KolmogorovTwoSamplesDistribution.Side - Enum in dev.nm.stat.test.distribution.kolmogorov
-
the available types of Kolmogorov-Smirnov two-sample test
- KroneckerProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
Given an m-by-n matrix A and a p-by-q matrix B, their Kronecker product C, also called their matrix direct product, is an (mp)-by-(nq) matrix with entries defined by cst = aij bkl where
- KroneckerProduct(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.KroneckerProduct
-
Construct the Kronecker product of two matrices.
- KruskalWallis - Class in dev.nm.stat.test.rank
-
The Kruskal-Wallis test is a non-parametric method for testing the equality of population medians among groups.
- KruskalWallis(double[]...) - Constructor for class dev.nm.stat.test.rank.KruskalWallis
-
Construct a Kruskal-Wallis test for the equality of medians of groups.
- KunduGupta2007 - Class in dev.nm.stat.random.rng.univariate.gamma
-
Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized exponential distribution, when the shape parameter lies between 0 and 1.
- KunduGupta2007(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007
-
Constructs a random number generator to sample from the gamma distribution.
- KunduGupta2007(double, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007
-
Constructs a random number generator to sample from the gamma distribution.
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.FDistribution
-
Gets the excess kurtosis of this distribution.
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
- kurtosis() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
Gets the excess kurtosis of this distribution.
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.TDistribution
-
Gets the excess kurtosis of this distribution.
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
- kurtosis() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
- kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
- kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
- kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
- kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
- kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
- kurtosis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- kurtosis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
-
Deprecated.
- kurtosis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- kurtosis() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- kurtosis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Deprecated.
- kurtosis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Deprecated.
- Kurtosis - Class in dev.nm.stat.descriptive.moment
-
Kurtosis measures the "peakedness" of the probability distribution of a real-valued random variable.
- Kurtosis() - Constructor for class dev.nm.stat.descriptive.moment.Kurtosis
-
Construct an empty
Kurtosis
calculator. - Kurtosis(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Kurtosis
-
Construct a
Kurtosis
calculator, initialized with a sample. - Kurtosis(Kurtosis) - Constructor for class dev.nm.stat.descriptive.moment.Kurtosis
-
Copy constructor.
L
- 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) - 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.univariate.realtime.inttime.SimpleTimeSeries
-
Constructs an instance of
SimpleTimeSeries
by lagging the time series. - 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, 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(double) - Constructor for class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm
- Lai2010OptimizationAlgorithm(MVOptimizer, ReturnsMoments.Estimator, ReturnsResamplerFactory, int, 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() - 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
- Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
-
Stores the Beta 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(int, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
-
Stores the Binomial distribution parameters.
- lambda_Jacobian - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
lambda Jacobian
- lambda1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
- lambda1() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
Gets the transition intensity from bull market to bear market.
- lambda2 - 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.
- 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() - Constructor for class dev.nm.analysis.function.special.gamma.Lanczos
-
Construct a Lanczos approximation instance using default parameters.
- Lanczos(double, int, int) - Constructor for class dev.nm.analysis.function.special.gamma.Lanczos
-
Construct a Lanczos approximation instance.
- LANCZOS - dev.nm.analysis.function.special.gamma.LogGamma.Method
-
Lanczos approximation.
- LANCZOS_QUICK - dev.nm.analysis.function.special.gamma.LogGamma.Method
-
Quick Lanczos approximation, where all computations are done in
double
precision. - 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) - 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(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.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) - 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(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, 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, boolean, boolean) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Constructs a Least Angel Regression (LARS) problem.
- LARSProblem(LARSProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Copy constructor.
- last() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
- LAST - dev.nm.stat.descriptive.rank.Rank.TiesMethod
- 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) - 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, 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.
- 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) - Constructor for class dev.nm.analysis.curvefit.LeastSquares
-
Construct a new instance of this algorithm, which uses uniform weighting for the observations.
- LeastSquares(int, LeastSquares.Weighting) - Constructor for class dev.nm.analysis.curvefit.LeastSquares
-
Construct a new instance of the algorithm 4.5.1 from Schilling & Harris, which will use a weighted sum of orthogonal polynomials up to order n (the number of points).
- 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.
- LECUYER - dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method
-
Lecuyer
- 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(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. - leftShift(T[]) - Static method in class dev.nm.misc.ArrayUtils
-
Get a left shifted 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() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
-
Construct a Lehmer (pure) linear congruential generator.
- 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 - dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method
-
Lehmer
- 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.
- LESS - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side
-
compute Dn-; check whether the cdf of a sample lies below the null hypothesis
- LESS - dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
-
two-sample; one-sided
- 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.
- LINEAR_INTERPOLATION_OF_EMPIRICAL_CDF - dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
the linear interpolation of the empirical cdf
- 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.
- linearEqualities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
- 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
- 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.
- 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() - 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
- 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.
- linearInequalities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
- 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() - 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
- 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.
- 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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
Construct the linear representation of an ARMA model up to the default number of lags
LinearRepresentation.DEFAULT_NUMBER_OF_LAGS
. - LinearRepresentation(ARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
-
Construct the linear representation of an ARMA model.
- 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 - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
- 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 - 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.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) - 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(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, 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(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
- LocalDateInterval - Class in dev.nm.misc.datastructure.time
-
Represents an interval between two
LocalDate
. - localDatePanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
- LocalDateTimeInterval - Class in dev.nm.misc.datastructure.time
-
Represents an interval between two
LocalDateTime
. - localDateTimePanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
- LocalDateTimeUtils - Class in dev.nm.misc.datastructure.time
-
Utility functions to manipulate LocalDateTime.
- LocalDateTimeUtils() - Constructor for class dev.nm.misc.datastructure.time.LocalDateTimeUtils
- 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(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the logs of values.
- log(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the log of a vector, element-by-element.
- log(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
Natural logarithm of a complex number.
- 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 - tech.nmfin.returns.ReturnsCalculators
-
The return is defined as the natural logarithm of the ratio v2/v1.
- 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() - Constructor for class dev.nm.analysis.function.special.gamma.LogGamma
-
Construct an instance of log-Gamma.
- LogGamma(LogGamma.Method, Lanczos) - 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(LMProblem) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticRegression
-
Constructs a Logistic instance.
- LogisticRegression(LogisticProblem) - 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 - Variable in class dev.nm.stat.hmm.mixture.MixtureHMMEM.TrainedModel
-
the log-likelihood
- 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() - 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[]) - 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[], 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.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(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.LogNormalRNG
-
Construct a random number generator to sample 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.
- 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
-
Deprecated.Invalid operation.
- 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 - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
- 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 - dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
-
a lower bi-diagonal matrix, where there are only non-zero entries on the main and sub diagonal
- LOWER - dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
-
diverge near x = a
- 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(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(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
-
Constructs a lower triangular matrix of dimension dim * dim.
- 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, 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.
- 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.
- 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() - 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(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.
- 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) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
-
Run the LU decomposition on a square matrix.
- LU(Matrix, double) - 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
- 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.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.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
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.
- M() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
-
Gets the number of interior time-axis grid points in the solution.
- 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
- 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) - 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.
- 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.
- 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[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model with unit variance and zero-mean.
- 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, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model with unit variance.
- MAModel(double, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
-
Construct a univariate MA model.
- 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) - 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.
- 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.
- 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(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.
- MARMAModel(UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MARMAModel
-
Create an instance with a given 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
-
Create an instance with the given
MARMAModel
. - 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() - Constructor for class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964
-
Construct a random number generator to sample from the standard Normal distribution.
- 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.
- 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() - Constructor for class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
-
Construct a random number generator to sample from the standard 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(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.
- 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 ofSVEC
. - 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(int) - Constructor for class dev.nm.misc.datastructure.MathTable
-
Constructs an empty table.
- MathTable(String...) - Constructor for class dev.nm.misc.datastructure.MathTable
-
Constructs an empty table by headers.
- 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
- 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(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(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the maximal entry in a matrix.
- max(LocalDateTime, LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Returns the later of the two given time instances, or the first instance if two instances are equal.
- 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 - dev.nm.stat.descriptive.rank.Rank.TiesMethod
- 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_ITERATIONS_EXCEEDED - dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
-
Thrown if the iterative algorithm fails to converge to a solution within a specified tolerance after the specified maximum number of iterations.
- 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.root.univariate.bracketsearch.BracketSearchMinimizer
-
the maximum number of iterations
- 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
- 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
-
Deprecated.the McCormick algorithm does not seem to work well; need further investigation; don't use it. TODO. Use
BFGSMinimizer
instead. - McCormickMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.McCormickMinimizer
-
Deprecated.the McCormick algorithm does not seem to work well; need further investigation; don't use it. TODO. Use
BFGSMinimizer
instead. - 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() - 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 class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
- mean() - Method in interface tech.nmfin.meanreversion.volarb.MeanPriceEstimator
- 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_REVERSION - tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
- 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
- MeanEstimatorMaxLevelShift - Class in tech.nmfin.meanreversion.volarb
- MeanEstimatorMaxLevelShift(int, double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
- MeanEstimatorMaxLevelShift(int, double, double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
- MeanPriceEstimator - Interface in tech.nmfin.meanreversion.volarb
-
Defines how to estimate the mean price.
- MEANS - dev.nm.stat.test.variance.Levene.Type
-
compute the absolute deviations from the group means
- 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
-
Deprecated.Not supported yet.
- 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.
- MEDIAN - dev.nm.stat.test.variance.Levene.Type
-
compute the absolute deviations from the group medians
- MEET - dev.nm.interval.IntervalRelation
-
X meets Y.
- MEET_INVERSE - dev.nm.interval.IntervalRelation
-
Y meets X.
- mergeIntervals(RealInterval[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenBoundUtils
- MERSENNE_TWISTER - dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method
-
Mersenne Twister (recommended)
- 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
-
Immutable parameters for creating a
MersenneTwister
RNG. - 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, 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.
- 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.
- 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.
- MIDWAY_THROUGH_STEPS_OF_EMPIRICAL_CDF - dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
a piecewise linear function where the knots are the values midway through the steps of the empirical cdf
- 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() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution
- min(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the minimum of the values.
- min(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the minimal entry in a matrix.
- min(LocalDateTime, LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Returns the earlier of the two given time instances, or the first instance if two instances are equal.
- 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 - dev.nm.stat.descriptive.rank.Rank.TiesMethod
- 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(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
Construct a MINRES solver.
- MinimalResidualSolver(PreconditionerFactory, 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 class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
- minimizer() - Method in class dev.nm.root.univariate.GridSearchMinimizer.Solution
- minimizer() - Method in interface dev.nm.solver.MinimizationSolution
-
Get the minimizer (solution) to the minimization problem.
- 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.interiorpoint.PrimalDualInteriorPointMinimizer1.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.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.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<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.
- 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 class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
- minimum() - Method in class dev.nm.root.univariate.GridSearchMinimizer.Solution
- 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.interiorpoint.PrimalDualInteriorPointMinimizer1.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.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.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
- 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
. - MINITAB_SPSS - dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
the definition in Minitab and SPSS
- 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(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- 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(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Subtract a constant from all entries in this vector.
- 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(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(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(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(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(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(DenseData) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
-
Subtract the elements in
this
bythat
, element-by-element. - minus(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- 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(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- minus(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- minus(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- minus(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
- minus(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
\(this - that\)
- 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, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
A vector subtracts another vector, element-by-element.
- minus(Polynomial) - Method in class dev.nm.analysis.function.polynomial.Polynomial
- minus(Complex) - Method in class dev.nm.number.complex.Complex
- minus(Real) - Method in class dev.nm.number.Real
- minus(G) - Method in interface dev.nm.algebra.structure.AbelianGroup
-
- : G × G → G
- 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) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
- ModelParam(double, 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() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
- ModelResamplerFactory(RandomLongGenerator) - 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(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
-
Deprecated.Not supported yet.
- 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
-
Deprecated.Not supported yet.
- 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(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.
- 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(Vector) - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
- moment1() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
- moment2() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
- moments() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
- 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.
- MomentsEstimatorLedoitWolf - Class in tech.nmfin.returns.moments
-
Estimates the first two moments of returns using Ledoit-Wolf 2004.
- MomentsEstimatorLedoitWolf() - Constructor for class tech.nmfin.returns.moments.MomentsEstimatorLedoitWolf
- MOMENTUM - tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
- 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[]) - Constructor for class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage
-
Construct a moving average filter using a symmetric window.
- MovingAverage(double[], MovingAverage.Side) - Constructor for class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage
-
Construct a moving average filter.
- 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 - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
-
For a symmetric matrix.
- MR3 - dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD.Method
-
Multiple Relatively Robust Representation (MRRR), for higher speed.
- 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) - Constructor for class tech.nmfin.meanreversion.volarb.MRModelRanged
- MRModelRanged(double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MRModelRanged
- mu - Variable in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
-
the mean
- mu - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
- 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(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.
- 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(MultiDimensionalGrid.Discretization...) - 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.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[]) - Constructor for class dev.nm.stat.random.rng.multivariate.MultinomialRVG
-
Constructs a multinomial random vector generator.
- MultinomialRVG(int, double[], RandomLongGenerator) - 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) - 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, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
Construct a multiplier penalty function from a collection of constraints.
- 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(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(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
- 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(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(Matrix) - 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(Matrix) - 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(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(Matrix) - 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(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
- 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(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
- multiply(MatrixAccess, MatrixAccess) - 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, 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.AutoParallelMatrixMathOperation
- multiply(MatrixAccess, Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
-
A * v
- multiply(MatrixAccess, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
- multiply(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- 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(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- 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(Vector) - 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.diagonal.DiagonalMatrix
- multiply(Vector) - 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.SymmetricMatrix
- multiply(Vector) - 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.GivensMatrix
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Left multiplication by P.
- multiply(Vector) - 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.DOKSparseMatrix
- 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(Vector) - 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(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
- multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
- multiply(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- 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
bythat
, entry-by-entry. - multiply(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Multiplies two vectors, element-by-element.
- 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(Real) - Method in class dev.nm.number.Real
- multiply(G) - Method in interface dev.nm.algebra.structure.Monoid
-
× : G × G → G
- 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
-
Implementation of
MultivariateGrid
, backed by the givenMultiDimensionalCollection
instance. - 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, 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, ...].
- 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, 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.
- 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) - 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, MultivariateIntTimeTimeSeries, NormalRVG) - 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[], 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.
- MultivariateGenericTimeTimeSeries(T[], Vector[]) - 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(int) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
Constructs an instance of the standard Normal distribution.
- MultivariateNormalDistribution(Vector, Matrix) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
-
Constructs an instance with the given mean and covariance matrix.
- MultivariateObservationEquation - Class in dev.nm.stat.dlm.multivariate
-
This is the observation equation in a controlled dynamic linear model.
