# SuanShu, a Java numerical and statistical library

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

## A

A() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the homogeneous part, the coefficient matrix, of the linear system.
a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
Gets the width (x-axis) of the rectangular region.
a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
Gets the size of the one-dimensional space, that is, the range of x, (0 < x < a).
a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Get the size of the two-dimensional space along the x-axis, that is, the range of x, (0 < x < a).
a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the size of the one-dimensional space, that is, the range of x, (0 < x < a).
a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets the size of the one-dimensional space, that is, the range of x, (0 < x < a).
a() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Gets the size of the two-dimensional space along the x-axis, that is, the range of x, (0 < x < a).
a() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.QuadraticSyntheticDivision
Get a as in the remainder (b * (x + u) + a).
A(int, double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
Compute an.
a() - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
Get a.
A() - Method in class com.numericalmethod.suanshu.model.elliott2005.Elliott2005DLM
Gets A as in eq.
A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
Get the constraint coefficients.
A - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
This is either [A] or [ A] [-C]
A(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem
Gets Ai.
A(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
Gets Ai.
A(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Gets Ai.
A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
$A = [A_1, A_2, ... A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint Gets A. A() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem Get the coefficients, A, of the greater-than-or-equal-to constraints A * x ≥ b. A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1 A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem Get the coefficients of the inequality constraints: A as in $$Ax \geq b$$. a - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1 A() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1 a() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter A() - Method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC Gets the state transition probabilities. A() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators Gets the estimated sequence of A. A() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem Gets the regressor matrix. A - Variable in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159.RandomMatrix a random matrix constructed by AS159 a0() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model Gets the constant coefficient. a0() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel Get the constant term. a1() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model Gets the ARCH coefficient. AbelianGroup<G> - Interface in com.numericalmethod.suanshu.algebra.structure An Abelian group is a group with a binary additive operation (+), satisfying the group axioms: closure associativity existence of additive identity existence of additive opposite commutativity of addition ABMPredictorCorrector - Interface in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton The Adams-Bashforth predictor and the Adams-Moulton corrector pair. ABMPredictorCorrector1 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton The Adams-Bashforth predictor and the Adams-Moulton corrector of order 1. ABMPredictorCorrector1() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1 ABMPredictorCorrector2 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton The Adams-Bashforth predictor and the Adams-Moulton corrector of order 2. ABMPredictorCorrector2() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2 ABMPredictorCorrector3 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton The Adams-Bashforth predictor and the Adams-Moulton corrector of order 3. ABMPredictorCorrector3() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3 ABMPredictorCorrector4 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton The Adams-Bashforth predictor and the Adams-Moulton corrector of order 4. ABMPredictorCorrector4() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4 ABMPredictorCorrector5 - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton The Adams-Bashforth predictor and the Adams-Moulton corrector of order 5. ABMPredictorCorrector5() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5 abs(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation Computes the absolute values of a vector, element-by-element. abs(double[]) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath Get the absolute values. ABSOLUTE_ZERO_T0 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants The absolute zero temperature in Celsius (°C). absoluteError(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils Compute the absolute difference between x1 and x0. AbsoluteErrorPenalty - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod This penalty function sums up the absolute error penalties. AbsoluteErrorPenalty(EqualityConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty Construct an absolute error penalty function from a collection of equality constraints. AbsoluteErrorPenalty(EqualityConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty Construct an absolute error penalty function from a collection of equality constraints. AbsoluteErrorPenalty(EqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty Construct an absolute error penalty function from a collection of equality constraints. AbsoluteTolerance - Class in com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance The stopping criteria is that the norm of the residual r is equal to or smaller than the specified tolerance, that is, ||r||2 ≤ tolerance AbsoluteTolerance() - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.AbsoluteTolerance AbsoluteTolerance(double) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.AbsoluteTolerance Construct an instance with specified tolerance. AbstractBivariateEVD - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate AbstractBivariateEVD() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.AbstractBivariateEVD AbstractBivariateProbabilityDistribution - Class in com.numericalmethod.suanshu.stats.distribution.multivariate AbstractBivariateProbabilityDistribution() - Constructor for class com.numericalmethod.suanshu.stats.distribution.multivariate.AbstractBivariateProbabilityDistribution AbstractBivariateRealFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1 A bivariate real function takes two real arguments and outputs one real value. AbstractBivariateRealFunction() - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractBivariateRealFunction AbstractGridExecutor - Class in com.numericalmethod.suanshu.grid.executor Provides basic default implementations of GridExecutor functions on top of the map operation. AbstractGridExecutor() - Constructor for class com.numericalmethod.suanshu.grid.executor.AbstractGridExecutor AbstractHybridMCMC - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid Hybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional Metropolis algorithm, with molecular dynamics simulation. AbstractHybridMCMC(Vector, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC Constructs a new instance with the given parameters. AbstractMetropolis - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis The Metropolis algorithm is a Markov Chain Monte Carlo algorithm, which requires only a function f proportional to the PDF from which we wish to sample. AbstractMetropolis(Vector, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis Constructs a new instance with the given parameters. AbstractR1RnFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2rm This is a function that takes one real argument and outputs one vector value. AbstractR1RnFunction(int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2rm.AbstractR1RnFunction AbstractRealScalarFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1 This abstract implementation implements Function.dimensionOfRange() by always returning 1, and Function.dimensionOfDomain() by returning the input argument for the dimension of domain. AbstractRealScalarFunction(int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractRealScalarFunction Construct an instance with the dimension of the domain. AbstractRealVectorFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2rm This abstract implementation implements Function.dimensionOfDomain() and Function.dimensionOfRange() by returning the input arguments at constructor. AbstractRealVectorFunction(int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2rm.AbstractRealVectorFunction AbstractTrivariateRealFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1 A trivariate real function takes three real arguments and outputs one real value. AbstractTrivariateRealFunction() - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractTrivariateRealFunction AbstractUnivariateRealFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate A univariate real function takes one real argument and outputs one real value. AbstractUnivariateRealFunction() - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction acceptanceProbability(Vector, double, Vector, double, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.BoxGSAAcceptanceProbabilityFunction acceptanceProbability(Vector, double, Vector, double, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.GSAAcceptanceProbabilityFunction acceptanceProbability(Vector, double, Vector, double, double) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.MetropolisAcceptanceProbabilityFunction acceptanceProbability(Vector, double, Vector, double, double) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.TemperedAcceptanceProbabilityFunction Computes the probability that the next state transition will be accepted. acceptanceRate() - Method in class com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis Gets the acceptance rate, i.e. acceptanceTemperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction acceptanceTemperature(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction acceptanceTemperature(int) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.TemperatureFunction Gets the acceptance temperature $$T^A_t$$ at time t. ACERAnalysis - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer Average Conditional Exceedance Rate (ACER) method is for estimating the cdf of the maxima $$M$$ distribution from observations. ACERAnalysis() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis Create an instance with the default values. ACERAnalysis(int, int, double, boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERAnalysis Create an instance with various options listed below. ACERAnalysis.Result - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer ACERByCounting - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical Estimate epsilons by counting conditional exceedances from the observations. ACERByCounting(double[], int) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.ACERByCounting Create an instance for estimating epsilon for each of the given barrier levels. ACERConfidenceInterval - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer Using the given (estimated) ACER function as the mean, find the ACER parameters at the lower and upper bounds of the estimated confidence interval of ACER values. ACERConfidenceInterval(ACERFunction.ACERParameter, EmpiricalACER, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERConfidenceInterval ACERFunction - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer The ACER (Average Conditional Exceedance Rate) function $$\epsilon_k(\eta)$$ approximates the probability \[ \epsilon_k(\eta) = Pr(X_k > \eta | X_1 \le \eta, X_2 \le \eta, ..., X_{k-1} \le \eta)$ for a sequence of stochastic process observations $$X_i$$ with a k-step memory.
ACERFunction(ACERFunction.ACERParameter) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction

ACERFunction.ACERParameter - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
ACERInverseFunction - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
The inverse of the ACER function.
ACERInverseFunction(ACERFunction.ACERParameter) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERInverseFunction

ACERLogFunction - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
The ACER function in log scale (base e), i.e., $$log(\epsilon_k(\eta))$$.
ACERLogFunction(ACERFunction.ACERParameter) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERLogFunction
Create an instance with the ACER function parameter.
ACERParameter(double[]) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
Create an instance with a double[] which contains q, b, a, c.
ACERParameter(double, double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter

ACERReturnLevel - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer
Given an ACER function, compute the return level $$\eta$$ for a given return period $$R$$.
ACERReturnLevel(ACERFunction.ACERParameter, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERReturnLevel
Create an instance with the (estimated) ACER function parameter and the total number of events.
ACERUtils - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical
Utility functions used in ACER empirical analysis.
ACERUtils() - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.ACERUtils

acos(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
Inverse of cosine.
acosh(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc hyperbolic cosine of a value; the returned hyperbolic angle is positive.
acot(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc cotangent of a value; the returned angle is in the range -pi/2 through pi/2.
acot2(double, double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
acoth(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc hyperbolic cotangent of a value.
acovers(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc coversine of a value; the returned angle is in the range -pi/2 through pi/2.
acsc(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc cosecant of a value; the returned angle is in the range -pi/2 through pi/2.
acsch(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc hyperbolic cosecant of a value.
actions() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators
Gets the the sequence of actions taken.
ActiveList - Interface in com.numericalmethod.suanshu.misc.algorithm.bb
This interface defines the node popping strategy used in a branch-and-bound algorithm, e.g., depth-first-search, best-first-search.
ActiveSet - Class in com.numericalmethod.suanshu.misc.algorithm
This class keeps track of the active and inactive indices.
ActiveSet(boolean) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
Construct a working set of active/inactive indices.
ActiveSet(boolean, Collection<Integer>) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
Construct a working set of active/inactive indices.
ActiveSet(boolean, int[]) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
Construct a working set of active/inactive indices.
activeSize() - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
Get the number of active indices.
ACTOR - Static variable in class com.numericalmethod.suanshu.grid.executor.remote.akka.Kernel
The name of the slave actor in the system on this kernel.
ActorProps - Class in com.numericalmethod.suanshu.grid.executor.remote.akka
Static factory class that contains all of the common Props, to make the code that uses them more readable.
This class uses an Adams-Bashford predictor and an Adams-Moulton corrector of the specified order.
Create a new instance of the Adams-Bashforth-Moulton method using the given predictor-corrector pair.
Create a new instance of the Adams-Bashforth-Moulton method using the given predictor-corrector pair and the given ODE integrator.
add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix

add(Matrix) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixRing
this + that
add(DenseData) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Add up the elements in this and that, element-by-element.
add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Computes the sum of two diagonal matrices.
add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

add(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation

add(MatrixAccess, MatrixAccess) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
A1 + A2
add(MatrixAccess, MatrixAccess) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

add(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

add(SparseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

add(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

add(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef

add(ComplexMatrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

add(GenericFieldMatrix<F>) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

add(RealMatrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.RealMatrix

add(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector

add(DenseVector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector

add(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector

add(Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
add(Vector, double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
Adds a constant to a vector, element-by-element.
add(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector

add(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector

add(double) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef

add(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
$$this + that$$
add(double) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
Add a constant to all entries in this vector.
add(G) - Method in interface com.numericalmethod.suanshu.algebra.structure.AbelianGroup
+ : G × G → G
add(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial

add(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.StepFunction
Dynamically add points to the step function.
add(double) - Method in class com.numericalmethod.suanshu.combinatorics.Counter
Add a number to the counter.
add(double...) - Method in class com.numericalmethod.suanshu.combinatorics.Counter
add(Interval<T>) - Method in class com.numericalmethod.suanshu.interval.Intervals
Add an interval to the set.
add(Interval<T>...) - Method in class com.numericalmethod.suanshu.interval.Intervals
add(BBNode) - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.ActiveList
Add a node to the active list.
add(double, T) - Method in class com.numericalmethod.suanshu.misc.algorithm.Bins
Add a valued item to the bin.
add(T) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet

add(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex

add(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation

add(double[], double) - Static method in class com.numericalmethod.suanshu.number.doublearray.DoubleArrayMath
Add a double value to each element in an array.
add(double[], double[]) - Method in interface com.numericalmethod.suanshu.number.doublearray.DoubleArrayOperation
Add two double arrays, entry-by-entry.
add(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.ParallelDoubleArrayOperation

add(double[], double[]) - Method in class com.numericalmethod.suanshu.number.doublearray.SimpleDoubleArrayOperation

add(Real) - Method in class com.numericalmethod.suanshu.number.Real

add(SOCPGeneralConstraint) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
addActive(Collection<Integer>) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
addActive(int[]) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
addActive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
Add an active constraint by index.
addAll(Collection<? extends T>) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet

addColAt(int, Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
addColAt(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
addColumn(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
Column addition: A[, j1] = A[, j1] + c * A[, j2]
addData(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.LinearInterpolator

addData(OrderedPairs) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.NevilleTable

addData(OrderedPairs) - Method in interface com.numericalmethod.suanshu.analysis.curvefit.interpolation.OnlineInterpolator
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.KendallRankCorrelation
Update the statistic with more data.
addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.KendallRankCorrelation
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.SpearmanRankCorrelation
Update the statistic with more data.
addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.correlation.SpearmanRankCorrelation
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
Update the covariance statistic with more data.
addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
Update the covariance statistic with more data.
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis

addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean

addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments

addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness

addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance

addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedMean

addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedMean

addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedVariance

addData(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.weighted.WeightedVariance

addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max

addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min

addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile

addData(double...) - Method in interface com.numericalmethod.suanshu.stats.descriptive.Statistic
Recompute the statistic with more data, incrementally if possible.
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.SynchronizedStatistic

addEdge(E) - Method in interface com.numericalmethod.suanshu.graph.Graph
Adds an edge to this graph.
addEdge(E) - Method in class com.numericalmethod.suanshu.graph.type.SparseDAGraph

addEdge(E) - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph

addEdge(Arc<V>) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree
Add an edge to the tree, connecting v1, the parent and v2..., the children.
addEdge(Arc<VertexTree<T>>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree

addEdges(Graph<V, E>, E...) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
Add a set of edges to a graph.
addFactor(int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.GLMModelSelection
addInactive(Collection<Integer>) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
addInactive(int[]) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
addInactive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
Add an inactive constraint by index.
addIterate(S) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.CountMonitor

addIterate(S) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.IteratesMonitor

addIterate(S) - Method in interface com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.IterationMonitor
Record a new iteration state.
addIterate(S) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.NullMonitor

addIterate(Vector) - Method in class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.VectorMonitor

The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
Construct a univariate time series by adding up the components.
Construct a univariate time series by adding up the components.
addRow(int, int, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ElementaryOperation
Row addition: A[i1, ] = A[i1, ] + c * A[i2, ]
addRow(double, double[]) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
Adds a row to the table.
addRow(String, String, double...) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
Inserts a row of data into the panel.
addRow(PanelData.Row) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData
Inserts a row of data into the panel.
addRowAt(int, Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
addRowAt(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
addRows(double[][]) - Method in class com.numericalmethod.suanshu.misc.datastructure.MathTable
Adds rows by a double[][].
addVertex(V) - Method in interface com.numericalmethod.suanshu.graph.Graph
Adds a vertex to this graph.
addVertex(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph

addVertex(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree

addVertex(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree

addVertices(Graph<V, ?>, V...) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
Add a set of vertices to a graph.
This class computes the asymptotic distribution of the Augmented Dickey-Fuller (ADF) test statistic.
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
Deprecated.
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
Deprecated.
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
Deprecated.
the available types of Dickey-Fuller tests
This represents an Augmented Dickey Fuller distribution.
A table contains the simulated observations/values of an empirical ADF distribution for a given set of parameters.

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.

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.

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.

This class computes the finite sample distribution of the Augmented Dickey-Fuller (ADF) test statistics.
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
Construct a finite sample distribution for the original Dickey-Fuller test statistic.
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
Aeq() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Get the coefficients, Aeq, of the equality constraints Aeq * x ≥ beq.
Aeq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

Aeq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Get the coefficients of the equality constraints: Aeq as in $$A_{eq}x = b_{eq}$$.
Aeq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1

aexsec(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc exsecant of a value; the returned angle is in the range 0.0 through pi.
AfterIterations - Class in com.numericalmethod.suanshu.misc.algorithm.stopcondition
Stops after a given number of iterations.
AfterIterations(int) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AfterIterations
Stops after a given number of iterations.
AfterNoImprovement - Class in com.numericalmethod.suanshu.misc.algorithm.stopcondition

AfterNoImprovement(int) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AfterNoImprovement

AhatEstimation - Class in com.numericalmethod.suanshu.model.daspremont2008
Estimates the coefficient of a VAR(1) model by penalized maximum likelihood.
AhatEstimation(Matrix, Matrix, double) - Constructor for class com.numericalmethod.suanshu.model.daspremont2008.AhatEstimation
Estimates the coefficient matrix of a vector autoregressive process of order 1.
ahav(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc haversine of a value; the returned angle is in the range 0 to pi.
AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial

AIC(Vector, Vector, Vector, double, double, int) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMExponentialDistribution
AIC = 2 * #param - 2 * log-likelihood
AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma

AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian

AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian

AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson

AIC() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
Gets the Akaike information criterion (AIC).
AIC() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression
Gets the AIC.
AIC() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMInformationCriteria
Gets the Akaike information criterion.
AIC() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
Compute the AIC of fitted model.
AIC() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Compute the AIC, a model selection criterion.
AICC() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
Compute the AICC of fitted model.
AICC() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Compute the AICC, a model selection criterion.
ak - Variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
the increment in the search direction
AkkaGridExecutor - Class in com.numericalmethod.suanshu.grid.executor.remote.akka
Uses Akka to distribute the computational load between multiple machines.
AkkaGridExecutor(RemoteConfiguration) - Constructor for class com.numericalmethod.suanshu.grid.executor.remote.akka.AkkaGridExecutor
Creates a new instance from the given configuration, using the provided router.
AkkaGridExecutor(RemoteConfiguration, boolean) - Constructor for class com.numericalmethod.suanshu.grid.executor.remote.akka.AkkaGridExecutor
Creates a new instance from the given configuration, using the provided router.
AkkaGridExecutorFactory - Class in com.numericalmethod.suanshu.grid.executor.remote.akka
Creates instances of GridExecutorFactory that use Akka's remoting to distribute computation, from a configuration object.
AkkaGridExecutorFactory(RemoteConfiguration) - Constructor for class com.numericalmethod.suanshu.grid.executor.remote.akka.AkkaGridExecutorFactory
Creates a new instance which uses the hosts in the given document to configure instances.
AkkaUtils - Class in com.numericalmethod.suanshu.grid.executor.remote.akka
Utility methods for Akka.
algebraicMultiplicity() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
Get the multiplicity of the eigenvalue (a root) of the characteristic polynomial.
allForecasts() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
Gets all the predictions of the next h steps in one vector.
allForecasts() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Gets all the predictions of the next h steps in one vector.
AllIntegers() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers

allMSEs() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
Gets all the mean squared errors (MSE) of the h-step ahead predictions.
allMSEs() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Gets all the mean squared errors (MSE) of the h-step ahead predictions.
alpha(Vector, Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
Get the percentage increment along the minimizer increment direction.
alpha(Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1

alpha(Vector, Vector, Vector, Vector) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPASVariation
Get the percentage increment along the minimizer increment direction.
alpha(Vector, Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.SQPASVariation1
Get the percentage increment along the minimizer increment direction.
alpha() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
Get the set of adjusting coefficients, by columns.
alpha - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
α: the shape parameter
alpha() - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.FixedEffectsModel
Gets the individual/subject specific terms.
alpha(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarlingPValue
Gets the p-value for a test statistic.
alpha() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
Gets the ARCH coefficient.
alpha() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Get the ARCH coefficients.
alpha0 - Variable in class com.numericalmethod.suanshu.model.kst1995.KnightSatchellTran1995

alpha1 - Variable in class com.numericalmethod.suanshu.model.kst1995.KnightSatchellTran1995

AlternatingDirectionImplicitMethod - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2
Alternating direction implicit (ADI) method is an implicit method for obtaining numerical approximations to the solution of a HeatEquation2D.
AlternatingDirectionImplicitMethod(double) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod
Create an ADI method with the given precision parameter.
AlternatingDirectionImplicitMethod(double, boolean) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod
Create an ADI method with the given precision parameter, and choice for using multi-core parallel computation for higher performance.
AndersonDarling - Class in com.numericalmethod.suanshu.stats.test.distribution
This algorithm calculates the Anderson-Darling k-sample test statistics and p-values.
AndersonDarling(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarling
Runs the Anderson-Darling test.
AndersonDarlingPValue - Class in com.numericalmethod.suanshu.stats.test.distribution
This algorithm calculates the p-value when the Anderson-Darling statistic and the number of samples are given.
AndersonDarlingPValue(int) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.AndersonDarlingPValue
Construct the Anderson-Darling distribution for a particular number of samples.
AndStopConditions - Class in com.numericalmethod.suanshu.misc.algorithm.stopcondition
Combines an arbitrary number of stop conditions, terminating when all conditions are met.
AndStopConditions(StopCondition...) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AndStopConditions

angle(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

angle(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector

angle(Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the angle between two vectors.
angle(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector

angle(Vector) - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector
Measure the angle, $$\theta$$, between this and that.
angle(H) - Method in interface com.numericalmethod.suanshu.algebra.structure.HilbertSpace
∠ : H × H → F

Inner product formalizes the geometrical notions such as the length of a vector and the angle between two vectors.

angle(double, double, double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the angle $$\alpha$$ opposite the side a, given the three side-lengths of the triangle.
angle(Pair, Pair, Pair) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Given a the coordinates of A, B and C, the apices of triangle ABC, returns the value of the angle $$alpha$$ at apex A.
AnnealingFunction - Interface in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction
An annealing function or a tempered proposal function gives the next proposal/state from the current state and temperature.
AntitheticVariates - Class in com.numericalmethod.suanshu.stats.random.variancereduction
The antithetic variates technique consists, for every sample path obtained, in taking its antithetic path - that is given a path $$\varepsilon_1,\dots,\varepsilon_M$$ to also take, for example, $$-\varepsilon_1,\dots,-\varepsilon_M$$ or $$1-\varepsilon_1,\dots,1-\varepsilon_M$$.
AntitheticVariates(UnivariateRealFunction, RandomNumberGenerator, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.AntitheticVariates
Estimate $$E(f(X_1))$$, where f is a function of a random variable.
AntitheticVariates(UnivariateRealFunction, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.AntitheticVariates
Estimate $$E(f(X_1))$$ and use AntitheticVariates.INVERSE as the default antithetic path.
AntoniouLu2007 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint
This implementation is based on Algorithm 14.5 in the reference.
APERY - Static variable in class com.numericalmethod.suanshu.misc.Constants
the Apery's constant
AR() - Method in class com.numericalmethod.suanshu.stats.evt.timeseries.MARMAModel
Get the AR coefficients.
AR(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel
Get the i-th AR coefficient; AR(0) = 1.
AR(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
Get the i-th AR coefficient; AR(0) = 1.
AR1GARCH11Model - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch
An AR1-GARCH11 model takes this form.
AR1GARCH11Model(double, double, double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model