- MultivariateObservationEquation(Matrix, Matrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs a time-invariant an observation equation.
- MultivariateObservationEquation(Matrix, Matrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs a time-invariant an observation equation.
- MultivariateObservationEquation(R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs an observation equation.
- MultivariateObservationEquation(R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs an observation equation.
- MultivariateObservationEquation(MultivariateObservationEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Copy constructor.
- MultivariateObservationEquation(ObservationEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Constructs a multivariate observation equation from a univariate observation equation.
- 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, int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
-
Construct a random realization generator from a multivariate SDE.
- MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, TimeGrid, Vector) - 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(double[]...) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
-
Construct an instance of
MultivariateSimpleTimeSeries
. - MultivariateSimpleTimeSeries(Matrix) - 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(Matrix, Matrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a time-invariant 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(R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a state equation without control variables.
- MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a state equation.
- MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a state equation.
- MultivariateStateEquation(MultivariateStateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Copy constructor.
- MultivariateStateEquation(StateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Constructs a multivariate state equation from a univariate state equation.
- 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, int) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
Constructs an instance of the standard t distribution, mean 0, variance 1.
- MultivariateTDistribution(int, Vector, Matrix) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
-
Constructs an instance with the given mean and scale matrix.
- 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
- n - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
- n - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
sample size
- 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.PDESolutionTimeSpaceGrid2D
-
Get the number of interior x-axis grid points in the 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() - Method in class dev.nm.stat.cointegration.CointegrationMLE
-
Get the number of rows of the multivariate time series used in regression.
- n(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets the number of columns of Ai.
- n(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
Gets the number of columns of \({A^q}_i\).
- N - Variable in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
-
the number of observations
- N - Variable in class tech.nmfin.signal.infantino2010.Infantino2010PCA
- 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.PDESolutionTimeSpaceGrid1D
-
Gets the number of interior space-axis grid points in the solution.
- 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.WeightedMedian
- 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() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
-
Gets the number of H inversions.
- n_l() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- n_q() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
Gets the total number of \({A^q}_i\) matrices.
- n_u() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
- NA - tech.nmfin.meanreversion.hvolatility.Kagi.Trend
- 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
-
Constructs 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.
- NEAREST_EVEN_ORDER_STATISTICS - dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
the nearest even order statistic as in SAS
- 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() - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
Construct an empty Neville table.
- 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)}.
- 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() - Method in interface dev.nm.solver.multivariate.constrained.SubProblemMinimizer.ConstrainedMinimizerFactory
-
Constructs a new instance of ConstrainedMinimizer to solve a real valued minimization problem.
- newInstance() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.MinimizerFactory
-
Construct a new instance of
Minimizer
for a mutation operation. - 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
-
Construct a new instance of
Preconditioner
for a coefficient matrix. - 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
-
Creates a new SOCPGeneralConstraints so we can add SOCPGeneralConstraint to it.
- newSOCPLinearEqualities(SOCPPortfolioConstraint.Variable...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
- newSOCPLinearInequalities(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, 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. - 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. - 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() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassSampler
-
Gets the next random element from the range of the probability distribution.
- 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() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Gets the next forecast.
- next(double) - Method in class dev.nm.stat.dlm.univariate.DLMSim
-
Get the next innovation.
- next(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast
-
Gets the next n-step forecasts.
- next(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
-
Gets the next n-step forecasts.
- 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(Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim
-
Gets the next innovation.
- 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() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability ep.
- nextLogTrial(RandomNumberGenerator, double) - Static 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(RandomVectorGenerator, int) - Static method in class dev.nm.stat.random.rng.RNGUtils
-
Generates
n
random vectors from a given random vector generator. - nextN(RandomNumberGenerator, int) - Static method in class dev.nm.stat.random.rng.RNGUtils
-
Generates
n
random numbers from a given random number 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() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
-
Performs a Bernoulli trial that succeeds with probability p.
- nextTrial(RandomNumberGenerator, double) - Static 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
-
Gets 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(LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Gets 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
- NIL - tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
- 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) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
- NMSAAM(double, double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
- NMSAAM(double, double, int) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
- NMSAAM(double, double, int, int, double) - 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. - NO_CONSTANT - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
-
This is trend type I: no constant, no linear trend:
- NO_CONSTANT - dev.nm.stat.test.timeseries.adf.TrendType
-
test for a unit root without drift or time trend
- 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
-
Weights are unconstrained and NoConstraints.constraints() returns
null
. - 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
- NON_BASIC - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
the non-basic variables, i.e., original variables in the cost function
- 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() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- norm() - 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() - Method in interface dev.nm.algebra.structure.BanachSpace
-
|⋅| : B → F
- 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(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
- norm(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Gets the \(L^p\)-norm \(\|v\|_p\) of this vector.
- norm(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the norm of a vector.
- norm(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Computes the norm of a 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 deviationsigma
. - NormalMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
-
The HMM states use the Normal distribution to model the observations.
- 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(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.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) - 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, RandomStandardNormalGenerator) - 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(int) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG
-
Constructs a standard 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(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.
- 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
-
Constructs 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
-
Deprecated.Not supported yet.
- NullMonitor<S> - Class in dev.nm.misc.algorithm.iterative.monitor
-
This
IterationMonitor
does nothing when a new iterate is added. - 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.
- NUMERICAL - dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
-
use the finite difference method
- 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.
O
- ObjectResampler<X> - Interface in dev.nm.stat.random.sampler.resampler
-
This is the interface of a re-sampler method for objects.
- objects() - Method in class dev.nm.combinatorics.Ties
-
Get the set of objects counted.
- observation - Variable in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim.Innovation
-
the simulated observation
- observation - Variable in class dev.nm.stat.dlm.univariate.DLMSim.Innovation
-
the simulated observation
- observation() - Method in class dev.nm.stat.hmm.HmmInnovation
-
Get the observation associated with the hidden state.
- ObservationEquation - Class in dev.nm.stat.dlm.univariate
-
This is the observation equation in a controlled dynamic linear model.
- ObservationEquation(double, double) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
-
Construct a time-invariant an observation equation.
- ObservationEquation(double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
-
Construct a time-invariant an observation equation.
- ObservationEquation(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
-
Construct an observation equation.
- ObservationEquation(UnivariateRealFunction, UnivariateRealFunction, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
-
Construct an observation equation.
- ObservationEquation(ObservationEquation) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
-
Copy constructor.
- ODE - Class in dev.nm.analysis.differentialequation.ode.ivp.problem
-
An ordinary differential equation (ODE) is an equation in which there is only one independent variable and one or more derivatives of a dependent variable with respect to the independent variable, so that all the derivatives occurring in the equation are ordinary derivatives.
- ODE(RealScalarFunction, double[], double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
-
Construct an ODE of order n together with its initial values.
- ODE1stOrder - Class in dev.nm.analysis.differentialequation.ode.ivp.problem
-
A first order ordinary differential equation (ODE) initial value problem (IVP) takes the following form.
- ODE1stOrder(DerivativeFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Constructs a first order ODE with the given vector-valued function and its initial values.
- ODE1stOrder(ODE) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Reduces a high order ODE to a system of first order ODEs.
- ODE1stOrder(RealScalarFunction[], double[], double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Constructs a system of first order ODEs {Yi} with their initial values {yi0}.
- ODE1stOrder(RealVectorFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Constructs a first order ODE with the given vector-valued function and its initial values.
- ODE1stOrderWith2ndDerivative - Class in dev.nm.analysis.differentialequation.ode.ivp.problem
-
Some ODE solvers require the second derivative for more accurate Taylor series approximation.
- ODE1stOrderWith2ndDerivative(DerivativeFunction, DerivativeFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative
-
Constructs a first order ODE with initial values.
- ODE1stOrderWith2ndDerivative(RealVectorFunction, RealVectorFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative
-
Constructs a first order ODE with initial values.
- ODEIntegrator - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver
-
This defines the interface for the numerical integration of a first order ODE, for a sequence of pre-defined steps.
- ODESolution - Class in dev.nm.analysis.differentialequation.ode.ivp.solver
-
Solution to an ODE problem.
- ODESolution(double[], Vector[]) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolution
-
Create a solution with estimated values at the given points.
- ODESolver - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver
-
Solver for first order ODE problems.
- of(LocalDate, LocalDate) - Static method in class dev.nm.misc.datastructure.time.LocalDateInterval
- of(LocalDateTime, LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
-
Creates an interval from a start and end time.
- of(LocalDateTime, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
-
Creates an interval from a start time and a period.
- of(Period, LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
-
Creates an interval from a period and an end time.
- OLSBeta - Class in dev.nm.stat.regression.linear.ols
-
Beta coefficient estimator, β^, of an Ordinary Least Square linear regression model.
- OLSBeta(Vector, OLSResiduals) - Constructor for class dev.nm.stat.regression.linear.ols.OLSBeta
-
Constructs an instance of
Beta
. - OLSRegression - Class in dev.nm.stat.regression.linear.ols
-
(Weighted) Ordinary Least Squares (OLS) is a method for fitting a linear regression model.
- OLSRegression(LMProblem) - Constructor for class dev.nm.stat.regression.linear.ols.OLSRegression
-
Constructs an OLSRegression instance.
- OLSRegression(LMProblem, double) - Constructor for class dev.nm.stat.regression.linear.ols.OLSRegression
-
Constructs an OLSRegression instance.
- OLSResiduals - Class in dev.nm.stat.regression.linear.ols
-
This is the residual analysis of the results of an ordinary linear regression model.
- OLSResiduals(LMProblem, Vector) - Constructor for class dev.nm.stat.regression.linear.ols.OLSResiduals
-
Performs the residual analysis for an ordinary linear regression problem.
- OLSSolver - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
-
This class solves an over-determined system of linear equations in the ordinary least square sense.
- OLSSolver(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolver
-
Construct an OLS solver for an over-determined system of linear equations.
- OLSSolverByQR - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
-
This class solves an over-determined system of linear equations in the ordinary least square sense.
- OLSSolverByQR(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverByQR
-
Construct an OLS solver for an over-determined system of linear equations.
- OLSSolverBySVD - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
-
This class solves an over-determined system of linear equations in the ordinary least square sense.
- OLSSolverBySVD(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverBySVD
-
Construct an OLS solver for an over-determined system of linear equations.
- ONE - Static variable in class dev.nm.analysis.function.polynomial.Polynomial
-
a polynomial representing 1
- ONE - Static variable in class dev.nm.number.complex.Complex
-
a number representing 1.0 + 0.0i
- ONE - Static variable in class dev.nm.number.Real
-
a number representing 1
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
- ONE() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
-
Get an identity matrix that has the same dimension as this matrix.
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
- ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
- ONE() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
- ONE() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
- ONE() - Method in interface dev.nm.algebra.structure.Monoid
-
The multiplicative element 1 in the group such that for any elements a in the group, the equation 1 × a = a × 1 = a holds.
- ONE() - Method in class dev.nm.analysis.function.polynomial.Polynomial
- ONE() - Method in class dev.nm.number.complex.Complex
-
Get one - the number representing 1.0 + 0.0i.
- ONE() - Method in class dev.nm.number.Real
- ONE_SAMPLE - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Type
-
the one-sample Kolmogorov-Smirnov test
- OneDimensionTimeSeries<T extends Comparable<? super T>> - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime
-
This class constructs a univariate realization from a multivariate realization by taking one of its dimension (coordinate).
- OneDimensionTimeSeries(MultivariateTimeSeries<T, ? extends MultivariateTimeSeries.Entry<T>>, int) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
-
Construct a univariate realization from a multivariate realization by taking one of its dimension (coordinate).
- ones(int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a matrix of 1's.
- ones(int, int, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a matrix of the same scalar, e.g.,1.
- oneSidedPvalue(ProbabilityDistribution, double) - Static method in class dev.nm.stat.test.HypothesisTest
-
The one-sided p-value is the probability of observing a test statistic at least as extreme as the one observed.
- OneWayANOVA - Class in dev.nm.stat.test.mean
-
The One-Way ANOVA test tests for the equality of the means of several groups.
- OneWayANOVA(double[]...) - Constructor for class dev.nm.stat.test.mean.OneWayANOVA
-
Perform the one-way ANOVA test to test for the equality of the means of several groups.
- OnlineInterpolator - Interface in dev.nm.analysis.curvefit.interpolation
-
An online interpolator allows dynamically adding more points for interpolation.
- OPEN - dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type
-
The first and the last terms in the Euler-Maclaurin formula are not included in the sum.
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
- opposite() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
-
Get the opposite of this matrix.
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
- opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
- opposite() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
- opposite() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
- opposite() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- opposite() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
- opposite() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
- opposite() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Get the opposite of this vector.
- opposite() - Method in interface dev.nm.algebra.structure.AbelianGroup
-
For each a in G, there exists an element b in G such that a + b = b + a = 0.
- opposite() - Method in class dev.nm.analysis.function.polynomial.Polynomial
- opposite() - Method in class dev.nm.number.complex.Complex
- opposite() - Method in class dev.nm.number.Real
- opposite(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Multiples a vector by -1, element-by-element.
- optimalModelByAIC() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
Selects the optimal ARIMA model by AIC.
- optimalModelByAICC() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
-
Selects the optimal ARIMA model by AICC.
- optimalWeights(Matrix, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel
-
Computes the weights based on given historical returns and the risk-aversion index λ.
- optimalWeights(Vector, Matrix, double, double) - Method in interface tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizer
-
Solves for the optimal weights given the moments, lambda, and eta.
- optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerLongOnly
- optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights
- optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerNoConstraint
- optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerShrankMean
- OptimalWeights(Vector, Ceta.PortfolioMoments) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
- Optimizer<P,S> - Interface in dev.nm.solver
-
Optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives.
- OptimProblem - Interface in dev.nm.solver.problem
-
This is an optimization problem that minimizes a real valued objective function, one or multi dimension.
- order() - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector
-
Get the order of this predictor-corrector pair.
- order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
- order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
- order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
- order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
- order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
- order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
- order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
- order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
- order() - Method in interface dev.nm.stat.random.rng.univariate.uniform.linear.LinearCongruentialGenerator
-
Get the order of recursion.
- order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
- order(double[]) - Static method in class dev.nm.number.DoubleUtils
-
Returns a permutation which rearranges its first argument into ascending or descending order.
- order(double[], boolean) - Static method in class dev.nm.number.DoubleUtils
-
Returns a permutation which rearranges its first argument into ascending or descending order.
- OrderedPairs - Interface in dev.nm.analysis.function.tuple
-
Cartesian products and binary relations (and hence the ubiquitous functions) are defined in terms of ordered pairs.
- OrderStatisticsDistribution - Class in dev.nm.stat.evt.evd.univariate
-
The asymptotic nondegenerate distributions of the r-th smallest (largest) order statistic.