AR2() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
Gets the diagnostic measure: adjusted R-squared
Arc<V> - Interface in com.numericalmethod.suanshu.graph
An arc is an ordered pair of vertices.
areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact
Checks whether all SOCP constraints represented by this portfolio constraint are satisfied.
areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
Checks whether all SOCP constraints represented by this portfolio constraint are satisfied.
areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
Checks whether all SOCP constraints represented by this portfolio constraint are satisfied.
areAllConstraintsSatisfied(Vector) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
Checks whether the constraints are satisfied with a solution vector x.
areAllSparse(Matrix...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if all matrices are SparseMatrix.
areAllSparse(Vector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if all vectors are SparseVector.
areEqual(Matrix, Matrix, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks the equality of two matrices up to a precision.
areEqual(Vector, Vector, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if two vectors are equal, i.e., v1 - v2 is a zero vector, up to a precision.
areOrthogonal(Vector, Vector, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if two vectors are orthogonal, i.e., v1 âˆ™ v2 == 0.
areOrthogonal(Vector[], double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a set of vectors are orthogonal, i.e., for any v1, v2 in v, v1 âˆ™ v2 == 0.
areOrthogonormal(Vector, Vector, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if two vectors are orthogonormal.
areOrthogonormal(Vector[], double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a set of vectors are orthogonormal.
arg() - Method in class com.numericalmethod.suanshu.number.complex.Complex
Get the θ of the complex number in polar representation.
ArgumentAssertion - Class in com.numericalmethod.suanshu.misc
Utility class for checking numerical arguments.
ARIMAForecast - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
Forecasts an ARIMA time series using the innovative algorithm.
ARIMAForecast(IntTimeTimeSeries, ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast
Constructs a forecaster for a time series assuming ARIMA model.
ARIMAForecast(IntTimeTimeSeries, int, int, int, double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecast
Constructs a forecaster for a time series assuming ARIMA model.
ARIMAForecast.Forecast - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
The forecast value and variance.
ARIMAForecastMultiStep - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
Makes forecasts for a time series assuming an ARIMA model using the innovative algorithm.
ARIMAForecastMultiStep(IntTimeTimeSeries, ARIMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
Makes the h-step ahead prediction for an ARIMA model.
ARIMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
An ARIMA(p, d, q) process, Xt, is such that $(1 - B)^d X_t = Y_t$ where B is the backward or lag operator, d the order of difference, Yt an ARMA(p, q) process, for which $Y_t = \mu + \Sigma \phi_i Y_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,$
ARIMAModel(double, double[], int, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
Construct a univariate ARIMA model.
ARIMAModel(double, double[], int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
Construct a univariate ARIMA model with unit variance.
ARIMAModel(double[], int, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
Construct a univariate ARIMA model with zero-intercept (mu).
ARIMAModel(double[], int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
Construct a univariate ARIMA model with unit variance and zero-intercept (mu).
ARIMAModel(ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAModel
Copy constructor.
ARIMASim - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
This class simulates an ARIMA (AutoRegressive Integrated Moving Average) process.
ARIMASim(ARIMAModel, double[], double[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
Construct an ARIMA model.
ARIMASim(ARIMAModel, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
Construct an ARIMA model.
ARIMASim(ARIMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMASim
Construct an ARIMA model, using random standard Gaussian innovations.
ARIMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables.
ARIMAXModel(double, double[], int, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
Construct a univariate ARIMAX model.
ARIMAXModel(double, double[], int, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
Construct a univariate ARIMAX model with unit variance.
ARIMAXModel(double[], int, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
Construct a univariate ARIMAX model with zero-intercept (mu).
ARIMAXModel(double[], int, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
Construct a univariate ARIMAX model with unit variance and zero-intercept (mu).
ARIMAXModel(ARIMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel
Copy constructor.
ARMAFit - Interface in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
This interface represents a fitting method for estimating φ, θ, μ, σ2 in an ARMA model.
ARMAForecast - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
Forecasts an ARMA time series using the innovative algorithm.
ARMAForecast(IntTimeTimeSeries, ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
Constructs a forecaster for a time series assuming ARMA model.
ARMAForecast(IntTimeTimeSeries, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
Constructs a forecaster for a time series assuming ARMA model.
ARMAForecastMultiStep - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
Computes the h-step ahead prediction of a causal ARMA model, by the innovative algorithm.
ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int, InnovationsAlgorithm, ARMAForecastOneStep) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the h-step ahead prediction for an ARMA model.
ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int, InnovationsAlgorithm) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the h-step ahead prediction for an ARMA model.
ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the h-step ahead prediction for an ARMA model.
ARMAForecastMultiStep(double[], ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the h-step ahead prediction for an ARMA model.
ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the one-step ahead prediction for an ARMA model.
ARMAForecastOneStep - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
Computes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm.
ARMAForecastOneStep(IntTimeTimeSeries, ARMAModel, InnovationsAlgorithm) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Makes the one-step ahead prediction for an ARMA model.
ARMAForecastOneStep(IntTimeTimeSeries, ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Makes the one-step ahead prediction for an ARMA model.
ARMAForecastOneStep(double[], ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Makes the one-step ahead prediction for an ARMA model.
ARMAGARCHFit - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch
This implementation fits, for a data set, an ARMA-GARCH model by Quasi-Maximum Likelihood Estimation.
ARMAGARCHFit(double[], int, int, int, int, double, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
Constructs a model.
ARMAGARCHFit(double[], int, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
Constructs a model with the default tolerance and maximum number of iterations.
ARMAGARCHModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch
An ARMA-GARCH model takes this form: $X_t = \mu + \sum_{i=1}^p \phi_i X_{t-i} + \sum_{i=1}^q \theta_j \epsilon_{t-j} + \epsilon_t, \\ \epsilon_t = \sqrt{h_t\eta_t}, \\ h_t = \alpha_0 + \sum_{i=1}^{r} (\alpha_i e_{t-i}^2) + \sum_{i=1}^{s} (\beta_i h_{t-i})$
ARMAGARCHModel(ARMAModel, GARCHModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
Construct a univariate ARMA-GARCH model.
ARMAModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
A univariate ARMA model, Xt, takes this form.
ARMAModel(double, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Construct a univariate ARMA model.
ARMAModel(double, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Construct a univariate ARMA model with unit variance.
ARMAModel(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Construct a univariate ARMA model with zero-intercept (mu).
ARMAModel(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Construct a univariate ARMA model with unit variance and zero-intercept (mu).
ARMAModel(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Copy constructor.
armaxMean(Matrix, Matrix, Vector) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
Compute the multivariate ARMAX conditional mean.
armaxMean(double[], double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Compute the univariate ARMAX conditional mean.
ARMAXModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
ARMAXModel(double, double[], double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Construct a univariate ARMAX model.
ARMAXModel(double, double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Construct a univariate ARMAX model with unit variance.
ARMAXModel(double[], double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Construct a univariate ARMAX model with zero-intercept (mu).
ARMAXModel(double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Construct a univariate ARMAX model with unit variance and zero-intercept (mu).
ARMAXModel(ARMAXModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Copy constructor.
ARModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
This class represents an AR model.
ARModel(double, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Construct a univariate AR model.
ARModel(double, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Construct a univariate AR model with unit variance.
ARModel(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Construct a univariate AR model with zero-intercept (mu).
ARModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Construct a univariate AR model with unit variance and zero-intercept (mu).
ARModel(ARModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Copy constructor.
ArrayUtils - Class in com.numericalmethod.suanshu.misc

ARResamplerFactory - Class in com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler

ARResamplerFactory(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler.ARResamplerFactory

ARResamplerFactory() - Constructor for class com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler.ARResamplerFactory

ARTIFICIAL - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

ARTIFICIAL_COST - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

AS159 - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the lists of row and column sums.
AS159(int[], int[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
Constructs a random table generator according to the row and column totals.
AS159(int[], int[], RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159
Constructs a random table generator according to the row and column totals.
AS159.RandomMatrix - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
a random matrix generated by AS159 and its probability
asArray() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Cast this data structure as a double[].
asec(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc secant of a value; the returned angle is in the range 0.0 through pi.
asech(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc hyperbolic secant of a value.
asin(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
Inverse of sine.
asinh(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc hyperbolic sine of a value.
assertEqual(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if Number x equal to bound.
assertEqual(T, T, String, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if two Numbers x1 and x2 are equal.
assertFalse(boolean, String, Object...) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Check if an argument condition is false.
assertGreaterThan(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if Number x is greater than bound.
assertLessThan(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if Number x is less than bound.
assertNegative(T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if Number x is negative.
assertNonNegative(T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if Number x is non-negative.
assertNonPositive(T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if Number x is non-positive.
assertNormalDouble(double, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Check if an argument is a normal double value (that is, NOT Double.NaN nor infinity).
assertNormalFloat(float, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Check if an argument is a normal float value (that is, NOT Float.NaN nor infinity).
assertNotGreaterThan(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if Number x is not greater than bound.
assertNotInfinity(double, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
assertNotInfinity(float, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
assertNotLessThan(T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if Number x is not less than bound.
assertNotNaN(double, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Check if an argument is NOT a Double.NaN.
assertNotNaN(float, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Check if an argument is NOT a Float.NaN.
assertNotNull(Object, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Check if obj is not null.
assertNull(Object, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Check if obj is null.
assertPositive(T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test if Number x is positive.
assertRange(T, T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test whether the specified Number occurs within the range [low, high] (both inclusive).
assertRangeLeftOpen(T, T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test whether the specified Number occurs within the range (low, high] (left exclusive, right inclusive).
assertRangeOpen(T, T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test whether the specified Number occurs within the range (low, high) (both exclusive).
assertRangeRightOpen(T, T, T, String) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Test whether the specified Number occurs within the range [low, high) (left inclusive, right exclusive).
assertTrue(boolean, String, Object...) - Static method in class com.numericalmethod.suanshu.misc.ArgumentAssertion
Check if an argument condition is true.
assign(long, int) - Static method in class com.numericalmethod.suanshu.grid.executor.remote.akka.actor.WorkAssignment

asymptoticCDF(double) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
This is the asymptotic distribution of the Kolmogorov distribution.
asymptoticCDF(double, double) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
This is the asymptotic distribution of the one-sided Kolmogorov distribution.
atan(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
Inverse of tangent.
atanh(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc hyperbolic tangent of a value.
ATOMIC_MASS_MU - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
The atomic mass constant $$m_u$$ in kilograms (kg).
AtThreshold - Class in com.numericalmethod.suanshu.misc.algorithm.stopcondition
Stops when the value reaches a given value with a given precision.
AtThreshold(double, double) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.stopcondition.AtThreshold
Stops when the value reaches a given value with a given precision.
The Augmented Dickey Fuller test tests whether a one-time differencing (d = 1) will make the time series stationary.
Performs the Augmented Dickey-Fuller test to test for the existence of unit root.
AugmentedDickeyFuller(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
Performs the Augmented Dickey-Fuller test to test for the existence of unit root.
AUTO_SELECT_PORT - Static variable in class com.numericalmethod.suanshu.grid.executor.remote.akka.AkkaUtils
Setting this port causes Akka to automatically select a free port.
AutoARIMAFit - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima
Selects the order and estimates the coefficients of an ARIMA model automatically by AIC or AICC.
AutoARIMAFit(double[], int, int, int, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
Automatically selects and estimates the ARIMA model using custom parameters.
AutoARIMAFit(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.AutoARIMAFit
Automatically selects and estimates the ARIMA model using default parameters.
AutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.
AutoCorrelation(ARMAModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
Compute the auto-correlation function for an ARMA model.
AutoCorrelationFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate
This is the auto-correlation function of a univariate time series {xt}.
AutoCorrelationFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCorrelationFunction

AutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion.
AutoCovariance(ARMAModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
Computes the auto-covariance function for an ARMA model.
AutoCovarianceFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate
This is the auto-covariance function of a univariate time series {xt}.
AutoCovarianceFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCovarianceFunction

autoEpsilon(double...) - Static method in class com.numericalmethod.suanshu.misc.PrecisionUtils
Guess a reasonable precision parameter.
autoEpsilon(double[]...) - Static method in class com.numericalmethod.suanshu.misc.PrecisionUtils
Guess a reasonable precision parameter.
autoEpsilon(MatrixTable) - Static method in class com.numericalmethod.suanshu.misc.PrecisionUtils
Guess a reasonable precision parameter.
AutoParallelMatrixMathOperation - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation
This class uses ParallelMatrixMathOperation when the first input matrix argument's size is greater than the defined threshold; otherwise, it uses SimpleMatrixMathOperation.
AutoParallelMatrixMathOperation() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation

AutoParallelMatrixMathOperation(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation

avers(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the arc versine of a value; the returned angle is in the range zero through pi.
avg_duration1 - Variable in class com.numericalmethod.suanshu.model.dai2011.Dai2011HMM.CalibrationParam

avg_duration2 - Variable in class com.numericalmethod.suanshu.model.dai2011.Dai2011HMM.CalibrationParam

AVOGADRO_NA - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
The Avogadro constant $$N_A$$, $$L$$ in units per mole (mol-1).

## B

B() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization
Gets B, which is the square upper part of U.t().multiply(A).multiply(V).
B() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos

B() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
Gets B, which is the square upper part of U.t().multiply(A).multiply(V).
b() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the non-homogeneous part, the right-hand side vector, of the linear system.
B() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.Pow
Get the double precision matrix.
b() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
Gets the height (y-axis) of the rectangular region.
b() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Get the size of the two-dimensional space along the y-axis, that is, the range of y, (0 < y < b).
b() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Gets the size of the two-dimensional space along the y-axis, that is, the range of y, (0 < y < b).
b() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.QuadraticSyntheticDivision
Get b as in the remainder (b * (x + u) + a).
B(int, double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
Compute bn.
b() - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
Get b.
B() - Method in class com.numericalmethod.suanshu.model.elliott2005.Elliott2005DLM
Gets B as in eq.
B() - Method in class com.numericalmethod.suanshu.model.infantino2010.Infantino2010PCA.Signal

b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
Get the constraint values.
b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem
Gets b.
b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
Gets the objective vector, b, in the compact form.
b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Gets b.
b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
Gets b.
b() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Get the values, b, of the greater-than-or-equal-to constraints A * x ≥ b.
b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

B - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Get the values of the inequality constraints: b as in $$Ax \geq b$$.
b() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1

b() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter

B() - Method in class com.numericalmethod.suanshu.stats.hmm.discrete.DiscreteHMM
Gets the conditional probabilities of the observation symbols: rows correspond to state; columns corresponds symbols.
b() - Method in interface com.numericalmethod.suanshu.stats.random.variancereduction.ControlVariates.Estimator
Gets the optimal b.
B(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
Get the Brownian motion value at the i-th time point.
b1() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
Gets the GARCH coefficient.
backSearch(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDeflationSearch
Finds H22 such that H22 is the largest unreduced Hessenberg sub-matrix, and H33 is upper quasi-triangular.
backSearch(Matrix) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch

backSearch(Matrix, int) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch

backward(Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
Perform a backward sweep.
BackwardElimination - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection
Constructs a GLM model for a set of observations using the backward elimination method.
BackwardElimination(GLMProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.BackwardElimination
Constructs a GLM model using the backward elimination method, with EliminationByAIC as the default algorithm.
BackwardElimination(GLMProblem, BackwardElimination.Step) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection.BackwardElimination
Constructs a GLM model using the backward elimination method.
BackwardElimination.Step - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection

BackwardSubstitution - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem
Backward substitution solves a matrix equation in the form Ux = b by an iterative process for an upper triangular matrix U.
BackwardSubstitution() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution

BanachSpace<B,F extends Field<F> & Comparable<F>> - Interface in com.numericalmethod.suanshu.algebra.structure
A Banach space, B, is a complete normed vector space such that every Cauchy sequence (with respect to the metric d(x, y) = |x - y|) in B has a limit in B.
Bartlett - Class in com.numericalmethod.suanshu.stats.test.variance
Bartlett's test is used to test if k samples are from populations with equal variances, hence homoscedasticity.
Bartlett(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.Bartlett
Perform the Bartlett test to test for equal variances across the groups.
base() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.Pow
Get the radix or base of the coefficient.
basis() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel
Get the kernel basis.
Basis - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation
A basis is a set of linearly independent vectors spanning a vector space.
Basis(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.Basis
Construct a vector that corresponds to the i-th dimension in Rn.
basisAndFreeVars() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem.Kernel
Get the kernel basis and the associated free variables for each basis/column.
BaumWelch - Class in com.numericalmethod.suanshu.stats.hmm.discrete
This implementation trains an HMM model by observations using the Baumâ€“Welch algorithm.
BaumWelch(int[], DiscreteHMM, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.discrete.BaumWelch
Constructs an HMM model by training an initial model using the Baumâ€“Welch algorithm.
BBNode - Interface in com.numericalmethod.suanshu.misc.algorithm.bb
A branch-and-bound algorithm maintains a tree of nodes to keep track of the search paths and the pruned paths.
begin() - Method in class com.numericalmethod.suanshu.interval.Interval
Get the beginning of this interval.
BEGINNING_OF_TIME - Static variable in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
This represents a time before all (representable) times.
BEGINNING_OF_TIME_LONG - Static variable in class com.numericalmethod.suanshu.misc.datastructure.time.JodaTimeUtils
This represents a time before all (representable) times, in long representation.
beq() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Get the values, beq, of the equality constraints Aeq * x ≥ beq.
beq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

beq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Get the values of the equality constraints: beq as in $$A_{eq}x = b_{eq}$$.
beq() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1

BernoulliTrial - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
A Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success, p, is the same every time the experiment is conducted.
BernoulliTrial(RandomNumberGenerator, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.BernoulliTrial
Creates a new instance that uses the given RandomNumberGenerator to do the trial.
Best1Bin - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
The Best-1-Bin rule is the same as the Rand-1-Bin rule, except that it always pick the best candidate in the population to be the base.
Best1Bin(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Best1Bin
Construct an instance of Best1Bin.
Best2Bin - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
The Best-1-Bin rule always picks the best chromosome as the base.
Best2Bin(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin
Construct an instance of Best2Bin.
Best2Bin.DeBest2BinCell - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim

beta - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
β = 2 / v'v.
beta() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
Gets the value of the wave coefficient β
beta() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Get the value of the wave coefficient β
beta() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets β in the equation (also called thermal diffusivity in case of the heat equation).
beta() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Gets the coefficient in the PDE (thermal diffusivity in case of the heat equation).
Beta - Class in com.numericalmethod.suanshu.analysis.function.special.beta
The beta function defined as: $B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0$

The R equivalent function is beta.

Beta() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.Beta

beta() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
Get the set of cointegrating factors, by columns.
beta(int) - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
Get the r-th cointegrating factor, counting from 1.
beta - Variable in class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
β: the shape parameter
beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
Gets the GLM coefficients estimator, β^.
beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
Gets the GLM coefficient estimator, β^.
beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyLARS

beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyQP

beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent

beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.UnconstrainedLASSObyQP

beta() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.LinearModel
Gets $$\hat{\beta}$$ and statistics.
beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticRegression

beta() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression

beta() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
Gets the GARCH coefficient.
beta() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Get the GARCH coefficients.
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.
Construct a Beta distribution.
betaHat() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.GLMFitting
Gets the estimates of β, β^, as in E(Y) = μ = g-1(Xβ)
betaHat() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS

betaHat() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMNewtonRaphson

betaHat() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMBeta
Gets the coefficient estimates, β^.
betaMatrix() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators
Gets the entire sequence of estimated (LARS) regression coefficients.
BetaMixtureDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
The HMM states use the Beta distribution to model the observations.
BetaMixtureDistribution(BetaMixtureDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution
Constructs a Beta distribution for each state in the HMM model.
BetaMixtureDistribution(BetaMixtureDistribution.Lambda[], int) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BetaMixtureDistribution
Constructs a Beta distribution for each state in the HMM model.
BetaMixtureDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
the Beta distribution parameters
BetaRegularized - Class in com.numericalmethod.suanshu.analysis.function.special.beta
The Regularized Incomplete Beta function is defined as: $I_x(p,q) = \frac{B(x;\,p,q)}{B(p,q)} = \frac{1}{B(p,q)} \int_0^x t^{p-1}\,(1-t)^{q-1}\,dt, p > 0, q > 0$

The R equivalent function is pbeta.

BetaRegularized(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularized
Construct an instance of Ix(p,q) with the parameters p and q.
BetaRegularizedInverse - Class in com.numericalmethod.suanshu.analysis.function.special.beta
The inverse of the Regularized Incomplete Beta function is defined at: $x = I^{-1}_{(p,q)}(u), 0 \le u \le 1$

The R equivalent function is qbeta.

BetaRegularizedInverse(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.beta.BetaRegularizedInverse
Construct an instance of $$I^{-1}_{(p,q)}(u)$$ with parameters p and p.
betas() - Method in class com.numericalmethod.suanshu.stats.regression.linear.lasso.lars.LARSFitting.Estimators

BFGSImpl(C2OptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer.BFGSImpl

BFGSMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
The Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.
BFGSMinimizer(boolean, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
Construct a multivariate minimizer using the BFGS method.
BFGSMinimizer.BFGSImpl - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
an implementation of the BFGS algorithm
BFS<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
This class implements the breadth-first-search using iteration.
BFS(Graph<V, ? extends Edge<V>>) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS
Constructs a BFS tree of a graph.
BFS(Graph<W, ? extends Edge<V>>, V, int) - Static method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS
Runs the breadth-first-search on a graph from a designated root.
BFS.Node<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
This is a node in a BFS-spanning tree.
BIC() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMInformationCriteria
Gets the Bayesian information criterion.
The Biconjugate Gradient method (BiCG) is useful for solving non-symmetric n-by-n linear systems.
Construct a Biconjugate Gradient (BiCG) solver.
Construct a Biconjugate Gradient (BiCG) solver.
The Biconjugate Gradient Stabilized (BiCGSTAB) method is useful for solving non-symmetric n-by-n linear systems.
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
BicubicInterpolation - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
Bicubic interpolation is the two-dimensional equivalent of cubic Hermite spline interpolation.
BicubicInterpolation() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
Create a new instance which computes the partial derivatives using PartialDerivativesByCenteredDifferencing.
BicubicInterpolation(BicubicInterpolation.PartialDerivatives) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
Create a new instance which uses the given derivatives to interpolate.
BicubicInterpolation.PartialDerivatives - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
Specify the partial derivatives defined at points on a BivariateGrid.
BicubicSpline - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
Bicubic splines are the two-dimensional equivalent of cubic splines.
BicubicSpline() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BicubicSpline

BiDiagonalization - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization
Given a tall (m x n) matrix A, where m ≥ n, find orthogonal matrices U and V such that U' * A * V = B.
BiDiagonalizationByGolubKahanLanczos - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization
This implementation uses Golub-Kahan-Lanczos algorithm with reorthogonalization.
BiDiagonalizationByGolubKahanLanczos(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
BiDiagonalizationByGolubKahanLanczos(Matrix, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
BiDiagonalizationByGolubKahanLanczos(Matrix, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
BiDiagonalizationByHouseholder - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization
Given a tall (m x n) matrix A, where m ≥ n, we find orthogonal matrices U and V such that U' * A * V = B.
BiDiagonalizationByHouseholder(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
Runs the Householder bi-diagonalization for a tall matrix.
BidiagonalMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
A bi-diagonal matrix is either upper or lower diagonal.
BidiagonalMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
Constructs a bi-diagonal matrix from a 2D double[][] array.
BidiagonalMatrix(int, BidiagonalMatrix.BidiagonalMatrixType) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
Constructs a 0 bi-diagonal matrix of dimension dim * dim.
BidiagonalMatrix(BidiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
Copy constructor.
BidiagonalMatrix.BidiagonalMatrixType - Enum in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
the available types of bi-diagonal matrices
BidiagonalSVDbyMR3 - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3
Given a bidiagonal matrix A, computes the singular value decomposition (SVD) of A, using "Algorithm of Multiple Relatively Robust Representations" (MRRR).
BidiagonalSVDbyMR3(BidiagonalMatrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
Creates a singular value decomposition for a bidiagonal matrix A.
BigDecimalUtils - Class in com.numericalmethod.suanshu.number.big
These are the utility functions to manipulate BigDecimal.
bigDecimalValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
Convert the number to BigDecimal.
BigIntegerUtils - Class in com.numericalmethod.suanshu.number.big
These are the utility functions to manipulate BigInteger.
BilinearInterpolation - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
Bilinear interpolation is the 2-dimensional equivalent of linear interpolation.
BilinearInterpolation() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BilinearInterpolation

bin(MultinomialRVG) - Static method in class com.numericalmethod.suanshu.stats.markovchain.SimpleMC
Picks the first non-empty bin.
BinomialDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.
BinomialDistribution(int, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
Construct a Binomial distribution.
BinomialMixtureDistribution - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
The HMM states use the Binomial distribution to model the observations.
BinomialMixtureDistribution(BinomialMixtureDistribution.Lambda[]) - Constructor for class com.numericalmethod.suanshu.stats.hmm.mixture.distribution.BinomialMixtureDistribution
Constructs a Binomial distribution for each state in the HMM model.
BinomialMixtureDistribution.Lambda - Class in com.numericalmethod.suanshu.stats.hmm.mixture.distribution
the Binomial distribution parameters
BinomialRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
This random number generator samples from the binomial distribution.
BinomialRNG(int, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.BinomialRNG
Construct a random number generator to sample from the binomial distribution.
BinomialRNG(int, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.BinomialRNG
Construct a random number generator to sample from the binomial distribution.
Bins<T> - Class in com.numericalmethod.suanshu.misc.algorithm
This class divides the items based on their keys into a number of bins.
Bins(int) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.Bins
Constructs an empty bin of valued items.
Bins(int, Map<Double, T>) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.Bins
Constructs a bin with valued items.
BisectionRoot - Class in com.numericalmethod.suanshu.analysis.root.univariate
The bisection method repeatedly bisects an interval and then selects a subinterval in which a root must lie for further processing.
BisectionRoot() - Constructor for class com.numericalmethod.suanshu.analysis.root.univariate.BisectionRoot
Create an instance with Constants.EPSILON as the tolerance and Integer.MAX_VALUE as the maximum number of iterations.
BisectionRoot(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.root.univariate.BisectionRoot
Create an instance with the tolerance for convergence and the maximum number of iterations.
BivariateArrayGrid - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
Implementation of BivariateGrid, backed by arrays.
BivariateArrayGrid(double[][], double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
Create a new grid with a given two-dimensional array of grid values, and values for grid line positions along the x-axis and the y-axis.
BivariateEVD - Interface in com.numericalmethod.suanshu.stats.evt.evd.bivariate
Bivariate Extreme Value (BEV) distribution is the joint distribution of component-wise maxima of two-dimensional iid random vectors.
BivariateEVDAsymmetricLogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
The bivariate asymmetric logistic model.
BivariateEVDAsymmetricLogistic(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

BivariateEVDAsymmetricLogistic(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

BivariateEVDAsymmetricLogistic(double, double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

BivariateEVDAsymmetricLogistic(double, double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

BivariateEVDAsymmetricMixed - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
The asymmetric mixed model.
BivariateEVDAsymmetricMixed(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed

BivariateEVDAsymmetricMixed(double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed

BivariateEVDAsymmetricMixed(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed

BivariateEVDAsymmetricNegativeLogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
The bivariate asymmetric negative logistic model.
BivariateEVDAsymmetricNegativeLogistic(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

BivariateEVDAsymmetricNegativeLogistic(double, double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

BivariateEVDAsymmetricNegativeLogistic(double, double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

BivariateEVDAsymmetricNegativeLogistic(double, double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

BivariateEVDBilogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
The bilogistic model.
BivariateEVDBilogistic(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic

BivariateEVDBilogistic(double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic

BivariateEVDBilogistic(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic

BivariateEVDColesTawn - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
The Coles-Tawn model.
BivariateEVDColesTawn(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn

BivariateEVDColesTawn(double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn

BivariateEVDColesTawn(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn

BivariateEVDHuslerReiss - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
The Husler-Reiss model.
BivariateEVDHuslerReiss(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss

BivariateEVDHuslerReiss(double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss

BivariateEVDHuslerReiss(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss

BivariateEVDLogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
The bivariate logistic model.
BivariateEVDLogistic(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic

BivariateEVDLogistic(double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic

BivariateEVDLogistic(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic

BivariateEVDNegativeBilogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
The negative bilogistic model.
BivariateEVDNegativeBilogistic(double, double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic

BivariateEVDNegativeBilogistic(double, double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic

BivariateEVDNegativeBilogistic(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic

BivariateEVDNegativeLogistic - Class in com.numericalmethod.suanshu.stats.evt.evd.bivariate
The bivariate negative logistic model.
BivariateEVDNegativeLogistic(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic

BivariateEVDNegativeLogistic(double, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic

BivariateEVDNegativeLogistic(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic

BivariateGrid - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
A rectilinear (meaning that grid lines are not necessarily equally-spaced) bivariate grid of double values.
BivariateGridInterpolation - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
A bivariate interpolation, which requires the input to form a rectilinear grid.
BivariateProbabilityDistribution - Interface in com.numericalmethod.suanshu.stats.distribution.multivariate
A bivariate or joint probability distribution for X_1, X_2 is a probability distribution that gives the probability that each of X_1, X_2, ... falls in any particular range or discrete set of values specified for that variable.
BivariateRealFunction - Interface in com.numericalmethod.suanshu.analysis.function.rn2r1
A bivariate real function takes two real arguments and outputs one real value.
BivariateRegularGrid - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate
A regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes (e.g.
BivariateRegularGrid(double[][], double, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
Create a new grid where the dependent variable values are taken from the given two-dimensional array and the values of the dependent variables are specified by their first values and the difference between successive values.
BlockSplitPointSearch - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3
Computes the splitting points with the given threshold.
BlockSplitPointSearch(double, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.BlockSplitPointSearch

This implementation accelerates matrix multiplication via a combination of the Strassen algorithm and block matrix multiplication.

BMSDE - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete
A Brownian motion is a stochastic process with the following properties.
BMSDE(double, double) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.BMSDE
Construct a univariate Brownian motion.
BMSDE() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.BMSDE
Construct a univariate standard Brownian motion.
BoltzAnnealingFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction
Matlab: @annealingboltz - The step has length square root of temperature, with direction uniformly at random.
BoltzAnnealingFunction(int, RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
Constructs a new instance where the RVG is created from a given RLG.
BoltzAnnealingFunction(RandomVectorGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
Constructs a new instance that uses a given RVG.
BOLTZMANN_K - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
The Boltzmann constant $$k$$ in joule per kelvin (J K-1).
BoltzTemperatureFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction
$$T_k = T_0 / ln(k)$$.
BoltzTemperatureFunction(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction.BoltzTemperatureFunction
Constructs a new instance with an initial temperature.
BootstrapEstimator - Class in com.numericalmethod.suanshu.stats.random.sampler.resampler
This class estimates the statistic of a sample using a bootstrap method.
BootstrapEstimator(Resampler, StatisticFactory, int) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.BootstrapEstimator
Constructs a bootstrap estimator.
BootstrapEstimator(Resampler, StatisticFactory, int, boolean) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.BootstrapEstimator
Constructs a bootstrap estimator.
BorderedHessian - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
A bordered Hessian matrix consists of the Hessian of a multivariate function f, and the gradient of a multivariate function g.
BorderedHessian(RealScalarFunction, RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.BorderedHessian
Construct the bordered Hessian matrix for multivariate functions f and g at point x.
BottomUp<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
This implementation traverses a directed acyclic graph starting from the leaves at the bottom, and reaches the roots.
BottomUp(DAGraph<V, ? extends Arc<V>>) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.BottomUp
Constructs a BottomUp traversal instance.
bound(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Bounds a given value by a given range.
Bound(int, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints.Bound
Construct a bound constraint for a variable.
bounds() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
Get a deep copy of the bounds.
BoxConstraints - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
This represents the lower and upper bounds for a variable.
BoxConstraints(int, BoxConstraints.Bound...) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
Construct a set of bound constraints.
BoxConstraints(Vector, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints
Construct a set of bound constraints.
BoxConstraints.Bound - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear
A bound constraint for a variable.
BoxGeneralizedSimulatedAnnealingMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box
This is an extension to GeneralizedSimulatedAnnealingMinimizer, which allows adding box constraints to bound solutions.
BoxGeneralizedSimulatedAnnealingMinimizer(int, double, double, double, StopCondition, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
BoxGeneralizedSimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
BoxGeneralizedSimulatedAnnealingMinimizer(int, StopCondition) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
BoxGSAAcceptanceProbabilityFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
This probability function boxes an unconstrained probability function so that when a proposed state is outside the box, it has a probability of 0.
BoxGSAAcceptanceProbabilityFunction(Vector, Vector, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.BoxGSAAcceptanceProbabilityFunction
Constructs a boxed acceptance probability function.
BoxGSAAnnealingFunction - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction

BoxGSAAnnealingFunction(Vector, Vector, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction.BoxGSAAnnealingFunction
Constructs a boxed annealing function.
BoxMinimizer<P extends BoxOptimProblem,S extends MinimizationSolution<?>> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained
A box minimizer solves a BoxOptimProblem.
BoxMuller - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.normal
The Box-Muller transform (by George Edward Pelham Box and Mervin Edgar Muller 1958) is a pseudo-random number sampling method for generating pairs of independent standard normally distributed (zero expectation, unit variance) random numbers, given a source of uniformly distributed random numbers.
BoxMuller(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.BoxMuller
Construct a random number generator to sample from the standard Normal distribution.
BoxMuller() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.BoxMuller
Construct a random number generator to sample from the standard Normal distribution.
BoxOptimProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.problem
A box constrained optimization problem, for which a solution must be within fixed bounds.
BoxOptimProblem(RealScalarFunction, BoxConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
Constructs an optimization problem with box constraints.
BoxOptimProblem(RealScalarFunction, Vector, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
Constructs an optimization problem with box constraints.
BoxOptimProblem(BoxOptimProblem) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.BoxOptimProblem
Copy constructor.
BoxPierce - Class in com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
Deprecated.
BoxPierce(double[], int, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
Deprecated.
Perform the Box-Pierce test to check auto-correlation in a time series.
BracketSearchMinimizer - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
This class provides implementation support for those univariate optimization algorithms that are based on bracketing.
BracketSearchMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BracketSearchMinimizer
Construct a univariate minimizer using a bracket search method.
BracketSearchMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch

BranchAndBound - Class in com.numericalmethod.suanshu.misc.algorithm.bb
Branch-and-Bound (BB or B&B) is a general algorithm for finding optimal solutions of various optimization problems, especially in discrete and combinatorial optimization.
BranchAndBound(ActiveList, BBNode) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.bb.BranchAndBound
Solve a minimization problem using a branch-and-bound algorithm.
BranchAndBound(BBNode) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.bb.BranchAndBound
Solve a minimization problem using a branch-and-bound algorithm using depth-first search.
branching() - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.BBNode
Get the children of this node by using the branching operation.
branching() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb.ILPNode
Get the children of this node by using the branching operation.
BrentCetaMaximizer - Class in com.numericalmethod.suanshu.model.lai2010.ceta.maximizer
Searches for the maximal point of C(η) by Brent's method.
BrentCetaMaximizer(double) - Constructor for class com.numericalmethod.suanshu.model.lai2010.ceta.maximizer.BrentCetaMaximizer
Constructs a maximizer with a given ε (for the Brent's search algorithm).
BrentCetaMaximizer() - Constructor for class com.numericalmethod.suanshu.model.lai2010.ceta.maximizer.BrentCetaMaximizer
Constructs a maximizer using the default epsilon (for the Brent's search algorithm).
BrentMinimizer - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
Brent's algorithm is the preferred method for finding the minimum of a univariate function.
BrentMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.univariate.bracketsearch.BrentMinimizer
Construct a univariate minimizer using Brent's algorithm.
BrentMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.univariate.bracketsearch
This is the solution to a Brent's univariate optimization.
BrentRoot - Class in com.numericalmethod.suanshu.analysis.root.univariate
Brent's root-finding algorithm combines super-linear convergence with reliability of bisection.
BrentRoot(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.root.univariate.BrentRoot
Construct an instance of Brent's root finding algorithm.
BreuschPagan - Class in com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
The Breusch-Pagan test tests for conditional heteroskedasticity.
BreuschPagan(LMResiduals, boolean) - Constructor for class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.BreuschPagan
Perform the Breusch-Pagan test to test for heteroskedasticity in a linear regression model.
A message that is sent to each slave by the master.
Creates a new instance which should cause the
BrownForsythe - Class in com.numericalmethod.suanshu.stats.test.variance
The Brown-Forsythe test is a statistical test for the equality of group variances based on performing an ANOVA on a transformation of the response variable.
BrownForsythe(double[]...) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
Perform the Brown-Forsythe test to test for equal variances across the groups.
BruteForceIPMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce
This implementation solves an integral constrained minimization problem by brute force search for all possible integer combinations.
BruteForceIPMinimizer(SubProblemMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem, IterativeSolution<Vector>>>) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
Constructs a brute force minimizer to solve integral constrained minimization problems.
BruteForceIPMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
Constructs a brute force minimizer to solve integral constrained minimization problems.
BruteForceIPMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce
This is the solution to an integral constrained minimization using the brute-force search.
BruteForceIPProblem - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce
This implementation is an integral constrained minimization problem that has enumerable integral domains.
BruteForceIPProblem(RealScalarFunction, EqualityConstraints, LessThanConstraints, BruteForceIPProblem.IntegerDomain[], double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
Construct an integral constrained minimization problem with explicit integral domains.
BruteForceIPProblem(RealScalarFunction, BruteForceIPProblem.IntegerDomain[], double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
Construct an integral constrained minimization problem with explicit integral domains.
BruteForceIPProblem.IntegerDomain - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce
This specifies the integral domain for an integral variable, i.e., the integer values the variable can take.
Bt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration
This is a FiltrationFunction that returns $$B(t_i)$$, the Brownian motion value at the i-th time point.
Bt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Bt

Bt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
Get the entire Brownian path.
build() - Method in class com.numericalmethod.suanshu.model.dai2011.Dai2011Solver.Builder

build(double) - Method in class com.numericalmethod.suanshu.model.hvolatility.Kagi
Makes a KAGI construction for the given random process.
Builder(Dai2011HMM, double) - Constructor for class com.numericalmethod.suanshu.model.dai2011.Dai2011Solver.Builder

BurlischStoerExtrapolation - Class in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation
Burlisch-Stoer extrapolation (or Gragg-Bulirsch-Stoer (GBS)) algorithm combines three powerful ideas: Richardson extrapolation, the use of rational function extrapolation in Richardson-type applications, and the modified midpoint method, to obtain numerical solutions to ordinary differential equations (ODEs) with high accuracy and comparatively little computational effort.
BurlischStoerExtrapolation(double, int) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation.BurlischStoerExtrapolation
Create an instance of the algorithm with the precision parameter and the maximum number of iterations allowed.
BurnInRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate
A burn-in random number generator discards the first M samples.
BurnInRNG(RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.BurnInRNG
Construct a burn-in RNG.
BurnInRVG - Class in com.numericalmethod.suanshu.stats.random.rng.multivariate
A burn-in random number generator discards the first M samples.
BurnInRVG(RandomVectorGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.multivariate.BurnInRVG
Construct a burn-in RVG.

## C

c() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Gets the value of c.
c() - Method in class com.numericalmethod.suanshu.analysis.function.special.gaussian.Gaussian
Get c.
C() - Method in class com.numericalmethod.suanshu.model.elliott2005.Elliott2005DLM
Gets C as in eq.
C() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPDualProblem
Gets C.
C() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
Gets C.
c(int) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Gets ci.
c() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem

c() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
Gets c.
c() - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Get the objective function.
c() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

c() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem.ILPProblemImpl1

c() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter

c(int) - Method in class com.numericalmethod.suanshu.stats.hmm.ForwardBackwardProcedure

C0() - Method in class com.numericalmethod.suanshu.stats.dlm.multivariate.MultivariateDLM
Get the covariance matrix of x0.
C0() - Method in class com.numericalmethod.suanshu.stats.dlm.univariate.DLM
Get the variance of x0.
c1() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the coefficient c1 in the mixed boundary condition at the boundary x = 0.
c1() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets the coefficient c1 in the mixed boundary condition at the boundary x = 0.
C1 - Interface in com.numericalmethod.suanshu.analysis.differentiation.differentiability
A function, f, is said to be of class C1 if the derivative f' exists.
c2() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the coefficient c2 in the mixed boundary condition at the boundary x = a.
c2() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets the coefficient c2 in the mixed boundary condition at the boundary x = a.
C2 - Interface in com.numericalmethod.suanshu.analysis.differentiation.differentiability
A function, f, is said to be of class C2 if the first and second derivatives, f' and f'', exist.
C2OptimProblem - Interface in com.numericalmethod.suanshu.optimization.problem
This is an optimization problem of a real valued function that is twice differentiable.
C2OptimProblemImpl - Class in com.numericalmethod.suanshu.optimization.problem
This is an optimization problem of a real valued function: $$\max_x f(x)$$.
C2OptimProblemImpl(RealScalarFunction, RealVectorFunction, RntoMatrix) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
Construct an optimization problem with an objective function.
C2OptimProblemImpl(RealScalarFunction, RealVectorFunction) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
Construct an optimization problem with an objective function.
C2OptimProblemImpl(RealScalarFunction) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
Construct an optimization problem with an objective function.
C2OptimProblemImpl(C2OptimProblemImpl) - Constructor for class com.numericalmethod.suanshu.optimization.problem.C2OptimProblemImpl
Copy Ctor.
CalibrationParam(double, double, double, double, double, double) - Constructor for class com.numericalmethod.suanshu.model.dai2011.Dai2011HMM.CalibrationParam

CartesianProduct<T> - Class in com.numericalmethod.suanshu.misc.algorithm
The Cartesian product can be generalized to the n-ary Cartesian product over n sets X1, ..., Xn.
CartesianProduct(T[]...) - Constructor for class com.numericalmethod.suanshu.misc.algorithm.CartesianProduct
Construct an Iterable of all combinations of arrays, taking one element from each array.
CaseResamplingReplacement - Class in com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap
This is the classical bootstrap method described in the reference.
CaseResamplingReplacement(double[]) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacement
Constructs a bootstrap sample generator.
CaseResamplingReplacement(double[], RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacement
Constructs a bootstrap sample generator.
CaseResamplingReplacement(double[], ConcurrentCachedRLG) - Constructor for class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacement
Constructs a bootstrap sample generator.
CauchyPolynomial - Class in com.numericalmethod.suanshu.analysis.function.polynomial
The Cauchy's polynomial of a polynomial takes this form:
CauchyPolynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.CauchyPolynomial

cbind(Vector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of vectors by columns.
cbind(SparseVector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of sparse vectors by columns and returns a CSR sparse matrix.
cbind(List<Vector>) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines a list of vectors by columns.
cbind(Matrix...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of matrices by columns.
cbind(SparseMatrix...) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of sparse matrices by columns.
ccdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution

ccdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution
The complementary cumulative distribution function.
cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.AbstractBivariateProbabilityDistribution

cdf(double, double) - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.BivariateProbabilityDistribution
The joint distribution function $$F_{X_1,X_2}(x_1,x_2) = Pr(X_1 \le x_1, X_2 \le x_2)$$.
cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution

cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution

cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution

cdf(Vector) - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
Gets the cumulative probability F(x) = Pr(X ≤ x).
cdf(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
Gets the cumulative probability F(x) = Pr(X ≤ x).
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution

cdf(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
Gets the cumulative probability F(x) = Pr(X ≤ x).
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution

cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed

cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic

cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn

cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss

cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic

cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic

cdf(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic

cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
Gets the cumulative probability F(x) = Pr(X ≤ x).
cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
The cumulative distribution function.
cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
The cumulative distribution function.
cdf(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution

cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution

centralMoment(int) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
Get the value of the k-th central moment.
CentralPath - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing
A central path is a solution to both the primal and dual problems of a semi-definite programming problem.
CentralPath(Matrix, Vector, Matrix) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
Construct a central path.
Ceta - Class in com.numericalmethod.suanshu.model.lai2010.ceta
The function C(η) to be maximized (Eq.
Ceta(Matrix, double, Ceta.MomentsEstimator) - Constructor for class com.numericalmethod.suanshu.model.lai2010.ceta.Ceta

Ceta.Moments - Class in com.numericalmethod.suanshu.model.lai2010.ceta

Ceta.MomentsEstimator - Interface in com.numericalmethod.suanshu.model.lai2010.ceta

CetaMaximizer - Interface in com.numericalmethod.suanshu.model.lai2010.ceta.maximizer
Defines an algorithm to search for the maximal C(η).
CetaMaximizer.NegCetaFunction - Class in com.numericalmethod.suanshu.model.lai2010.ceta.maximizer

CetaMaximizer.Solution - Class in com.numericalmethod.suanshu.model.lai2010.ceta.maximizer

ChangeOfVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
Change of variable can easy the computation of some integrals, such as improper integrals.
ChangeOfVariable(SubstitutionRule, Integrator) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
Construct an integrator that uses change of variable to do integration.
CHARACTERISTIC_IMPEDANCE_Z0 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
The characteristic impedance of vacuum $$Z_0$$ in ohms (Ω).
CharacteristicPolynomial - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen
The characteristic polynomial of a square matrix is the function
CharacteristicPolynomial(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
Construct the characteristic polynomial for a square matrix.
ChebyshevRule - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule

ChebyshevRule(int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
Create a Chebyshev rule of the given order.
checkInputs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMProblem
Checks whether this LMProblem instance is valid.
checkInputs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticProblem

Cheng1978 - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.beta
Cheng, 1978, is a new rejection method for generating beta variates.
Cheng1978(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.Cheng1978
Construct a random number generator to sample from the beta distribution.
Cheng1978(double, double) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.beta.Cheng1978
Construct a random number generator to sample from the beta distribution.
children(V) - Method in interface com.numericalmethod.suanshu.graph.DiGraph
Gets the set of all children of this vertex.
children(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseDiGraph

children(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree

children(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree

ChiSquareDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
The Chi-square distribution is the distribution of the sum of the squares of a set of statistically independent standard Gaussian random variables.
ChiSquareDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
Construct a Chi-Square distribution.
ChiSquareIndependenceTest - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson
Pearson's chi-square test of independence assesses whether paired observations on two variables, expressed in a contingency table, are independent of each other.
ChiSquareIndependenceTest(Matrix, int, ChiSquareIndependenceTest.Type) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
Assess whether the two random variables in the contingency table are independent.
ChiSquareIndependenceTest(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquareIndependenceTest
Assess whether the two random variables in the contingency table are independent.
ChiSquareIndependenceTest.Type - Enum in com.numericalmethod.suanshu.stats.test.distribution.pearson
the available distributions used for the test
Chol - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.
Chol(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
Chol(Matrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
Chol(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
Cholesky - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.
CholeskyBanachiewicz - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.
CholeskyBanachiewicz(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewicz
Runs the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
CholeskyBanachiewiczParallelized - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
This is a parallelized version of CholeskyBanachiewicz.
CholeskyBanachiewiczParallelized(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized

CholeskySparse - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.
CholeskySparse(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskySparse
Runs the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
CholeskyWang2006 - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition works only for a positive definite matrix.
CholeskyWang2006(Matrix, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyWang2006
Constructs the Cholesky decomposition of a matrix.
Chromosome - Interface in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm
A chromosome is a representation of a solution to an optimization problem.
clear() - Method in interface com.numericalmethod.suanshu.misc.algorithm.bb.ActiveList
Removes all of the elements from this collection.
clear() - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet

Cluster(int, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.cluster.Clusters.Cluster
Create a cluster with the beginning and ending indices of the cluster.
ClusterAnalyzer - Class in com.numericalmethod.suanshu.stats.evt.cluster
This class counts clusters of exceedances based on observations above a given threshold, and the discontinuity of exceedances can be tolerated by an interval length r.
ClusterAnalyzer(double) - Constructor for class com.numericalmethod.suanshu.stats.evt.cluster.ClusterAnalyzer
Create an instance with the given threshold value and default interval length value of 1.
ClusterAnalyzer(double, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.cluster.ClusterAnalyzer
Create an instance with the given threshold and clustering interval length.
clusters() - Method in class com.numericalmethod.suanshu.graph.community.GirvanNewman
Gets all the clusters, each of which is connected.
Clusters - Class in com.numericalmethod.suanshu.stats.evt.cluster
Store cluster information obtained by cluster analysis.
Clusters(double[], List<Clusters.Cluster>, int) - Constructor for class com.numericalmethod.suanshu.stats.evt.cluster.Clusters

Clusters.Cluster - Class in com.numericalmethod.suanshu.stats.evt.cluster
Define the beginning and ending indices (inclusively) of a cluster.
Coefficients(ConvectionDiffusionEquation1D, int, int, double[]) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D.Coefficients
Constructs the coefficient computation
Coefficients(HeatEquation1D, int, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
Constructs the coefficient computation
CointegrationMLE - Class in com.numericalmethod.suanshu.stats.cointegration
Two or more time series are cointegrated if they each share a common type of stochastic drift, that is, to a limited degree they share a certain type of behavior in terms of their long-term fluctuations, but they do not necessarily move together and may be otherwise unrelated.
CointegrationMLE(MultivariateSimpleTimeSeries, boolean, int, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
Perform the Johansen MLE procedure on a multivariate time series.
CointegrationMLE(MultivariateSimpleTimeSeries, boolean, int) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
Perform the Johansen MLE procedure on a multivariate time series, using the EIGEN test.
CointegrationMLE(MultivariateSimpleTimeSeries, boolean) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMLE
Perform the Johansen MLE procedure on a multivariate time series, using the EIGEN test, with the number of lags = 2.
collection2DoubleArray(Collection<? extends Number>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Convert a collection of numbers to a double array.
collection2IntArray(Collection<Integer>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Convert a collection of Integers to an int array.
collection2LongArray(Collection<Long>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Convert a collection of Longs to a long array.
CollectWorkerCounts - Class in com.numericalmethod.suanshu.grid.executor.remote.akka.message
Request to collect the number of workers managed by the slaves.
CollectWorkerCounts() - Constructor for class com.numericalmethod.suanshu.grid.executor.remote.akka.message.CollectWorkerCounts

colMeans(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the column means.
colMeanVector(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the column mean vector of a given matrix.
color - Variable in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node

color() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS.Node
Gets the color of this node.
colSums(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the column sums.
colSumVector(MatrixTable) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the column sum vector of a given matrix.
ColumnBindMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
A fast "cbind" matrix from vectors.
ColumnBindMatrix(Vector...) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

columns(Matrix, int[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the columns of a matrix.
columns(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the columns of a matrix.
com.numericalmethod.suanshu.algebra.linear.matrix - package com.numericalmethod.suanshu.algebra.linear.matrix

com.numericalmethod.suanshu.algebra.linear.matrix.doubles - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3 - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite - package com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite

com.numericalmethod.suanshu.algebra.linear.matrix.generic - package com.numericalmethod.suanshu.algebra.linear.matrix.generic

com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype - package com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype

com.numericalmethod.suanshu.algebra.linear.vector - package com.numericalmethod.suanshu.algebra.linear.vector

com.numericalmethod.suanshu.algebra.linear.vector.doubles - package com.numericalmethod.suanshu.algebra.linear.vector.doubles

com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense - package com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense

com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation - package com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation

com.numericalmethod.suanshu.algebra.structure - package com.numericalmethod.suanshu.algebra.structure

com.numericalmethod.suanshu.analysis.curvefit - package com.numericalmethod.suanshu.analysis.curvefit

com.numericalmethod.suanshu.analysis.curvefit.interpolation - package com.numericalmethod.suanshu.analysis.curvefit.interpolation

com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate - package com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate

com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate - package com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate

com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate - package com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate

com.numericalmethod.suanshu.analysis.differentialequation - package com.numericalmethod.suanshu.analysis.differentialequation

com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem - package com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem

com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver - package com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver

com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation - package com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation

com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta - package com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta

com.numericalmethod.suanshu.analysis.differentialequation.pde - package com.numericalmethod.suanshu.analysis.differentialequation.pde

com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference

com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2 - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2

com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1 - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1

com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2 - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2

com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation

com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation

com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2 - package com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2

com.numericalmethod.suanshu.analysis.differentiation - package com.numericalmethod.suanshu.analysis.differentiation

com.numericalmethod.suanshu.analysis.differentiation.differentiability - package com.numericalmethod.suanshu.analysis.differentiation.differentiability

com.numericalmethod.suanshu.analysis.differentiation.multivariate - package com.numericalmethod.suanshu.analysis.differentiation.multivariate

com.numericalmethod.suanshu.analysis.differentiation.univariate - package com.numericalmethod.suanshu.analysis.differentiation.univariate

com.numericalmethod.suanshu.analysis.function - package com.numericalmethod.suanshu.analysis.function

com.numericalmethod.suanshu.analysis.function.matrix - package com.numericalmethod.suanshu.analysis.function.matrix

com.numericalmethod.suanshu.analysis.function.polynomial - package com.numericalmethod.suanshu.analysis.function.polynomial

com.numericalmethod.suanshu.analysis.function.polynomial.root - package com.numericalmethod.suanshu.analysis.function.polynomial.root

com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub - package com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub

com.numericalmethod.suanshu.analysis.function.rn2r1 - package com.numericalmethod.suanshu.analysis.function.rn2r1

com.numericalmethod.suanshu.analysis.function.rn2r1.univariate - package com.numericalmethod.suanshu.analysis.function.rn2r1.univariate

com.numericalmethod.suanshu.analysis.function.rn2rm - package com.numericalmethod.suanshu.analysis.function.rn2rm

com.numericalmethod.suanshu.analysis.function.special - package com.numericalmethod.suanshu.analysis.function.special

com.numericalmethod.suanshu.analysis.function.special.beta - package com.numericalmethod.suanshu.analysis.function.special.beta

com.numericalmethod.suanshu.analysis.function.special.gamma - package com.numericalmethod.suanshu.analysis.function.special.gamma

com.numericalmethod.suanshu.analysis.function.special.gaussian - package com.numericalmethod.suanshu.analysis.function.special.gaussian

com.numericalmethod.suanshu.analysis.function.tuple - package com.numericalmethod.suanshu.analysis.function.tuple

com.numericalmethod.suanshu.analysis.integration.univariate - package com.numericalmethod.suanshu.analysis.integration.univariate

com.numericalmethod.suanshu.analysis.integration.univariate.riemann - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann

com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian

com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule

com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes

com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution

com.numericalmethod.suanshu.analysis.root.multivariate - package com.numericalmethod.suanshu.analysis.root.multivariate

com.numericalmethod.suanshu.analysis.root.univariate - package com.numericalmethod.suanshu.analysis.root.univariate

com.numericalmethod.suanshu.analysis.sequence - package com.numericalmethod.suanshu.analysis.sequence

com.numericalmethod.suanshu.combinatorics - package com.numericalmethod.suanshu.combinatorics

com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles - package com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles

com.numericalmethod.suanshu.geometry - package com.numericalmethod.suanshu.geometry

com.numericalmethod.suanshu.geometry.polyline - package com.numericalmethod.suanshu.geometry.polyline

com.numericalmethod.suanshu.graph - package com.numericalmethod.suanshu.graph

com.numericalmethod.suanshu.graph.algorithm.shortestpath - package com.numericalmethod.suanshu.graph.algorithm.shortestpath

com.numericalmethod.suanshu.graph.algorithm.traversal - package com.numericalmethod.suanshu.graph.algorithm.traversal

com.numericalmethod.suanshu.graph.community - package com.numericalmethod.suanshu.graph.community

com.numericalmethod.suanshu.graph.type - package com.numericalmethod.suanshu.graph.type

com.numericalmethod.suanshu.grid.config.dc - package com.numericalmethod.suanshu.grid.config.dc

com.numericalmethod.suanshu.grid.config.local - package com.numericalmethod.suanshu.grid.config.local

com.numericalmethod.suanshu.grid.config.remote - package com.numericalmethod.suanshu.grid.config.remote

com.numericalmethod.suanshu.grid.config.xml - package com.numericalmethod.suanshu.grid.config.xml

com.numericalmethod.suanshu.grid.config.xml.schema - package com.numericalmethod.suanshu.grid.config.xml.schema