- OrderStatisticsDistribution(ProbabilityDistribution, int, int) - Constructor for class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
-
Create an instance with the probability distribution of \(X\), the number of iid samples to be drawn, and the order statistic.
- OrnsteinUhlenbeckProcess - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
-
This class represents a univariate Ornstein-Uhlenbeck (OU) process.
- OrnsteinUhlenbeckProcess(double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
-
Construct a univariate OU process with unit volatility.
- OrnsteinUhlenbeckProcess(double, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
-
Construct a univariate OU process.
- OrnsteinUhlenbeckProcess(OrnsteinUhlenbeckProcess) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
-
Copy constructor.
- OrStopConditions - Class in dev.nm.misc.algorithm.stopcondition
-
Combines an arbitrary number of stop conditions, terminating when the first condition is met.
- OrStopConditions(StopCondition...) - Constructor for class dev.nm.misc.algorithm.stopcondition.OrStopConditions
- OrthogonalPolynomialFamily - Interface in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
-
This factory class produces a family of orthogonal polynomials.
- OUFitting - Interface in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
-
This interface defines an estimation procedure to fit a univariate Ornstein-Uhlenbeck process.
- OUFittingMLE - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
-
This class fits a univariate Ornstein-Uhlenbeck process by using MLE.
- OUFittingMLE() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
-
Create an instance that estimates the volatility parameter σ.
- OUFittingMLE(boolean) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
-
Create an instance with the option whether to estimate the volatility parameter.
- OUFittingOLS - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
-
This class fits a univariate Ornstein-Uhlenbeck process by using least squares regression.
- OUFittingOLS() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
-
Create an instance that estimates the volatility parameter σ.
- OUFittingOLS(boolean) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
-
Create an instance with the option whether to estimate the volatility parameter.
- OUProcess - Interface in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
- OUSim - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
-
This class simulates a discrete path of a univariate Ornstein-Uhlenbeck (OU) process.
- OUSim(OUProcess) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
-
Create an OU process simulator with a time interval of 1, using the overall mean as the starting value.
- OUSim(OUProcess, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
-
Create an OU process simulator using the overall mean as the starting value.
- OUSim(OUProcess, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
-
Create an OU process simulator.
- OUSim(OUProcess, double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
-
Create an OU process simulator.
- outcome() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassFunction.Mass
-
Gets the outcome.
- OuterProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation
-
The outer product of two vectors a and b, is a row vector multiplied on the left by a column vector.
- OuterProduct(Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.OuterProduct
- outgoingArcs(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
- outgoingArcs(V) - Method in interface dev.nm.graph.DiGraph
-
Gets the set of all outgoing arcs associated with a vertex in this graph.
- outgoingArcs(V) - Method in class dev.nm.graph.type.SparseDiGraph
- outgoingArcs(V) - Method in class dev.nm.graph.type.SparseTree
- overdispersion() - Method in class dev.nm.stat.regression.linear.glm.GLMResiduals
-
Gets the over-dispersion.
- overdispersion() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMResiduals
-
Computes the over-dispersion.
- overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
- overdispersion(Vector, Vector, int) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
-
Over-dispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on the nominal variance of a given simple statistical model.
- overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
- overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
- overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
- overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
- OVERLAP - dev.nm.interval.IntervalRelation
-
X overlaps with Y.
- OVERLAP_INVERSE - dev.nm.interval.IntervalRelation
-
Y overlaps with X.
- overlaps(LocalDateTimeInterval) - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
-
Whether this time interval overlaps with the given time interval.
P
- 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 - Variable in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
-
the success probability in each trial
- p - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
- 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() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
-
Get the number of AR terms.
- 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() - 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.
- P11213 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P1279 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P19937 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P21701 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P2203 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P2281 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P23209 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P3217 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P4253 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P4423 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P44497 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P521 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P607 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P9689 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- P9941 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
- 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,T extends Comparable<? super T>> - 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(int) - Constructor for class dev.nm.misc.parallel.ParallelExecutor
-
Creates an instance with a specified concurrency number.
- ParallelExecutor(String) - Constructor for class dev.nm.misc.parallel.ParallelExecutor
-
Creates an instance with a specified executor name.
- 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(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
- 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
- parents(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
- 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
- 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)}.
- PAST - dev.nm.dsp.univariate.operation.system.doubles.MovingAverage.Side
-
Use only past values.
- paste(Collection<String>, String) - Static method in class dev.nm.misc.StringUtils
-
Concatenates
String
s into oneString
. - 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[]) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
-
Constructs a block bootstrap sample generator.
- 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) - 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.
- 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>, long, PattonPolitisWhite2009ForObject.Type, ConcurrentCachedRLG, ConcurrentCachedRNG) - 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>, 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.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) - 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.
- 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, 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.
- 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) - 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.
- 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, 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.
- 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() - 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(PenaltyMethodMinimizer.PenaltyFunctionFactory, double, IterativeC2Minimizer) - 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.
- periodCountBetween(LocalDateTime, LocalDateTime, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Returns the number of whole periods between two time instants.
- periodDayCount(Period, int) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Estimates the number of days in a period, with the assumed number of days per month.
- periodicInstants(LocalDateTime, Period, int) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Generates a list of periodic time instants.
- periodicInstantsSpanningInterval(LocalDateTimeInterval, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Generates a list of periodic time instants which span the whole given interval.
- periodicInstantsWithinInterval(LocalDateTimeInterval, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Generates a list of periodic time instants within a given interval.
- 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() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
-
Get the impact matrix.
- 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.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).
- 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.root.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
-
An immutable
PolygonalChain
that is backed by anArrayList
. - 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) - 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, 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.DefaultRoot - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Computes the matrix root by Cholesky and on failure by MatrixRootByDiagonalization.
- PortfolioRiskExactSigma.Diagonalization - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Computes the matrix root by MatrixRootByDiagonalization.
- 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() - 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
- position(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
- 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(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- 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(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Raise each element in an array to the power of the given exponent.
- pow(int) - Method in class dev.nm.analysis.function.polynomial.Polynomial
- pow(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Takes a power of a vector, element-by-element.
- pow(Complex, Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
-
z1 to the power z2.
- 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(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 - 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) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Pow
-
Construct the power matrix An so that An = (1e100)scale * B
- 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
- 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(LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
-
Gets 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.
- PrimalDualInteriorPointMinimizer1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
-
The SOCP dual problem we are solving here is : \max {\bm b}^T \hat{\bm y} \\ {\rm s.t.} ({\bm A_i^q})^T \hat{\bm y} + {\bm z_i^q} = c_i^q,\ {\bm z_i^q}\in \mathcal{K}_q^{q_i},\ for i\in [n_q];\\ ({\bm A^{\ell}})^T \hat{\bm y} + {\bm z}^{\ell} = c^{\ell},\ {\bm z}^{\ell} \ge 0;\\ ({\bm A^u})^T \hat{\bm y} = c^u;\\ \hat{\bm y} \in \mathbb{R}^m;\ {\bm z}^{\ell}\in \mathbb{R}^{n_{\ell}};\ {\bm z}^u \in \mathbb{R}^{n_u}.
- PrimalDualInteriorPointMinimizer1(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1
-
Constructs a Primal Dual Interior Point minimizer to solve Dual Second Order Conic Programming problems.
- PrimalDualInteriorPointMinimizer1.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 Predictor-Corrector 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) - 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, 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.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>>) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassQuantile
-
Constructs the quantile function for a probability mass function.
- ProbabilityMassQuantile(Iterable<ProbabilityMassFunction.Mass<X>>, double) - 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 - 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.
- Problem(Matrix, Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.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(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}.
- product(List<GivensMatrix>) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
- 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, Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection
-
Project a vector v onto another vector.
- 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, List<Vector>) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection
-
Project a vector v onto a set of basis {wi}.
- 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() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
- proportionVar(int) - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the proportion of overall variance explained by the i-th principal component.
- 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) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.PseudoInverse
-
Construct the Moore-Penrose pseudo-inverse matrix of 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.
- 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() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
- 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
- pValue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Compute the two-sided p-value for a critical value.
- pValue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Compute the two-sided p-value for a critical value.
- 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
Q
- q - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
- q() - Method in class dev.nm.analysis.function.special.beta.BetaRegularized
-
Get q, the shape parameter.
- q() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
-
Gets the number of A matrices.
- q() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
- q() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
-
Get the number of MA terms.
- q() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Get the number of MA terms.
- q() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get the number of MA terms.
- q() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
-
Get the number of ARCH terms.
- Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition
-
Returns the rotation matrix.
- Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization
-
Gets Q, such that Q * A * Q = T.
- Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
-
Get Q as in Q * D * Q' = A.
- Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition
-
Gets the Q matrix, where \[ Q = (Q_1 \times ...
- Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
Gets the Q matrix as in the real Schur canonical form Q'AQ = T.
- Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
-
Gets the Q matrix as in Q'AQ = D, where D is diagonal and Q is orthogonal.
- Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
- Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
-
Gets the Q matrix in the QR decomposition.
- Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
- Q() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
-
Get the orthogonal Q matrix in the QR decomposition, A = QR.
- QPbySOCPMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
-
We first convert a QP problem to an equivalent SOCP problem and then solve it using an SOCP solver.
- QPbySOCPMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer
-
Constructs an SOCP minimizer to solve quadratic programming problems.
- QPbySOCPMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
- QPbySOCPMinimizer1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
-
A QP problem is first converted into an equivalent SOCP problem
SOCPGeneralProblem1
and then solve it using an SOCP solverPrimalDualInteriorPointMinimizer1
. - QPbySOCPMinimizer1(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1
-
Constructs an SOCP minimizer to solve quadratic programming problems.
- QPbySOCPMinimizer1.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
- QPConstraint - Interface in tech.nmfin.portfoliooptimization.markowitz.constraints
-
This interface allows adding constraints to a Quadratic Programming problem solving w_eff, the efficient frontier or the optimal allocation of assets.
- QPDualActiveSetMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
-
This implementation solves a Quadratic Programming problem using the dual active set algorithm.
- QPDualActiveSetMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer
- QPDualActiveSetMinimizer(double, int, boolean) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer
- QPDualActiveSetMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
-
This is the solution to a Quadratic Programming problem using the Dual Active Set algorithm.
- QPException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
-
This is the exception thrown when there is an error solving a quadratic programming problem.
- QPException() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPException
-
Construct an instance of
QPException
. - QPInfeasible - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
-
This is the exception thrown by a quadratic programming solver when the quadratic programming problem is infeasible, i.e., no solution.
- QPInfeasible() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPInfeasible
- QPMinimizer - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver
-
A typedef for QP minimizer.
- QPMinWeights - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
- QPMinWeights(double...) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
- QPMinWeights(int, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
- QPMinWeights(Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
- QPNoConstraint - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
-
Deprecated.This constraint means that you can borrow indefinitely, which makes very little economics meaning.
- QPNoConstraint() - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint
-
Deprecated.
- QPNoShortSelling - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
- QPNoShortSelling(int) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling
- QPPrimalActiveSetMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
-
This implementation solves a Quadratic Programming problem using the Primal Active Set algorithm.
- QPPrimalActiveSetMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer
-
Constructs a Primal Active Set minimizer to solve quadratic programming problems.
- QPPrimalActiveSetMinimizer(double, int, boolean) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer
-
Constructs a Primal Active Set minimizer to solve quadratic programming problems.
- QPPrimalActiveSetMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
-
This is the solution to a Quadratic Programming problem using the Primal Active Set algorithm.
- QPProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
-
Quadratic Programming is the problem of optimizing (minimizing) a quadratic function of several variables subject to linear constraints on these variables.
- QPProblem(QuadraticFunction, LinearEqualityConstraints, LinearGreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Construct a quadratic programming problem with linear equality and greater-than-or-equal-to constraints.
- QPProblem(QuadraticFunction, LinearEqualityConstraints, LinearGreaterThanConstraints, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Construct a quadratic programming problem.
- QPProblem(QuadraticFunction, LinearEqualityConstraints, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Construct a quadratic programming problem with linear equality and less-than-or-equal-to constraints.
- QPProblem(QuadraticFunction, LinearGreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Construct a quadratic programming problem with linear greater-than-or-equal-to constraints.
- QPProblem(QuadraticFunction, LinearGreaterThanConstraints, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Construct a quadratic programming problem with linear inequality constraints.
- QPProblem(QuadraticFunction, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Construct a quadratic programming problem with linear less-than-or-equal-to constraints.
- QPProblem(QPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
-
Copy constructor.
- QPProblemOnlyEqualityConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
-
A quadratic programming problem with only equality constraints can be converted into a equivalent quadratic programming problem without constraints, hence a mere quadratic function.
- QPProblemOnlyEqualityConstraints(QuadraticFunction, LinearEqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
-
Construct a quadratic programming problem with only equality constraints.
- QPSimpleMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
-
These are the utility functions to solve simple quadratic programming problems that admit analytical solutions.
- QPSimpleMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
- QPSolution - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
-
This is a solution to a quadratic programming problem.
- QPtoSOCPTransformer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
-
Transforms a QPProblem to a SOCPGeneralProblem.
- QPtoSOCPTransformer() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer
- QPtoSOCPTransformer1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
-
Transforms a QPProblem to a SOCPGeneralProblem1.
- QPtoSOCPTransformer1() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer1
- QPUnity - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
- QPUnity(int) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity
- QPWeightsLimit - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
- QPWeightsLimit(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit
- QR - Class in dev.nm.algebra.linear.matrix.doubles.factorization.qr
-
QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.
- QR - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
-
For any matrix.
- QR - dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel.Method
-
use QR decomposition; moderately expensive (recommended)
- QR(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
-
Run the QR decomposition on a matrix.
- QR(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
-
Run the QR decomposition on a matrix.
- QR_SYMMETRIC - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
-
For a symmetric matrix.
- QRAlgorithm - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
-
The QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a matrix.
- QRAlgorithm(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
Runs the QR algorithm on a square matrix.
- QRAlgorithm(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
Runs the QR algorithm on a square matrix.
- QRAlgorithm(Matrix, double, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
Runs the QR algorithm on a square matrix.
- QRDecomposition - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.qr
-
QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.
- Qs() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
-
Gets the list of Qi's produced in the process of the QR algorithm (if
keepQs
is set totrue
). - Qt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
-
Get Q' as in Q * D * Q' = A.
- QuadraticFunction - Class in dev.nm.analysis.function.rn2r1
-
A quadratic function takes this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).
- QuadraticFunction(Matrix, Vector) - Constructor for class dev.nm.analysis.function.rn2r1.QuadraticFunction
-
Construct a quadratic function of this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p\).
- QuadraticFunction(Matrix, Vector, double) - Constructor for class dev.nm.analysis.function.rn2r1.QuadraticFunction
-
Construct a quadratic function of this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).
- QuadraticFunction(QuadraticFunction) - Constructor for class dev.nm.analysis.function.rn2r1.QuadraticFunction
-
Copy constructor.
- QuadraticMonomial - Class in dev.nm.analysis.function.polynomial
-
A quadratic monomial has this form: x2 + ux + v.
- QuadraticMonomial(double, double) - Constructor for class dev.nm.analysis.function.polynomial.QuadraticMonomial
-
Construct a quadratic monomial.
- QuadraticRoot - Class in dev.nm.analysis.function.polynomial.root
-
This is a solver for finding the roots of a quadratic equation, \(ax^2 + bx + c = 0\).
- QuadraticRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.QuadraticRoot
- QuadraticSyntheticDivision - Class in dev.nm.analysis.function.polynomial
-
Divide a polynomial P(x) by a quadratic monomial (x2 + ux + v) to give the quotient Q(x) and the remainder (b * (x + u) + a).
- QuadraticSyntheticDivision(Polynomial, QuadraticMonomial) - Constructor for class dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision
-
Divide a polynomial by a quadratic monomial.
- quant - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
point indices in F[i]; kappa index
- quantile(double) - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassQuantile
-
Gets the quantile at a cumulative probability mass.
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
-
Gets the quantile, the inverse of the cumulative distribution function.
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
-
Gets the quantile, the inverse of the cumulative distribution function.
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
- quantile(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
-
Gets the quantile, the inverse of the cumulative distribution function.
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
- quantile(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
- quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
- quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
- quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
- quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
- quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
- quantile(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
-
Deprecated.
- quantile(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
- quantile(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
-
Deprecated.
- quantile(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
-
Deprecated.
- quantile(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
- quantile(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
- Quantile - Class in dev.nm.stat.descriptive.rank
-
Quantiles are points taken at regular intervals from the cumulative distribution function (CDF) of a random variable.
- Quantile() - Constructor for class dev.nm.stat.descriptive.rank.Quantile
-
Construct a
Quantile
calculator using the default type:Quantile.QuantileType.APPROXIMATELY_MEDIAN_UNBIASED
, without initial data. - Quantile(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Quantile
-
Construct a
Quantile
calculator using the default type:Quantile.QuantileType.APPROXIMATELY_MEDIAN_UNBIASED
. - Quantile(double[], Quantile.QuantileType) - Constructor for class dev.nm.stat.descriptive.rank.Quantile
-
Construct a
Quantile
calculator. - Quantile.QuantileType - Enum in dev.nm.stat.descriptive.rank
-
the available quantile definitions
- QuarticRoot - Class in dev.nm.analysis.function.polynomial.root
-
This is a quartic equation solver that solves \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- QuarticRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRoot
-
Construct a quartic equation solver.
- QuarticRoot(QuarticRoot.QuarticSolver) - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRoot
-
Construct a quartic equation solver.
- QuarticRoot.QuarticSolver - Interface in dev.nm.analysis.function.polynomial.root
-
This defines a quartic equation solver.
- QuarticRootFerrari - Class in dev.nm.analysis.function.polynomial.root
-
This is a quartic equation solver that solves \(ax^4 + bx^3 + cx^2 + dx + e = 0\) using the Ferrari method.
- QuarticRootFerrari() - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
- QuarticRootFormula - Class in dev.nm.analysis.function.polynomial.root
-
This is a quartic equation solver that solves \(ax^4 + bx^3 + cx^2 + dx + e = 0\) using a root-finding formula.
- QuarticRootFormula() - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRootFormula
- QuasiBinomial - Class in dev.nm.stat.regression.linear.glm.quasi.family
-
This is the quasi Binomial distribution in GLM.
- QuasiBinomial() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiBinomial
- quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiBinomial
- quasiDeviance(double, double) - Method in interface dev.nm.stat.regression.linear.glm.quasi.family.QuasiDistribution
-
the quasi-deviance function corresponding to a single observation
- quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGamma
- quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGaussian
- quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiInverseGaussian
- quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiPoisson
- QuasiDistribution - Interface in dev.nm.stat.regression.linear.glm.quasi.family
-
This interface represents the quasi-distribution used in GLM.
- QuasiFamily - Class in dev.nm.stat.regression.linear.glm.quasi.family
-
This interface represents the quasi-family used in GLM.
- QuasiFamily(QuasiBinomial) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
-
Construct a Binomial quasi-family.
- QuasiFamily(QuasiDistribution, LinkFunction) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
- QuasiFamily(QuasiGamma) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
-
Construct a Gamma quasi-family.
- QuasiFamily(QuasiGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
-
Construct a Gaussian quasi-family.
- QuasiFamily(QuasiInverseGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
-
Construct an Inverse Gaussian quasi-family.
- QuasiFamily(QuasiPoisson) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
-
Construct a Poisson quasi-family.
- QuasiGamma - Class in dev.nm.stat.regression.linear.glm.quasi.family
-
This is the quasi Gamma distribution in GLM.
- QuasiGamma() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGamma
- QuasiGaussian - Class in dev.nm.stat.regression.linear.glm.quasi.family
-
This is the quasi Gaussian distribution in GLM.
- QuasiGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGaussian
- QuasiGLMBeta - Class in dev.nm.stat.regression.linear.glm.quasi
-
This is the estimate of beta, β^, in a quasi Generalized Linear Model, i.e., a GLM with a quasi-family of distributions.
- QuasiGLMBeta(QuasiGLMNewtonRaphson, GLMResiduals) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMBeta
-
Construct an instance of
Beta
. - QuasiGLMNewtonRaphson - Class in dev.nm.stat.regression.linear.glm.quasi
-
The Newton-Raphson method is an iterative algorithm to estimate the β of the quasi GLM regression.
- QuasiGLMNewtonRaphson(double, int) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
-
Constructs an instance to run the Newton-Raphson method.
- QuasiGLMProblem - Class in dev.nm.stat.regression.linear.glm.quasi
-
This class represents a quasi generalized linear regression problem.
- QuasiGLMProblem(Vector, Matrix, boolean, QuasiFamily) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMProblem
-
Constructs a quasi GLM problem.
- QuasiGLMProblem(LMProblem, QuasiFamily) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMProblem
-
Constructs a quasi GLM problem from a linear regression problem.
- QuasiGLMResiduals - Class in dev.nm.stat.regression.linear.glm.quasi
-
Residual analysis of the results of a quasi Generalized Linear Model regression.
- QuasiInverseGaussian - Class in dev.nm.stat.regression.linear.glm.quasi.family
-
This is the quasi Inverse-Gaussian distribution in GLM.
- QuasiInverseGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiInverseGaussian
- quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiBinomial
- quasiLikelihood(double, double) - Method in interface dev.nm.stat.regression.linear.glm.quasi.family.QuasiDistribution
-
the quasi-likelihood function corresponding to a single observation Q(μ; y)
- quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGamma
- quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGaussian
- quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiInverseGaussian
- quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiPoisson
- QuasiMinimalResidualSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
-
The Quasi-Minimal Residual method (QMR) is useful for solving a non-symmetric n-by-n linear system.
- QuasiMinimalResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
Construct a Quasi-Minimal Residual (QMR) solver.
- QuasiMinimalResidualSolver(PreconditionerFactory, PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
Construct a Quasi-Minimal Residual (QMR) solver.
- QuasiNewtonMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
-
The Quasi-Newton methods in optimization are for finding local maxima and minima of functions.
- QuasiNewtonMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer
-
Construct a multivariate minimizer using a Quasi-Newton method.
- QuasiNewtonMinimizer.QuasiNewtonImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
-
This is an implementation of the Quasi-Newton algorithm.
- QuasiPoisson - Class in dev.nm.stat.regression.linear.glm.quasi.family
-
This is the quasi Poisson distribution in GLM.
- QuasiPoisson() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiPoisson
- QuEST - Class in dev.nm.stat.covariance.nlshrink.quest
-
QuEST is a function that generates sample eigenvalues from population eigenvalues.
- QuEST(Vector, int) - Constructor for class dev.nm.stat.covariance.nlshrink.quest.QuEST
- QuEST(Vector, int, double, double) - Constructor for class dev.nm.stat.covariance.nlshrink.quest.QuEST
- QuEST.Result - Class in dev.nm.stat.covariance.nlshrink.quest
- quotient() - Method in class dev.nm.analysis.function.polynomial.HornerScheme
-
Get the quotient, Q(x).
- quotient() - Method in class dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision
-
Get the quotient Q(x).
R
- 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.
- 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_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.
- R1Projection - Class in dev.nm.analysis.function.rn2r1
-
Projection creates a real-valued function
RealScalarFunction
from a vector-valued functionRealVectorFunction
by taking only one of its coordinate components in the vector output. - 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
- 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.
- RANDOM - dev.nm.stat.descriptive.rank.Rank.TiesMethod
- 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
-
Constructs a random CSRSparseMatrix.
- randomDenseMatrix(int, int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a random DenseMatrix.
- randomDOKSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a random DOKSparseMatrix.
- 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
-
Constructs a random LILSparseMatrix.
- 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
-
Constructs a random LowerTriangularMatrix.
- 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) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator 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(DiscreteSDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from a discrete SDE.
- RandomRealizationOfRandomProcess(SDE, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
-
Construct a random realization generator from an SDE.
- RandomRealizationOfRandomProcess(SDE, TimeGrid, double) - 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
-
Constructs a random SymmetricMatrix.
- randomUpperTriangularMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Constructs a random UpperTriangularMatrix.
- 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, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk
-
Constructs a univariate stochastic process from an SDE.
- RandomWalk(DiscreteSDE, TimeGrid, 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() - 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(int) - Method in class dev.nm.stat.descriptive.rank.Rank
-
Get the rank of the i-th element.
- rank(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the numerical rank of a matrix.
- rank(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
-
Compute the numerical rank of a matrix.
- 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[], double, Rank.TiesMethod) - 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.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
-
Constructs 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(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.
- 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(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines an array of vectors by rows.
- rbind(List<Vector>) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
-
Combines a list of array of vectors by rows.
- rbinom(int, int, Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates
n
random binomial numbers. - rbinom(int, int, Vector, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates
n
random binomial numbers. - 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-Ddouble
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-Ddouble
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-Ddouble
array, with a given separator which overrides thedefault separator
. - 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-Ddouble
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-Ddouble
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-Ddouble
array, with a given separator which overrides thedefault 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-Ddouble
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-Ddouble
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-Ddouble
array, with a given separator which overrides thedefault 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-Ddouble
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-Ddouble
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-Ddouble
array, with a given separator which overrides thedefault 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 adouble
. - Real(long) - Constructor for class dev.nm.number.Real
-
Construct a
Real
from an integer. - Real(String) - Constructor for class dev.nm.number.Real
-
Construct a
Real
from aString
. - Real(BigDecimal) - Constructor for class dev.nm.number.Real
-
Construct a
Real
from aBigDecimal
. - Real(BigInteger) - Constructor for class dev.nm.number.Real
-
Construct a
Real
from aBigInteger
. - 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.signal.infantino2010.Infantino2010PCA.Signal
-
Gets the last H-period accumulated realized return.
- RealMatrix - Class in dev.nm.algebra.linear.matrix.generic.matrixtype
-
This is a
Real
matrix. - RealMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
Construct a
Real
matrix. - RealMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
Construct a
Real
matrix. - RealMatrix(Real[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
-
Construct a
Real
matrix. - 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
-
This constructs a
RealScalarFunction
from anotherRealScalarFunction
by restricting/fixing the values of a subset of variables. - 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(double, Vector...) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from an array of 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(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(Vector...) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from an array 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(List<Vector>, double) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
-
Construct a vector space from a list of vectors.
- RealVectorSubFunction - Class in dev.nm.analysis.function.rn2rm
-
This constructs a
RealVectorFunction
from anotherRealVectorFunction
by restricting/fixing the values of a subset of variables. - 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
-
Constructs an n-dimensional interpolation using a given univariate interpolation algorithm.
- reduce(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
-
Deprecated.Not supported yet.
- Reference<T> - Class in dev.nm.misc.parallel
- Reference() - Constructor for class dev.nm.misc.parallel.Reference
- 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.
- 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.Householder4SubVector
- reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4ZeroGenerator
- reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
-
Apply the Householder matrix, H, to a column vector, x.
- 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
andY
. - 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 specifiedtolerance
, that is, ||r||2/base ≤ tolerance - RelativeTolerance(double) - Constructor for class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance
-
Construct an instance with
RelativeTolerance.DEFAULT_TOLERANCE
. - 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(Arc<VertexTree<T>>) - Method in class dev.nm.graph.type.VertexTree
- removeEdge(Arc<V>) - Method in class dev.nm.graph.type.SparseTree
- 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
- 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(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
- 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
- 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. - RepeatedCoordinatesException - Exception in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
- RepeatedCoordinatesException(MatrixCoordinate) - Constructor for exception dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.RepeatedCoordinatesException
- 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.
- replaceInPlace(Vector, int, Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Replaces a sub-vector with a given smaller vector.
- 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 - Variable in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
- 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.
- RESTRICTED_CONSTANT - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
-
This is trend type II: no restricted constant, no linear trend:
- 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(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.
- ReturnLevel(UnivariateEVD) - Constructor for class dev.nm.stat.evt.function.ReturnLevel
-
Construct the return level function for a given 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(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.
- ReturnPeriod(UnivariateEVD) - Constructor for class dev.nm.stat.evt.function.ReturnPeriod
-
Construct the return period function for a given 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) - Constructor for class tech.nmfin.returns.ReturnsMatrix
- ReturnsMatrix(Matrix, ReturnsCalculator) - 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(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. - reverse(T[]) - Static method in class dev.nm.misc.ArrayUtils
-
Reverse an array in place.
- 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(RealScalarFunction, 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[], double, int) - Constructor for class dev.nm.analysis.differentiation.Ridders
-
Construct the derivative function of a vector-valued 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(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 - dev.nm.analysis.differentiation.univariate.Dfdx.Method
-
Ridders' method: try a sequence of decreasing increments to the compute derivative, and then extrapolate to zero using Neville's algorithm.
- 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() - Constructor for class dev.nm.analysis.integration.univariate.riemann.Riemann
-
Construct an integrator.
- Riemann(double, int) - 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() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
Get the right, one-sided p-value.
- rightOneSidedPvalue() - Method in class dev.nm.stat.test.variance.F
-
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(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.
- 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.
- 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.
- 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. - rightShift(T[]) - Static method in class dev.nm.misc.ArrayUtils
-
Get a right shifted 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) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
-
Generates
n
random standard Normals. - rnorm(int, RandomStandardNormalGenerator) - 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, 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.
- 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.
- 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
-
Deprecated.Not supported yet.
- rotate(V) - Method in class dev.nm.graph.type.SparseTree
-
This method re-pivots the tree with a new root vertex.
- round(double, int) - Static method in class dev.nm.number.DoubleUtils
-
Round a number to the precision specified.
- round(double, DoubleUtils.RoundingScheme) - Static method in class dev.nm.number.DoubleUtils
-
Round up or down a number to an integer.
- 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_MAJOR_ORDER - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
-
This
Comparator
sorts the matrix coordinates first from top to bottom (rows), and then from left to right (columns). - 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() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
Run the genetic algorithm.
- run() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
- run() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA
-
Runs the regression.
- run(double[][], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Run the analysis with multi-period observations.
- run(double[], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
-
Run the analysis with single-period observations.
- 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(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.
- 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
-
Create a new instance of the Runge-Kutta-Fehlberg method for the given parameters, with the default safety factor value.
- 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
- RWEIBULL - dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
-
Reversed Weibull distribution.
- RYDBERG_RINF - Static variable in class dev.nm.misc.PhysicalConstants
-
The Rydberg constant \(R_{\infty}\) in reciprocal meter (m-1).
S
- 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.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Gets the value of s.
- s() - Method in class dev.nm.stat.descriptive.rank.Rank
-
\[ s = \sum(t_i^2 - t_i) \]
- S - dev.nm.stat.descriptive.rank.Quantile.QuantileType
-
the definition in 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() - 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.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.signal.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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
-
Construct the sample ACF for a time series.
- SampleAutoCorrelation(IntTimeTimeSeries, SampleAutoCovariance.Type) - 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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
-
Construct the sample ACVF for a time series.
- SampleAutoCovariance(IntTimeTimeSeries, SampleAutoCovariance.Type) - 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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
-
Construct the sample PACF for a time series.
- SamplePartialAutoCorrelation(IntTimeTimeSeries, SampleAutoCovariance.Type) - 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() - 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
- scale(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Scale each element in an array by a multiplier.
- 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(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(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(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
- scaled(double) - 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(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
- 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(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(Complex) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
- scaled(Real) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- scaled(Real) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
- scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
- scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
- scaled(Real) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Scale this vector by a constant, entry-by-entry.
- scaled(Real) - Method in class dev.nm.analysis.function.polynomial.Polynomial
- scaled(F) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
- scaled(F) - Method in interface dev.nm.algebra.structure.VectorSpace
-
× : F × V → V
- 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) - Constructor for class dev.nm.analysis.function.polynomial.ScaledPolynomial
-
Construct a scaled polynomial, with 2 as the base of the scaling factor.
- ScaledPolynomial(Polynomial, double) - Constructor for class dev.nm.analysis.function.polynomial.ScaledPolynomial
-
Construct a scaled polynomial, with a 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) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of a
double
. - ScientificNotation(double, int) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of a number in this form: x = a * 10b.
- ScientificNotation(long) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of a
long
. - ScientificNotation(BigDecimal) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of a number.
- ScientificNotation(BigDecimal, int) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of a number in this form: x = a * 10b.
- ScientificNotation(BigInteger) - Constructor for class dev.nm.number.ScientificNotation
-
Construct the scientific notation of an integer.
- 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.
- SDPT3v4_1a - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
-
This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3 version 4.
- SDPT3v4_1b - 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() - 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() - 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() - 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.interiorpoint.PrimalDualInteriorPointMinimizer1.Solution
-
[Previous Version] 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() - 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() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
-
Performs a search for parameters with no id.
- search(double, double) - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
- search(double, double) - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer.Solution
- search(double, double) - Method in class dev.nm.root.univariate.bracketsearch.GoldenMinimizer.Solution
- search(double, double) - Method in class dev.nm.root.univariate.GridSearchMinimizer.Solution
- search(double, double) - Method in interface dev.nm.root.univariate.UnivariateMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- search(double, double, double) - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
- search(double, double, double) - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
- search(double, double, double) - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer.Solution
- search(double, double, double) - Method in class dev.nm.root.univariate.GridSearchMinimizer.Solution
-
Search for a minimum within the interval [lower, upper].
- search(double, double, double) - Method in interface dev.nm.root.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(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(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.integer.bruteforce.BruteForceIPMinimizer.Solution
- search(Vector...) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.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.SQPActiveSetOnlyInequalityConstraintMinimizer.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(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(BBNode...) - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
- 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.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.PrimalDualPathFollowingMinimizer.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(PrimalDualSolution) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1.Solution
-
Searches for a solution that optimizes the objective function from the given starting point.
- 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.PrimalDualInteriorPointMinimizer1.Solution
- 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.QPDualActiveSetMinimizer.Solution
- 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(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.Solution
- 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(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(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
- SEED - Static variable in class dev.nm.stat.random.rng.RNGUtils
- 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
-
Semi-Implicit Extrapolation is a method of solving ordinary differential equations, that is similar to Burlisch-Stoer 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(double, double, double) - Static method in class dev.nm.number.DoubleUtils
-
Generates a sequence of
double
s fromfrom
up toto
with incrementsinc
. - seq(double, double, int) - Static method in class dev.nm.number.DoubleUtils
-
Generate a sequence of
n
equi-spaceddouble
values, fromstart
toend
(inclusive). - 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(int, int) - Static method in class dev.nm.number.DoubleUtils
-
Generates a sequence of
int
s fromfrom
up toto
with increments 1. - seq(int, int, int) - Static method in class dev.nm.number.DoubleUtils
-
Generates a sequence of
int
s fromfrom
up toto
with incrementsinc
. - Sequence - Interface in dev.nm.analysis.sequence
-
A sequence is an ordered list of (real) numbers.
- set(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- 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, 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) - Method in class dev.nm.misc.datastructure.SortableArray
- 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
-
Deprecated.GivensMatrix is immutable
- set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
-
Deprecated.use the swap functions instead
- 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, 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
-
Deprecated.SubMatrixRef is immutable
- set(int, int, double) - Method in class dev.nm.misc.datastructure.FlexibleTable
- set(int, int, Complex) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
- set(int, int, Real) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
- 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, F) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
- 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 positionfrom
. - 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.
- 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(T) - Method in class dev.nm.misc.parallel.Reference
- 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.
- 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(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
- setInitials(BBNode...) - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
- 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(PrimalDualSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1.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(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.Solution
- setInitials(S...) - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod
-
Supply the starting points for the search.
- 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(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Set the current value of the stochastic process.
- setXt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Set the current value of the stochastic process.
- 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
- 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
- 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[]) - 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[], 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.
- sigma - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
- sigma - Variable in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
-
the standard deviation
- sigma - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
- sigma() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
- 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() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
-
Gets the diffusion volatility.
- 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() - 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_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.
- 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
- 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(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.
- 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.
- SIMPLE - tech.nmfin.returns.ReturnsCalculators
-
The return is defined as the absolute return over the original value.
- 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) - Constructor for class dev.nm.graph.type.SimpleArc
-
Construct a simple arc.
- SimpleArc(V, V, double) - 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
-
A
SimpleCellFactory
produces SimpleCellFactory.SimpleCells. - 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) - Constructor for class dev.nm.graph.type.SimpleEdge
-
Construct a simple edge.
- SimpleEdge(V, V, double) - 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(double, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
-
Construct a
SimpleGridMinimizer
to solve unconstrained minimization problems. - 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.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) - 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, 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(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(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(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(int, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
-
Constructs a new instance to use
BoltzTemperatureFunction
,BoltzAnnealingFunction
and MetropolisAcceptanceProbabilityFunction. - SimulatedAnnealingMinimizer(TemperatureFunction, AnnealingFunction, TemperedAcceptanceProbabilityFunction, int, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
-
Constructs a new instance.
- 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 - Variable in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
-
the size
- 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() - 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() - 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.SOCPDualProblem1.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.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
- size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
- 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() - 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
- 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(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.
- 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
- SOCPConstraints - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
Marker interface for SOCP constraints.
- 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
- SOCPDualProblem1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
This is the Dual Second Order Conic Programming problem.
- SOCPDualProblem1(Vector, Matrix[], Vector[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
Constructs a dual SOCP problem.
- SOCPDualProblem1(Vector, Matrix[], Vector[], Matrix, Vector, Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
Constructs a dual SOCP problem.
- SOCPDualProblem1(SOCPDualProblem1) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
-
Copy constructor.
- SOCPDualProblem1.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(SOCPGeneralConstraint[]) - 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.
- SOCPGeneralProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
Many convex programming problems can be represented in the following form.
- 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.
- SOCPGeneralProblem(SOCPGeneralProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
-
Copy constructor.
- SOCPGeneralProblem1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
Many convex programming problems can be represented in the following form.
- SOCPGeneralProblem1(Vector, SOCPGeneralConstraint[], SOCPLinearInequality[], SOCPLinearEquality[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem1
-
Construct a general Second Order Conic Programming problem.\[ \begin{align*} \min_x \quad &; \mathbf{f^T} x \\ \textrm{s.t.} \quad &; \lVert {A_i}^T x + c_i \rVert_2 \leq b_i^T x + d_i,\quad i = 1,\dots,m \end{align*} \]
- SOCPGeneralProblem1(Vector, List<SOCPGeneralConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem1
-
Construct a general Second Order Conic Programming problem.
- SOCPGeneralProblem1(Vector, List<SOCPGeneralConstraint>, List<SOCPLinearInequality>, List<SOCPLinearEquality>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem1
-
Construct a general Second Order Conic Programming problem.
- SOCPGeneralProblem1(SOCPGeneralProblem1) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem1
-
Copy constructor.
- SOCPLinearBlackList - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
A black list means that the positions of some assets must be zero.
- SOCPLinearBlackList(int, Collection<Integer>) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
-
Creates a blacklist constraint.
- SOCPLinearBlackList(int, Collection<Integer>, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
-
Creates a blacklist constraint.
- SOCPLinearEqualities - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
A list of
SOCPLinearEquality
's. - SOCPLinearEqualities() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
- SOCPLinearEquality - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
Linear equality for SOCP problem.
- SOCPLinearEquality(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEquality
- SOCPLinearInequalities - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
A list of
SOCPLinearInequality
's. - SOCPLinearInequalities() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
- SOCPLinearInequality - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
-
Linear inequality for SOCP problem.
- SOCPLinearInequality(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequality
- SOCPLinearMaximumLoan - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
A maximum loan constraint.
- SOCPLinearMaximumLoan(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
-
Creates a maximum loan constraint.
- SOCPLinearMaximumLoan(Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
-
Creates a maximum loan constraint.
- SOCPLinearSectorExposure - Class in tech.nmfin.portfoliooptimization.socp.constraints.ybar
-
A sector exposure constraint.
- SOCPLinearSectorExposure(Vector[], Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
-
Creates a sector exposure constraint.
- SOCPLinearSectorExposure(Vector[], Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
-
Creates a sector exposure constraint.
- SOCPLinearSectorNeutrality - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
A sector neutrality means that the sum of weights for given sectors are zero.
- SOCPLinearSectorNeutrality(Vector[]) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
-
Creates a sector neutrality constraint.
- SOCPLinearSectorNeutrality(Vector[], double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
-
Creates a sector neutrality constraint.
- SOCPLinearSelfFinancing - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
A self financing constraint.
- SOCPLinearSelfFinancing(Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
-
Creates a self financing constraint.
- SOCPLinearSelfFinancing(Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
-
Creates a self financing constraint.
- SOCPLinearZeroValue - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
A zero value constraint.
- SOCPLinearZeroValue(int) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
-
Creates a zero value constraint.
- SOCPLinearZeroValue(int, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
-
Creates a zero value constraint.
- SOCPMaximumLoan - Class in tech.nmfin.portfoliooptimization.socp.constraints
-
Transforms a maximum loan constraint into the compact SOCP form.
- SOCPMaximumLoan(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
-
Constructs a maximum loan constraint.
- SOCPMaximumLoan(Vector, Vector, double) - 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) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
-
Constructs a black list constraint.
- 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.
- 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) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
-
Constructs a black list constraint.
- SOCPNoTradingList2(Vector, Matrix, double) - 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: \[ ||A^{T}x+c||_{2}\leq b^{T}x+d \] or this form: /[ A^T x = c, x \in \Re^m /] or this form: /[ A^T x \leq c, x \in \Re^m /]
- 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(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, 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) - 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.
- SOCPPortfolioProblem1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
-
Constructs an SOCP problem for portfolio optimization.
- SOCPPortfolioProblem1(SOCPPortfolioObjectiveFunction, SOCPPortfolioConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
-
Constructs an SOCP problem for portfolio optimization.
- SOCPPortfolioProblem1(SOCPPortfolioObjectiveFunction, Collection<SOCPPortfolioConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
-
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) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
-
Constructs a sector exposure constraint.
- SOCPSectorExposure(Vector, Vector[], Vector, double) - 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[]) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
-
Constructs a sector neutral constraint.
- SOCPSectorNeutrality(Vector, Vector[], double) - 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) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
-
Constructs a zero value constraint.
- SOCPSelfFinancing(Vector, double) - 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) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
-
Constructs a zero value constraint.
- SOCPZeroValue(Vector, double) - 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() - 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(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
- Solution(UnivariateRealFunction) - Constructor for class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
- 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
- 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).
- 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(double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
- solve(double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFormula
- 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(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver
-
Solve 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(LSProblem) - 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) - 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) - 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) - 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) - 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) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
- solve(LSProblem) - 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) - 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 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, IterationMonitor<Vector>) - 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.BiconjugateGradientStabilizedSolver
- solve(LSProblem, IterationMonitor<Vector>) - 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.ConjugateGradientNormalResidualSolver
- solve(LSProblem, IterationMonitor<Vector>) - 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.ConjugateGradientSquaredSolver
- solve(LSProblem, IterationMonitor<Vector>) - 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.GeneralizedMinimalResidualSolver
- solve(LSProblem, IterationMonitor<Vector>) - 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.QuasiMinimalResidualSolver
- solve(LSProblem, IterationMonitor<Vector>) - 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.stationary.GaussSeidelSolver
- solve(LSProblem, IterationMonitor<Vector>) - 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.SuccessiveOverrelaxationSolver
- solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
- solve(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver
-
Get a particular solution for the linear system, Ax = b.
- solve(Matrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver
-
Solves AX = B.
- solve(TridiagonalMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ThomasAlgorithm
-
Solves a tridiagonal matrix equation.
- 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(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(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(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(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(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(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(ODE1stOrderWith2ndDerivative) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation
- 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(Function<Vector, R>) - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer
- 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) - Method in class dev.nm.analysis.function.polynomial.root.QuadraticRoot
- solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRoot
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFormula
-
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
- solve(Polynomial, double) - Method in class dev.nm.analysis.function.polynomial.root.QuadraticRoot
-
Solve \(ax^2 + bx + c = 0\).
- 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, 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, 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(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(RealScalarFunction[], Vector) - Method in class dev.nm.analysis.root.multivariate.NewtonSystemRoot
-
Searches for a root, x such that f(x) = 0.
- 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, EqualityConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
-
Minimize a function subject to only equality 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(UnivariateRealFunction) - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer
-
Minimize a univariate function.
- solve(UnivariateRealFunction) - Method in class dev.nm.root.univariate.GridSearchMinimizer
-
Minimizes a univariate function.
- solve(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) - Method in class dev.nm.analysis.root.univariate.NewtonRoot
- 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.BisectionRoot
- solve(UnivariateRealFunction, double, 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, double, double...) - Method in class dev.nm.analysis.root.univariate.NewtonRoot
- 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(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, 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(RealVectorFunction) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
-
Solve the minimization problem to minimize F = vf' * vf.
- solve(RealVectorFunction, Vector) - Method in class dev.nm.analysis.root.multivariate.NewtonSystemRoot
-
Searches for a root, x such that f(x) = 0.
- solve(RealVectorFunction, RntoMatrix) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
-
Solve the minimization problem to minimize F = vf' * vf.
- solve(ConstrainedOptimSubProblem) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
- 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(SOCPDualProblem1) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1
- 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(LPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
- solve(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
- 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(LPRevisedSimplexSolver.Problem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
- 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(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1
- 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(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(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
- solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
- solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
-
Solves a constrained optimization sub-problem that is already in the form of a ConstrainedOptimProblem.
- 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(MinMaxProblem<T>) - Method in class dev.nm.solver.multivariate.minmax.LeastPth
- solve(C2OptimProblem) - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer
- solve(C2OptimProblem) - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer
- solve(C2OptimProblem) - Method in class dev.nm.root.univariate.bracketsearch.GoldenMinimizer
- solve(C2OptimProblem) - Method in class dev.nm.root.univariate.GridSearchMinimizer
- 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(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.geneticalgorithm.minimizer.deoptim.DEOptim
- solve(OptimProblem) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
- solve(OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
- solve(OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.DoubleBruteForceMinimizer
- solve(P) - Method in interface dev.nm.solver.Optimizer
-
Solve an optimization problem, e.g.,
OptimProblem
. - 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
- 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
- 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(double[], double[]) - Constructor for class dev.nm.analysis.function.tuple.SortedOrderedPairs
- SortedOrderedPairs(OrderedPairs) - Constructor for class dev.nm.analysis.function.tuple.SortedOrderedPairs
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- SparseDAGraph<V,E extends Arc<V>> - Class in dev.nm.graph.type
-
This class implements the sparse directed acyclic graph representation.
- SparseDAGraph() - Constructor for class dev.nm.graph.type.SparseDAGraph
-
Construct an empty directed acyclic graph.
- SparseDAGraph(boolean) - 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.ValueArray - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
- SparseMatrixUtils - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
These are the utility functions for
SparseMatrix
. - 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(double...) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector from a
double[]
. - 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, Collection<SparseVector.Entry>) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector.
- SparseVector(SparseVector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Copy constructor.
- SparseVector(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
-
Constructs a sparse vector from a vector.
- SparseVector.Entry - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
-
This is an entry in a
SparseVector
. - 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(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(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.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(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(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.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
-
This particular implementation of
SQPActiveSetOnlyEqualityConstraint1Minimizer
usesSQPASEVariation2
. - 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.
- 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.
- 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(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(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.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() - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Construct a variation.
- SQPASEVariation1(double, double, int) - 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() - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
-
Construct a variation.
- SQPASEVariation2(double, double, int) - 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(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the square roots of values.
- 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.
- 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
- start() - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
- START - dev.nm.interval.IntervalRelation
-
X starts Y.
- START_INVERSE - dev.nm.interval.IntervalRelation
-
Y starts X.
- 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(double, double) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Construct a time-invariant 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(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Construct a state equation without control variables.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Construct a state equation.
- StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Construct a state equation.
- StateEquation(StateEquation) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
-
Copy constructor.
- STATIONARY - dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type
- 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
-
A factory to construct a new
Statistic
. - 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() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
- stdev() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
Gets the stdev of the spread.
- stdev(double) - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
-
Gets the stdev of the last % portion in the spread.
- stdev(int) - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
-
Gets the standard deviation of the i-th element.
- 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(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
-
Construct a multivariate minimizer using a steepest descent method.
- 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.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(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(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.
- 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() - 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.interiorpoint.PrimalDualInteriorPointMinimizer1.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.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.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.
- 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
- 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) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.StepFunction
-
Construct a step function from a collection ordered pairs.
- StepFunction(OrderedPairs, double) - 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(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
- subMatrix(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
- subMatrix(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
- subMatrix(int, int, int, int) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix
-
Extracts a sub-matrix given the bounds of row and column indices (inclusive).
- 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(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(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(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.
- 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.
- 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) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
-
Constructs a reference to the whole matrix.
- 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.
- 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(double) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
- SubProblemMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
- SubProblemMinimizer(SubProblemMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem, IterativeSolution<Vector>>>) - 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(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
- 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
- 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, 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).
- subVector(Vector, int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
-
Gets a sub-vector from a 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).
- 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(double[]) - Method in class dev.nm.analysis.sequence.Summation
-
Partial summation of the selected terms.
- 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(double, double, double) - Method in class dev.nm.analysis.sequence.Summation
-
Sum up the terms from
from
toto
with the incrementinc
. - sum(int...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the sum of the values.
- sum(int, int) - Method in class dev.nm.analysis.sequence.Summation
-
Sum up the terms from
from
toto
with the increment 1. - sum(int, int, int) - Method in class dev.nm.analysis.sequence.Summation
-
Sum up the terms from
from
toto
with the incrementinc
. - 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(BigDecimal...) - Static method in class dev.nm.number.big.BigDecimalUtils
-
Sum up the
BigDecimal
numbers. - 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).
- sum2(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
-
Get the sum of squares of the values.
- 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) - Constructor for class dev.nm.analysis.sequence.Summation
-
Construct a finite summation series.
- Summation(Summation.Term, double) - Constructor for class dev.nm.analysis.sequence.Summation
-
Construct a 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 ofdata
with respect to amean
. - sumToInfinity(double, double) - Method in class dev.nm.analysis.sequence.Summation
-
Sum up the terms from
from
to infinity with incrementinc
until the series converges. - 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. - sumUpLastRows(Matrix, int, int) - Static method in class tech.nmfin.signal.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() - Method in class dev.nm.stat.factor.pca.PCAbySVD
-
Gets the Singular Value Decomposition (SVD) of matrix X.
- 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 - 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(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 columns
. - 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(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.
- 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
- SYMMETRIC_WINDOW - dev.nm.dsp.univariate.operation.system.doubles.MovingAverage.Side
-
Use a symmetric window of past and future values, centered around lag 0.
- 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(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Construct a symmetric matrix from a 2D
double[][]
array. - SymmetricMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
-
Construct a symmetric matrix of dimension dim * dim.
- 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.
- SYMMETRY - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
-
For a 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
-
Returns a synchronized (thread-safe)
RandomLongGenerator
backed by a specified generator. - synchronizedRNG(RandomNumberGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils
-
Returns a synchronized (thread-safe)
RandomNumberGenerator
backed by a specified generator. - synchronizedRVG(RandomVectorGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils
-
Returns a synchronized (thread-safe)
RandomVectorGenerator
backed by a specified generator. - 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
- t - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
distinct population eigenvalues
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
- 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.FastKroneckerProduct
- t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
- t() - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
-
Gets the penalization parameter t for L1 regularization.
- 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.univariate.sde.FtWt
-
Get the current time.
- 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 - 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 - Variable in class tech.nmfin.signal.infantino2010.Infantino2010PCA
- 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[], 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(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() - 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.linearsystem.Kernel
-
Get the transformation matrix, T, such that T * A = U.
- T() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
-
Get the transformed matrix T.
- 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() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid
-
Get the end time.
- T() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
- 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 - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
- Table - Interface in dev.nm.misc.datastructure
-
A table is a means of arranging data in rows and columns.
- 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 - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
population eigenvalues in ascending order
- tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
-
Gets estimated population eigenvalues in ascending order.
- TauEstimator - Class in dev.nm.stat.covariance.nlshrink
-
Non-linear shrinkage estimator of population eigenvalues.
- TauEstimator(int, int, Vector) - Constructor for class dev.nm.stat.covariance.nlshrink.TauEstimator
- TauEstimator(int, int, Vector, int, double) - 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.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.signal.infantino2010 - package tech.nmfin.signal.infantino2010
- 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() - 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() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
-
Get all the MA coefficients.
- 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(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(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
- THOMSON - dev.nm.stat.factor.factoranalysis.FactorAnalysis.ScoringRule
-
Thomson's (1951) scores
- 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() - 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(int, long) - 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()
OrA1.nCols() != A2.nCols()
- throwIfIncompatible4Multiplication(Table, Vector) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Throws if
A.nCols() != v.size()
- throwIfIncompatible4Multiplication(Table, Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
-
Throws if
A1.nCols() != A2.nRows()
- 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 notnull
. - 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() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
-
Get the current time.
- time() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
Gets the forecast time.
- time(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
-
Get the i-th time point.
- 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.
- 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(LocalDateTime, LocalDateTime) - 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
-
This is a collection of time intervals
TimeInterval
. - TimeIntervals() - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Construct an empty collection of time interval.
- TimeIntervals(Interval<LocalDateTime>) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Construct a collection consisting of one time interval.
- TimeIntervals(Interval<LocalDateTime>...) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Construct a collection of time intervals.
- TimeIntervals(Intervals<LocalDateTime>) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Copy constructor.
- TimeIntervals(LocalDateTime, LocalDateTime) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
-
Construct a collection consisting of one time interval.
- 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() - 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.
- toArray(T[]) - Method in class dev.nm.misc.datastructure.IdentityHashSet
- 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
- toCSV() - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
- 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. - toEntryArray(int[], int[], double[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
- toEntryList(int[], int[], double[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
- 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(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
- 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
- toPrimitive(Double[]) - Static method in class dev.nm.number.DoubleUtils
-
Convert a
Double
array to a primitivedouble
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() - 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() - 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.misc.datastructure.time.LocalDateInterval
- toString() - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
- toString() - Method in class dev.nm.number.complex.Complex
- 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
- 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(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
-
Get the
String
representation of a matrix. - toString(SparseMatrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
-
Returns a string representation of a SparseMatrix.
- 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.
- TRACE - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test
-
the TRACE test
- 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
- transform(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer1
- 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(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(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(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. - TRIMMED_MEANS - dev.nm.stat.test.variance.Levene.Type
-
compute the absolute deviations from the group trimmed means
- 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) - Constructor for class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
Construct a truncated standard Normal distribution.
- TruncatedNormalDistribution(double, double, double, double) - Constructor for class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
-
Construct a truncated 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
- TWO_SAMPLE - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Type
-
the two-sample Kolmogorov-Smirnov test
- TWO_SIDED - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side
-
compute Dn
- type - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
-
the label type
- TYPE_I - dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance.Type
-
default: the denominator is the time series length
- TYPE_II - dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance.Type
-
the denominator is the time series length minus the lag
U
- u - Variable in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
-
the solution matrix
- u() - Method in class dev.nm.analysis.function.polynomial.QuadraticMonomial
-
Get u as in (x2 + ux + v).
- u(int, int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
-
Gets the value of the grid point at (xk, yj).
- u(int, int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
-
Gets the value of the grid point at (tm, xn).
- u(int, int, int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the value of the grid point at (tk, xi, yj).
- U() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization
-
Gets U, where U' = Uk * ...
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
-
Gets U, where U' = Uk * ...
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
-
Get the upper triangular matrix, U, such that T * A = U and P * A = L * U.
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
-
Get the reduced row echelon form matrix, U, such that T * A = U.
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
- U() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the U matrix as in SVD decomposition.
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Returns the matrix U as in A=UDV'.
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
- U() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition
-
Get the upper triangular matrix U as in the LU decomposition.
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
-
Get the upper triangular matrix U, such that T * A = U.
- U() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets the accumulated Householder reflections applied to A.
- unconditionalMean() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Compute the multivariate ARMA unconditional mean.
- unconditionalMean() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
-
Compute the multivariate ARMA unconditional mean.
- unconstrainedFactory - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
-
a factory that defines the unconstrained Differential Evolution operators
- UnconstrainedLASSObyCoordinateDescent - Class in dev.nm.stat.regression.linear.lasso
-
This class solves the unconstrained form of LASSO, that is, \[ \min_w \left \{ \left \| Xw - y \right \|_2^2 + \lambda * \left \| w \right \|_1 \right \} \] by Coordinate Descent method.
- UnconstrainedLASSObyCoordinateDescent(UnconstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
-
Solves an unconstrained LASSO problem by Coordinate Descent method.
- UnconstrainedLASSObyQP - Class in dev.nm.stat.regression.linear.lasso
-
This class solves the unconstrained form of LASSO (i.e.
- UnconstrainedLASSObyQP(UnconstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP
-
Solves an unconstrained LASSO problem by transforming it into a single quadratic programming problem.
- UnconstrainedLASSOProblem - 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.
- UnconstrainedLASSOProblem(Vector, Matrix, double) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSOProblem
-
Constructs a LASSO problem.
- UnconstrainedLASSOProblem(UnconstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSOProblem
-
Copy constructor.
- UNDEFINED - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
-
an undefined label
- UNDEFINED - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
- unDi2DAGraph(UnDiGraph<V, ? extends UndirectedEdge<V>>, V) - Static method in class dev.nm.graph.GraphUtils
-
Converts an undirected graph into a directed acyclic graph, arcs are created from the edges by parent-child relations as determined by breadth-first-search.
- unDi2DAGraph(UnDiGraph<V, ? extends UndirectedEdge<V>>, V, GraphUtils.EdgeFactory<V, N, E, UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils
-
Converts an undirected graph into a directed acyclic graph, arcs are created from the edges by parent-child relations as determined by breadth-first-search.
- UnDiGraph<V,E extends UndirectedEdge<V>> - Interface in dev.nm.graph
-
An undirected graph is a graph, or set of nodes connected by edges, where an edge does not differentiate between (a, b) or (b, a).
- UndirectedEdge<V> - Interface in dev.nm.graph
-
A tagging interface for implementations of an undirected graph that accept only undirected edges.
- uniform - Variable in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
-
This is a uniform random number generator.
- uniform - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
-
the uniform random number generator
- uniform - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
- UNIFORM - dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
-
Uniform distribution.
- UNIFORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
- UNIFORM_WEIGHTING - Static variable in class dev.nm.analysis.curvefit.LeastSquares
-
A uniform weighting
- UniformDistributionOverBox - Class in dev.nm.stat.random.rng.multivariate
-
This random vector generator uniformly samples points over a box region.
- UniformDistributionOverBox(RealInterval...) - Constructor for class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox
-
Constructs a random vector generator to uniformly sample points over a box region.
- UniformDistributionOverBox(RandomLongGenerator, RealInterval...) - Constructor for class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox
-
Constructs a random vector generator to uniformly sample points over a box region.
- UniformDistributionOverBox1 - Class in dev.nm.solver.multivariate.initialization
-
This algorithm, by sampling uniformly in each dimension, generates a set of initials uniformly distributed over a box region, with some degree of irregularity or randomness.
- UniformDistributionOverBox1(RandomLongGenerator, int, RealInterval...) - Constructor for class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1
-
Construct a generator to uniformly sample points over a feasible region.
- UniformDistributionOverBox2 - Class in dev.nm.solver.multivariate.initialization
-
This algorithm, by perturbing each grid point by a small random scale, generates a set of initials uniformly distributed over a box region, with some degree of irregularity or randomness.
- UniformDistributionOverBox2(double, RealInterval[], int[], RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
-
Construct a generator to uniformly sample points over a feasible region.
- UniformDistributionOverBox2(double, RealInterval[], int, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
-
Construct a generator to uniformly sample points over a feasible region.
- UniformMeshOverRegion - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
-
The initial population is generated by putting a uniform mesh/grid/net over the entire region.
- UniformMeshOverRegion(RealScalarFunction, SimpleCellFactory, RandomLongGenerator, int, Vector[], double) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.UniformMeshOverRegion
-
Generate an initial pool of chromosomes by putting a uniform mesh/grid/net over the entire region.
- UniformRNG - Class in dev.nm.stat.random.rng.univariate.uniform
-
A pseudo uniform random number generator samples numbers from the unit interval, [0, 1], in such a way that there are equal probabilities of them falling in any same length sub-interval.
- UniformRNG() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
-
Construct a pseudo uniform random number generator.
- UniformRNG(long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
-
Construct a seeded pseudo uniform random number generator.
- UniformRNG(UniformRNG.Method) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
-
Construct a pseudo uniform random number generator.
- UniformRNG.Method - Enum in dev.nm.stat.random.rng.univariate.uniform
-
the pseudo uniform random number generators available
- Uniroot - Interface in dev.nm.analysis.root.univariate
-
A root-finding algorithm is a numerical algorithm for finding a value x such that f(x) = 0, for a given function f.
- UnitGrid - Class in dev.nm.stat.stochasticprocess.timegrid
-
This is the sequence of time points [0, 1, ..., T].
- UnitGrid() - Constructor for class dev.nm.stat.stochasticprocess.timegrid.UnitGrid
-
Construct a sequence of time points [0, 1, ..., ∞].
- UnitGrid(int) - Constructor for class dev.nm.stat.stochasticprocess.timegrid.UnitGrid
-
Construct a sequence of time points [0, 1, ..., T].
- unitRoundOff() - Static method in class dev.nm.misc.Constants
-
Get the default unit round off.
- unitRoundOff(int, int) - Static method in class dev.nm.misc.Constants
-
Get the unit round off as defined in the reference.
- UnivariateEVD - Interface in dev.nm.stat.evt.evd.univariate
-
Distribution of extreme values (e.g., maxima, minima, or other order statistics).
- UnivariateMinimizer - Interface in dev.nm.root.univariate
-
A univariate minimizer minimizes a univariate function.
- UnivariateMinimizer.Solution - Interface in dev.nm.root.univariate
-
This is the solution to a univariate minimization problem.
- UnivariateRealFunction - Interface in dev.nm.analysis.function.rn2r1.univariate
-
A univariate real function takes one real argument and outputs one real value.
- UnivariateTimeSeries<T extends Comparable<? super T>,E extends UnivariateTimeSeries.Entry<T>> - Interface in dev.nm.stat.timeseries.datastructure.univariate
-
This is a univariate time series indexed by some notion of time.
- UnivariateTimeSeries.Entry<T> - Class in dev.nm.stat.timeseries.datastructure.univariate
-
This is the
TimeSeries.Entry
for a univariate time series. - UnivariateTimeSeriesUtils - Class in dev.nm.stat.timeseries.datastructure.univariate
-
These are the utility functions to manipulate
a univariate time series
. - UnsatisfiableErrorCriterionException - Exception in dev.nm.analysis.differentialequation
-
An exception that is thrown when the error criterion cannot be met.
- UnsatisfiableErrorCriterionException() - Constructor for exception dev.nm.analysis.differentialequation.UnsatisfiableErrorCriterionException
- UnsatisfiableErrorCriterionException(String) - Constructor for exception dev.nm.analysis.differentialequation.UnsatisfiableErrorCriterionException
- UP - dev.nm.number.DoubleUtils.RoundingScheme
-
Always round up.
- UP - tech.nmfin.meanreversion.hvolatility.Kagi.Trend
- update(double) - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
- update(LocalDateTime, double) - Method in class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
- update(LocalDateTime, double) - Method in interface tech.nmfin.meanreversion.volarb.MeanPriceEstimator
-
Updates the model with the current price.
- update(LocalDateTime, double) - Method in interface tech.nmfin.meanreversion.volarb.MRModel
-
Updates the model with the current price.
- update(LocalDateTime, double) - Method in class tech.nmfin.meanreversion.volarb.MRModelRanged
- updateHessian(Vector, Vector, Vector, Vector, Matrix, Matrix) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
-
Update the Hessian matrix using the latest iterates.
- updateHessian(Vector, Vector, Vector, Vector, Matrix, Matrix) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
- updateHessian(Vector, Vector, Vector, Vector, Matrix, Matrix) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
- updateHessian(Vector, Vector, Vector, Vector, Vector, Matrix, Matrix, Matrix) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation
-
Update the Hessian matrix using the latest iterates.
- updateHessian(Vector, Vector, Vector, Vector, Vector, Matrix, Matrix, Matrix) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
-
Update the Hessian matrix using the latest iterates.
- updateHessianInverse(Matrix, Matrix, Matrix) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.DFPMinimizer
-
Sk+1 = Sk + δδ' / γ'δ - Sγγ'S' / γ'Sγ
- updateHessianInverse1(Matrix, Matrix, Matrix) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
-
Sk+1 = Sk + (1 + γ'Sγ/γ'δ)/γ'δ * δδ' -(δγ'S + Sγδ') / γ'δ, where S = H-1
- updateHessianInverse2(Matrix, Matrix, Matrix) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
-
P + γγ' / γ'δ - P %*% γγ' %*% P / γ'Pδ, where P = S-1 is the Hessian.
- updateStates() - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
Update the bracketing interval and the best min found so far.
- updateStates() - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
- upper() - Method in class dev.nm.interval.RealInterval
-
Get the upper bound of this interval.
- upper() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
- UPPER - dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
-
an upper bi-diagonal matrix, where there are only non-zero entries on the main and super diagonal
- UPPER - dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
-
diverge near x = b
- upperBound() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
-
Gets the upper bounds.
- upperBoundConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
Split the box constraints and get the less-than-the-upper-bounds part.
- UpperBoundConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
-
This is an upper bound constraints such that for all xi's, xi ≤ b
- UpperBoundConstraints(RealScalarFunction, double) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.UpperBoundConstraints
-
Construct an upper bound constraints for all variables in a function.
- upperBounds() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
-
Gets the upper bounds.
- UpperTriangularMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle
-
An upper triangular matrix has 0 entries where row index is greater than column index.
- UpperTriangularMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
Constructs an upper triangular matrix from a 2D
double[][]
array. - UpperTriangularMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
Constructs an upper triangular matrix of dimension dim * dim.
- UpperTriangularMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
Constructs an upper triangular matrix from a matrix.
- UpperTriangularMatrix(UpperTriangularMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
-
Copy constructor.
- Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
- Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
- Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
- Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
- Ut() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
-
Get the transpose of U, i.e.,
U().t()
. - Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
-
Returns the matrix U' as in A=UDV'.
V
- 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() - 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() - 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.
- 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.
- 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() - 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.WeightedMedian
- 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() - 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() - 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() - Method in class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
-
Get the expectation (the mean).
- value() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
- value(double) - Method in class dev.nm.stat.descriptive.rank.Quantile
-
Compute the sample value corresponding to a quantile.
- 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(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(double[], double[], double[]) - Method in class dev.nm.stat.regression.WeightedRSS
-
Computes the weighted RSS for a set of observations.
- 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(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
-
Integrate the function with respect to a given filtration.
- 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.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.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.
- valueOf(String) - Static method in enum tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
-
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() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
- 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.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.
- values() - Static method in enum tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
-
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
-
Deprecated.
Cheng1978
is a much better algorithm. - 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.
- 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.
- var - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
- var() - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
-
Gets the variance of the elements.
- 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() - 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.descriptive.correlation.CorrelationMatrix
-
Gets the variance of the i-th element.
- 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(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., \(E(x_{n+1} - \hat{x}_{n+1})^2\).
- 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() - 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() - 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
- 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 - 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[]) - Constructor for class dev.nm.stat.descriptive.moment.Variance
-
Construct an unbiased
Variance
calculator. - Variance(double[], boolean) - Constructor for class dev.nm.stat.descriptive.moment.Variance
-
Construct a
Variance
calculator, initialized with a sample. - Variance(Variance) - Constructor for class dev.nm.stat.descriptive.moment.Variance
-
Copy constructor.
- 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(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(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(Vector, Matrix[], int, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model with unit variance.
- VARIMAModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate ARIMA model.
- VARIMAModel(VARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Copy constructor.
- VARIMAModel(ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
-
Construct a multivariate model from a univariate ARIMA model.
- VARIMASim - Class in dev.nm.stat.timeseries.linear.multivariate.arima
-
This class simulates a multivariate ARIMA process.
- VARIMASim(VARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
-
Construct a multivariate ARIMA model, using random standard Gaussian innovations.
- 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.
- 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(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(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(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(Vector, Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
-
Construct a multivariate ARIMAX model.
- 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) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
-
Construct the linear representation of an ARMA model up to the default number of lags
VARLinearRepresentation.DEFAULT_NUMBER_OF_LAGS
. - VARLinearRepresentation(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
-
Construct the linear representation of an ARMA model.
- 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(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(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(Vector, Matrix[], Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model with unit variance.
- VARMAModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate ARMA model.
- VARMAModel(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Copy constructor.
- VARMAModel(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
-
Construct a multivariate model from a univariate ARMA model.
- 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(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(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(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(Vector, Matrix[], Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate ARMAX model.
- VARMAXModel(VARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Copy constructor.
- VARMAXModel(ARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
-
Construct a multivariate model from a univariate ARMAX model.
- VARModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a VAR model.
- 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(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model with zero-intercept (mu).
- VARModel(Vector, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model with unit variance.
- VARModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a VAR model.
- VARModel(VARModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Copy constructor.
- VARModel(ARModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
-
Construct a multivariate model from a univariate AR model.
- 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(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(Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model with zero-mean.
- VARXModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model with unit variance.
- VARXModel(Vector, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX model.
- VARXModel(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Copy constructor.
- 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(VECMTransitory) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
-
Construct a VARX(p) from a transitory VECM(p).
- VECM - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
-
A Vector Error Correction Model (VECM(p)) has one of the following specifications:
- 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(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(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(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(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(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
-
Construct a transitory VECM(p) model.
- 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
basedVector
. - VectorMathOperation() - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
- VectorMonitor - Class in dev.nm.misc.algorithm.iterative.monitor
-
This
IterationMonitor
stores all vectors generated during iterations. - 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
-
Gets the first visit time of this node.
- Viterbi - Class in dev.nm.stat.hmm
-
The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states - called the Viterbi path - that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models.
- Viterbi(HiddenMarkovModel) - Constructor for class dev.nm.stat.hmm.Viterbi
-
Constructs an Viterbi algorithm for an HMM.
- VMAInvertibility - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
-
The inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages.
- VMAInvertibility(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
Construct the inverse representation of an ARMA model up to the default number of lags
VMAInvertibility.DEFAULT_NLAGS
. - VMAInvertibility(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
-
Construct the inverse representation of an ARMA model.
- VMAModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
-
This class represents a multivariate MA model.
- 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(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model with zero-mean.
- VMAModel(Vector, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model with unit variance.
- VMAModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model.
- VMAModel(VMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Copy constructor.
- VMAModel(MAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
-
Construct a multivariate MA model from a univariate MA model.
- volatility() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
-
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
- Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
-
Gets the inverse (or transpose) of accumulated Householder right-reflections applied to A.
W
- W(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Gets W(t), the covariance matrix of wt.
- W(int) - Method in class dev.nm.stat.dlm.univariate.StateEquation
-
Get W(t), the variance of wt.
- W(Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
-
Compute W.
- w0 - Variable in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights
- wA() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Gets the weighted regressor matrix.
- WaveEquation1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1
-
A one-dimensional wave equation is a hyperbolic PDE that takes the following form.
- WaveEquation1D(double, double, double, UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
-
Constructs an one-dimensional wave equation.
- WaveEquation2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2
-
A two-dimensional wave equation is a hyperbolic PDE that takes the following form.
- WaveEquation2D(double, double, double, double, BivariateRealFunction, BivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
-
Create a two-dimensional wave equation.
- WeibullDistribution - Class in dev.nm.stat.distribution.univariate
-
The Weibull distribution interpolates between the exponential distribution k = 1 and the Rayleigh distribution (k = 2), where k is the shape parameter.
- WeibullDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.WeibullDistribution
-
Construct a Weibull distribution.
- WeibullRNG - Class in dev.nm.stat.random.rng.univariate
-
This random number generator samples from the Weibull distribution using the inverse transform sampling method.
- WeibullRNG() - Constructor for class dev.nm.stat.random.rng.univariate.WeibullRNG
-
Constructs a random number generator to sample from the Weibull distribution.
- WeibullRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.WeibullRNG
-
Constructs a random number generator to sample from the Weibull distribution.
- weight() - Method in class tech.nmfin.portfoliooptimization.clm.TurningPoint
- WeightedArc<V> - Interface in dev.nm.graph
-
A weighted arc is an arc that has a weight or a cost associated with it.
- WeightedEdge<V> - Interface in dev.nm.graph
-
A weighted edge has a weight or a cost associated with it.
- weightedFittedValues() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the weighted, fitted values.
- WeightedMean - Class in dev.nm.stat.descriptive.moment.weighted
-
The weighted mean is defined as \[ \bar{x} = \frac{ \sum_{i=1}^N w_i x_i}{\sum_{i=1}^N w_i} \]
- WeightedMean() - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedMean
- WeightedMean(double[], double[]) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedMean
- WeightedMedian - Class in dev.nm.stat.descriptive.moment.weighted
-
A weighted median of a sample is the 50% weighted percentile.It was first proposed by F.
- WeightedMedian(double[], double[]) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedMedian
-
Finds the weighted median of an array.
- weightedResiduals() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
-
Gets the weighted residuals.
- WeightedRSS - Class in dev.nm.stat.regression
-
Weighted sum of squared residuals (RSS) for a given function \(f(.)\) and observations \((x_i,y_i)\).
- WeightedRSS(UnivariateRealFunction) - Constructor for class dev.nm.stat.regression.WeightedRSS
-
Constructs a calculator to compute the weighted RSS for a given function.
- WeightedVariance - Class in dev.nm.stat.descriptive.moment.weighted
-
The weighted sample variance is defined as follows.
- WeightedVariance() - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
- WeightedVariance(boolean) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
- WeightedVariance(double[], double[]) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
- WeightedVariance(double[], double[], boolean) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
- weights - Variable in class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
-
the weights for the constraints
- weights() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
-
Gets the weights assigned to the observations.
- weights() - Method in class dev.nm.stat.regression.linear.glm.IWLS
- weights() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
- weights() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Gets the weights assigned to each observation.
- weights() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
- which(double[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils
-
Get the indices of the array elements which satisfy the
boolean
test. - which(int[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils
-
Get the indices of the array elements which satisfy the
boolean
test. - White - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
-
The White test tests for conditional heteroskedasticity.
- White(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.White
-
Perform the White test to test for heteroskedasticity in a linear regression model.
- WHITE - dev.nm.graph.algorithm.traversal.DFS.Node.Color
-
not seen
- WilcoxonRankSum - Class in dev.nm.stat.test.rank.wilcoxon
-
The Wilcoxon rank sum test tests for the equality of means of two populations, or whether the means differ by an offset.
- WilcoxonRankSum(double[], double[]) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations.
- WilcoxonRankSum(double[], double[], double) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations, or whether the means differ by an offset.
- WilcoxonRankSum(double[], double[], double, boolean) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations, or whether the means differ by an offset.
- WilcoxonRankSum(double[], double[], double, boolean, boolean) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
-
Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations, or whether the means differ by an offset.
- WilcoxonRankSumDistribution - Class in dev.nm.stat.test.rank.wilcoxon
-
Compute the exact distribution of the Wilcoxon rank sum test statistic.
- WilcoxonRankSumDistribution(int, int) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
-
Construct a Wilcoxon Rank Sum distribution for sample sizes
M
andN
. - WilcoxonSignedRank - Class in dev.nm.stat.test.rank.wilcoxon
-
The Wilcoxon signed rank test tests, for the one-sample case, the median of the distribution against a hypothetical median, and for the two-sample case, the equality of medians of groups.
- WilcoxonSignedRank(double[]) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
Perform the Wilcoxon Signed Rank test to test for the equality of medians.
- WilcoxonSignedRank(double[], double[]) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
Perform the Wilcoxon Signed Rank test to test for the equality of medians.
- WilcoxonSignedRank(double[], double[], double, boolean) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
Perform the Wilcoxon Signed Rank test to test for the equality of medians.
- WilcoxonSignedRank(double[], int) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
-
Perform the Wilcoxon Signed Rank test to test for the equality of medians.
- WilcoxonSignedRankDistribution - Class in dev.nm.stat.test.rank.wilcoxon
-
Compute the exact distribution of the Wilcoxon signed rank test statistic.
- WilcoxonSignedRankDistribution(int) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
-
Construct a Wilcoxon Signed Rank distribution for a sample size
N
. - withInitialGuess(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Overrides the initial guess of the solution.
- withLeftPreconditioner(Preconditioner) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Overrides the left preconditioner.
- withMaxIteration(int) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Overrides the maximum count of iterations.
- withNewLength(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
- withRightPreconditioner(Preconditioner) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Overrides the right preconditioner.
- withTolerance(Tolerance) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
-
Overrides the tolerance instance.
- Wt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
-
Get the current value(s) of the driving Brownian motion(s).
- Wt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
-
Get the current value of the driving Brownian motion.
- wy() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Gets the weighted response vector.
X
- x - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
-
This is the minimizer for the primal problem.
- x() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
- x() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
-
Get the values of the independent variable xi.
- x() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
- x() - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
-
Get a copy of the x's.
- x() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolution
-
Get the values of the independent variable.
- x() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
- x() - Method in interface dev.nm.analysis.function.tuple.OrderedPairs
-
Get the abscissae.
- x() - Method in class dev.nm.analysis.function.tuple.Pair
-
x
- x() - Method in class dev.nm.analysis.function.tuple.PartialFunction
- x() - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs
- x() - Method in class dev.nm.analysis.function.tuple.Triple
-
Return the value of the first element of the triple.
- x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
- x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
- x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
- x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
- x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
- x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
- x() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule
-
the transformation: x(t)
- x() - Method in exception dev.nm.analysis.root.univariate.NoRootFoundException
-
the best approximate root found so far
- x() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
-
Get the candidate solution.
- x() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState
-
Gets the position.
- x() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
-
Gets the current position.
- x(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
- x(int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
-
Get the value of xi, the i-th value of the independent variable x.
- x(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
- x(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
- x(int) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
-
Get all the values of the independent variable xi as an array.
- x(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
- x(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
-
Gets the value on the x-axis at index
k
. - x(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
-
Gets the value on the space axis at index
j
. - x(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the value on the x-axis at index
i
. - x(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
- x(int, int) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
-
Get the value of the independent variable xi at the given index.
- x(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
- X - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
-
This is the minimizer for the primal problem.
- X() - Method in interface dev.nm.stat.factor.pca.PCA
-
Gets the (possibly centered and/or scaled) data matrix X used for the PCA.
- X() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Gets the factor matrix.
- X() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
- x_F - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
-
transform zeta_list back from abscissa; it is same as whole_x_list; just follow the R code.
- x0() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
Get the value of x0, the first value of the independent variable x.
- x0() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
-
Get the start point of the integrating interval [x0, x1].
- x0() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Gets the start point of the integrating interval [x0, x1].
- x0(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
-
Get the value of \(\mathbf{x_i}_0\), the first value of the independent variable \(x_i\).
- x1() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
-
Get the end point of the integrating interval [x0, x1].
- x1() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Gets the end point of the integrating interval [x0, x1].
- xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
-
Gets the prediction at time t.
- xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
The next h-step ahead prediction.
- xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
-
Gets the h-step ahead prediction of the time series.
- xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Gets the one-step ahead prediction of the time series.
- xHat(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
-
Get the one-step prediction \(\hat{X}_{n+1} = P_{\mathfrak{S_n}}X_{n+1}\), made at time n.
- xHat(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
-
Get the one-step prediction \(\hat{X}_{n+1} = P_{\mathfrak{S_n}}X_{n+1}\), made at time n.
- xHat(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
-
Gets the one-step ahead prediction \(\hat{x}_{n+1}\).
- xi(HiddenMarkovModel, int[], ForwardBackwardProcedure) - Static method in class dev.nm.stat.hmm.discrete.BaumWelch
-
Gets the ξ matrices, where for 1 ≤ t ≤ T - 1, the t-th entry of ξ is an (N * N) matrix, for which the (i, j)-th entry is ξt(i, j).
- XiTanLiu2010a - Class in dev.nm.stat.random.rng.univariate.gamma
-
Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable.
- XiTanLiu2010a(double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a
-
Construct a random number generator to sample from the gamma distribution.
- XiTanLiu2010a(double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a
-
Construct a random number generator to sample from the gamma distribution.
- XiTanLiu2010b - Class in dev.nm.stat.random.rng.univariate.gamma
-
Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable.
- XiTanLiu2010b(double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b
-
Construct a random number generator to sample from the gamma distribution.
- XiTanLiu2010b(double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b
-
Construct a random number generator to sample from the gamma distribution.
- xl - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the lower bound of the bracketing interval
- XL2Norm() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Gets the L2 norms of the covariates (a vector of ones if no standardization is required).
- XLARS() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Gets the matrix of covariates (possibly demeaned and/or scaled) to be used in LARS.
- XMean() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Gets the mean vector to be subtracted from the covariates (a vector of zeros if no intercept is included).
- xmin - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the best minimizer found so far
- xmin - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
- xnext - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the next best guess of the minimizer
- xnext() - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
Compute the next best estimate within the bracketing interval.
- xnext() - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
- xnext() - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer.Solution
- xnext() - Method in class dev.nm.root.univariate.bracketsearch.GoldenMinimizer.Solution
- xShifted(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Gets the shifted time series of observations.
- xt(int, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
-
Evaluate the state equation without the control variable.
- xt(int, double, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
-
Evaluate the state equation.
- xt(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Evaluates the state equation without the control variable.
- xt(int, Vector, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Evaluates the state equation.
- Xt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the current value of the stochastic process.
- Xt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Get the current value of the stochastic process.
- xt_mean(int, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
-
Predict the next state without control variable.
- xt_mean(int, double, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
-
Predict the next state.
- xt_mean(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Predicts the next state without control variable.
- xt_mean(int, Vector, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Predicts the next state.
- xt_var(int, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
-
Get the variance of the apriori prediction for the next state.
- xt_var(int, Matrix) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
-
Gets the variance of the apriori prediction for the next state.
- XtAdaptedFunction - Class in dev.nm.stat.stochasticprocess.univariate.sde
-
This represents an Ft-adapted function that depends only on X(t).
- XtAdaptedFunction() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction
- xu - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
-
the upper bound of the bracketing interval
Y
- y - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
-
This is the maximizer for the dual problem.
- y - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
-
This is the maximizer for the dual problem.
- y() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
- y() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
-
Get the values of the independent variable yj.
- y() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
- y() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolution
-
Get the corresponding values of the dependent variable.
- y() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
- y() - Method in interface dev.nm.analysis.function.tuple.OrderedPairs
-
Get the ordinates.
- y() - Method in class dev.nm.analysis.function.tuple.Pair
-
y
- y() - Method in class dev.nm.analysis.function.tuple.PartialFunction
- y() - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs
- y() - Method in class dev.nm.analysis.function.tuple.Triple
-
Return the value of the second element of the triple.
- y() - Method in class dev.nm.stat.regression.linear.LMProblem
-
Gets the response vector, the regressands, the dependent variables.
- y(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
- y(int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
-
Get the value of yj, the j-th value of the independent variable y.
- y(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
- y(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
-
Gets the value on the y-axis at index
j
. - y(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
-
Get the value on the y-axis at index
j
. - y(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
-
Gets the stationary arma series.
- y(int...) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
- y(int...) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
-
Get the value of the dependent variable y at the given indices in the grid.
- y(int...) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
- y0() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
-
Get the value of y0, the first value of the independent variable y.
- y0() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
-
Gets the initial value of y, that is, y0.
- y0(int) - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
-
Get the initial value for the n-th order (derivative).
- yearMonthPanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
- yearPanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
- yes(double) - Method in interface dev.nm.number.DoubleUtils.ifelse
-
Return value for a
true
element of test. - yLARS() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Gets the vector of response variable (possibly demeaned) to be used in LARS.
- yMean() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
-
Gets the mean to be subtracted from the response variable (0 if no intercept is included).
- yt(int, double) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
-
Evaluate the observation equation.
- yt(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Evaluates the observation equation.
- yt_mean(int, double) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
-
Predict the next observation.
- yt_mean(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Predicts the next observation.
- yt_var(int, double) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
-
Get the variance of the apriori prediction for the next observation.
- yt_var(int, Matrix) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
-
Gets the covariance of the apriori prediction for the next observation.
Z
- z() - Method in class dev.nm.analysis.function.tuple.Triple
-
Return the value of the third element of the triple.
- z(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
- z(int, int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
-
Get the value of the dependent variable z at the given indices in the grid.
- z(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
- Z1() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
-
Get Z1.
- Z2() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
-
Get Z1.
- ZangwillImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer.ZangwillImpl
- ZangwillMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
-
Zangwill's algorithm is an improved version of Powell's algorithm.
- ZangwillMinimizer(double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer
-
Construct a multivariate minimizer using the Zangwill method.
- ZangwillMinimizer.ZangwillImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
-
an implementation of Zangwill's algorithm
- ZERO - Static variable in class dev.nm.analysis.function.polynomial.Polynomial
-
a polynomial representing 0
- ZERO - Static variable in class dev.nm.number.complex.Complex
-
a number representing 0.0 + 0.0i
- ZERO - Static variable in class dev.nm.number.Real
-
a number representing 0
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
- ZERO() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
-
Get a zero matrix that has the same dimension as this matrix.
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
-
Deprecated.no zero matrix for GivensMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
- ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
- ZERO() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
- ZERO() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
- ZERO() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
- ZERO() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
- ZERO() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
-
Get a 0-vector that has the same length as this vector.
- ZERO() - Method in interface dev.nm.algebra.structure.AbelianGroup
-
The additive element 0 in the group, such that for all elements a in the group, the equation 0 + a = a + 0 = a holds.
- ZERO() - Method in class dev.nm.analysis.function.polynomial.Polynomial
- ZERO() - Method in class dev.nm.number.complex.Complex
-
Get zero - the number representing 0.0 + 0.0i.
- ZERO() - Method in class dev.nm.number.Real
- ZERO_ENTRY - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
- ZeroDriftVector - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
-
This class represents a 0 drift function.
- ZeroDriftVector() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ZeroDriftVector
- ZeroPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
-
This is a dummy zero cost (no cost) penalty function.
- ZeroPenalty(int) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.ZeroPenalty
-
Construct a no-cost penalty function.
- Ziggurat2000 - Class in dev.nm.stat.random.rng.univariate.normal
-
The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution.
- Ziggurat2000() - Constructor for class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000
-
Construct a Ziggurat random normal generator.
- Ziggurat2000(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000
-
Construct a Ziggurat random normal generator.
- Ziggurat2000Exp - Class in dev.nm.stat.random.rng.univariate.exp
-
This implements the ziggurat algorithm to sample from the exponential distribution.
- Ziggurat2000Exp() - Constructor for class dev.nm.stat.random.rng.univariate.exp.Ziggurat2000Exp
- Zignor2005 - Class in dev.nm.stat.random.rng.univariate.normal
-
This is an improved version of the Ziggurat algorithm as proposed in the reference.
- Zignor2005() - Constructor for class dev.nm.stat.random.rng.univariate.normal.Zignor2005
-
Construct an improved Ziggurat random normal generator.
- Zignor2005(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.Zignor2005
-
Construct an improved Ziggurat random normal generator.
- Zt() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
-
Get a d-dimensional Gaussian innovation.
- Zt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
-
Get the current value of the Gaussian innovation.
- Zt() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
-
Get a Gaussian innovation.
- Zt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
-
Get the current value of the Gaussian innovation.
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