com.numericalmethod.suanshu.grid.executor - package com.numericalmethod.suanshu.grid.executor

com.numericalmethod.suanshu.grid.executor.local - package com.numericalmethod.suanshu.grid.executor.local

com.numericalmethod.suanshu.grid.executor.remote - package com.numericalmethod.suanshu.grid.executor.remote

com.numericalmethod.suanshu.grid.executor.remote.akka - package com.numericalmethod.suanshu.grid.executor.remote.akka

com.numericalmethod.suanshu.grid.executor.remote.akka.actor - package com.numericalmethod.suanshu.grid.executor.remote.akka.actor

com.numericalmethod.suanshu.grid.executor.remote.akka.message - package com.numericalmethod.suanshu.grid.executor.remote.akka.message

com.numericalmethod.suanshu.grid.executor.remote.akka.serialization - package com.numericalmethod.suanshu.grid.executor.remote.akka.serialization

com.numericalmethod.suanshu.grid.function - package com.numericalmethod.suanshu.grid.function

com.numericalmethod.suanshu.grid.function.random - package com.numericalmethod.suanshu.grid.function.random

com.numericalmethod.suanshu.grid.test - package com.numericalmethod.suanshu.grid.test

com.numericalmethod.suanshu.grid.test.config - package com.numericalmethod.suanshu.grid.test.config

com.numericalmethod.suanshu.interval - package com.numericalmethod.suanshu.interval

com.numericalmethod.suanshu.misc - package com.numericalmethod.suanshu.misc

com.numericalmethod.suanshu.misc.algorithm - package com.numericalmethod.suanshu.misc.algorithm

com.numericalmethod.suanshu.misc.algorithm.bb - package com.numericalmethod.suanshu.misc.algorithm.bb

com.numericalmethod.suanshu.misc.algorithm.iterative - package com.numericalmethod.suanshu.misc.algorithm.iterative

com.numericalmethod.suanshu.misc.algorithm.iterative.monitor - package com.numericalmethod.suanshu.misc.algorithm.iterative.monitor

com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance - package com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance

com.numericalmethod.suanshu.misc.algorithm.stopcondition - package com.numericalmethod.suanshu.misc.algorithm.stopcondition

com.numericalmethod.suanshu.misc.datastructure - package com.numericalmethod.suanshu.misc.datastructure

com.numericalmethod.suanshu.misc.datastructure.time - package com.numericalmethod.suanshu.misc.datastructure.time

com.numericalmethod.suanshu.misc.parallel - package com.numericalmethod.suanshu.misc.parallel

com.numericalmethod.suanshu.model.corvalan2005 - package com.numericalmethod.suanshu.model.corvalan2005

com.numericalmethod.suanshu.model.corvalan2005.constraint - package com.numericalmethod.suanshu.model.corvalan2005.constraint

com.numericalmethod.suanshu.model.corvalan2005.diversification - package com.numericalmethod.suanshu.model.corvalan2005.diversification

com.numericalmethod.suanshu.model.covarianceselection - package com.numericalmethod.suanshu.model.covarianceselection

com.numericalmethod.suanshu.model.covarianceselection.lasso - package com.numericalmethod.suanshu.model.covarianceselection.lasso

com.numericalmethod.suanshu.model.dai2011 - package com.numericalmethod.suanshu.model.dai2011

com.numericalmethod.suanshu.model.daspremont2008 - package com.numericalmethod.suanshu.model.daspremont2008

com.numericalmethod.suanshu.model.elliott2005 - package com.numericalmethod.suanshu.model.elliott2005

com.numericalmethod.suanshu.model.hvolatility - package com.numericalmethod.suanshu.model.hvolatility

com.numericalmethod.suanshu.model.infantino2010 - package com.numericalmethod.suanshu.model.infantino2010

com.numericalmethod.suanshu.model.kst1995 - package com.numericalmethod.suanshu.model.kst1995

com.numericalmethod.suanshu.model.lai2010 - package com.numericalmethod.suanshu.model.lai2010

com.numericalmethod.suanshu.model.lai2010.ceta - package com.numericalmethod.suanshu.model.lai2010.ceta

com.numericalmethod.suanshu.model.lai2010.ceta.maximizer - package com.numericalmethod.suanshu.model.lai2010.ceta.maximizer

com.numericalmethod.suanshu.model.lai2010.ceta.npeb - package com.numericalmethod.suanshu.model.lai2010.ceta.npeb

com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler - package com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler

com.numericalmethod.suanshu.model.lai2010.fit - package com.numericalmethod.suanshu.model.lai2010.fit

com.numericalmethod.suanshu.model.lai2010.optimizer - package com.numericalmethod.suanshu.model.lai2010.optimizer

com.numericalmethod.suanshu.model.returns.moments - package com.numericalmethod.suanshu.model.returns.moments

com.numericalmethod.suanshu.model.volarb - package com.numericalmethod.suanshu.model.volarb

com.numericalmethod.suanshu.number - package com.numericalmethod.suanshu.number

com.numericalmethod.suanshu.number.big - package com.numericalmethod.suanshu.number.big

com.numericalmethod.suanshu.number.complex - package com.numericalmethod.suanshu.number.complex

com.numericalmethod.suanshu.number.doublearray - package com.numericalmethod.suanshu.number.doublearray

com.numericalmethod.suanshu.optimization - package com.numericalmethod.suanshu.optimization

com.numericalmethod.suanshu.optimization.multivariate.constrained - package com.numericalmethod.suanshu.optimization.multivariate.constrained

com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint - package com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint

com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general - package com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general

com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear - package com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activeset - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activeset

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver

com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem

com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box - package com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box

com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod - package com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod

com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset - package com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset

com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint - package com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint

com.numericalmethod.suanshu.optimization.multivariate.constrained.integer - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer

com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce

com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb

com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane

com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem

com.numericalmethod.suanshu.optimization.multivariate.constrained.problem - package com.numericalmethod.suanshu.optimization.multivariate.constrained.problem

com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm

com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim

com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained

com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local

com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid

com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration - package com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration

com.numericalmethod.suanshu.optimization.multivariate.initialization - package com.numericalmethod.suanshu.optimization.multivariate.initialization

com.numericalmethod.suanshu.optimization.multivariate.minmax - package com.numericalmethod.suanshu.optimization.multivariate.minmax

com.numericalmethod.suanshu.optimization.multivariate.unconstrained - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained

com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing

com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction

com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction

com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction

com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2

com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection

com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch

com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton

com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent - package com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent

com.numericalmethod.suanshu.optimization.problem - package com.numericalmethod.suanshu.optimization.problem

com.numericalmethod.suanshu.optimization.univariate - package com.numericalmethod.suanshu.optimization.univariate

com.numericalmethod.suanshu.optimization.univariate.bracketsearch - package com.numericalmethod.suanshu.optimization.univariate.bracketsearch

com.numericalmethod.suanshu.stats.cointegration - package com.numericalmethod.suanshu.stats.cointegration

com.numericalmethod.suanshu.stats.descriptive - package com.numericalmethod.suanshu.stats.descriptive

com.numericalmethod.suanshu.stats.descriptive.correlation - package com.numericalmethod.suanshu.stats.descriptive.correlation

com.numericalmethod.suanshu.stats.descriptive.covariance - package com.numericalmethod.suanshu.stats.descriptive.covariance

com.numericalmethod.suanshu.stats.descriptive.moment - package com.numericalmethod.suanshu.stats.descriptive.moment

com.numericalmethod.suanshu.stats.descriptive.moment.weighted - package com.numericalmethod.suanshu.stats.descriptive.moment.weighted

com.numericalmethod.suanshu.stats.descriptive.rank - package com.numericalmethod.suanshu.stats.descriptive.rank

com.numericalmethod.suanshu.stats.distribution.discrete - package com.numericalmethod.suanshu.stats.distribution.discrete

com.numericalmethod.suanshu.stats.distribution.multivariate - package com.numericalmethod.suanshu.stats.distribution.multivariate

com.numericalmethod.suanshu.stats.distribution.multivariate.exponentialfamily - package com.numericalmethod.suanshu.stats.distribution.multivariate.exponentialfamily

com.numericalmethod.suanshu.stats.distribution.univariate - package com.numericalmethod.suanshu.stats.distribution.univariate

com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily - package com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily

com.numericalmethod.suanshu.stats.dlm.multivariate - package com.numericalmethod.suanshu.stats.dlm.multivariate

com.numericalmethod.suanshu.stats.dlm.univariate - package com.numericalmethod.suanshu.stats.dlm.univariate

com.numericalmethod.suanshu.stats.evt.cluster - package com.numericalmethod.suanshu.stats.evt.cluster

com.numericalmethod.suanshu.stats.evt.evd.bivariate - package com.numericalmethod.suanshu.stats.evt.evd.bivariate

com.numericalmethod.suanshu.stats.evt.evd.univariate - package com.numericalmethod.suanshu.stats.evt.evd.univariate

com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting - package com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting

com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer - package com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer

com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical - package com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical

com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot - package com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot

com.numericalmethod.suanshu.stats.evt.evd.univariate.rng - package com.numericalmethod.suanshu.stats.evt.evd.univariate.rng

com.numericalmethod.suanshu.stats.evt.exi - package com.numericalmethod.suanshu.stats.evt.exi

com.numericalmethod.suanshu.stats.evt.function - package com.numericalmethod.suanshu.stats.evt.function

com.numericalmethod.suanshu.stats.evt.markovchain - package com.numericalmethod.suanshu.stats.evt.markovchain

com.numericalmethod.suanshu.stats.evt.timeseries - package com.numericalmethod.suanshu.stats.evt.timeseries

com.numericalmethod.suanshu.stats.factoranalysis - package com.numericalmethod.suanshu.stats.factoranalysis

com.numericalmethod.suanshu.stats.hmm - package com.numericalmethod.suanshu.stats.hmm

com.numericalmethod.suanshu.stats.hmm.discrete - package com.numericalmethod.suanshu.stats.hmm.discrete

com.numericalmethod.suanshu.stats.hmm.mixture - package com.numericalmethod.suanshu.stats.hmm.mixture

com.numericalmethod.suanshu.stats.hmm.mixture.distribution - package com.numericalmethod.suanshu.stats.hmm.mixture.distribution

com.numericalmethod.suanshu.stats.markovchain - package com.numericalmethod.suanshu.stats.markovchain

com.numericalmethod.suanshu.stats.pca - package com.numericalmethod.suanshu.stats.pca

com.numericalmethod.suanshu.stats.random - package com.numericalmethod.suanshu.stats.random

com.numericalmethod.suanshu.stats.random.rng - package com.numericalmethod.suanshu.stats.random.rng

com.numericalmethod.suanshu.stats.random.rng.concurrent.cache - package com.numericalmethod.suanshu.stats.random.rng.concurrent.cache

com.numericalmethod.suanshu.stats.random.rng.concurrent.context - package com.numericalmethod.suanshu.stats.random.rng.concurrent.context

com.numericalmethod.suanshu.stats.random.rng.multivariate - package com.numericalmethod.suanshu.stats.random.rng.multivariate

com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid - package com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid

com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis - package com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis

com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction - package com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction

com.numericalmethod.suanshu.stats.random.rng.univariate - package com.numericalmethod.suanshu.stats.random.rng.univariate

com.numericalmethod.suanshu.stats.random.rng.univariate.beta - package com.numericalmethod.suanshu.stats.random.rng.univariate.beta

com.numericalmethod.suanshu.stats.random.rng.univariate.exp - package com.numericalmethod.suanshu.stats.random.rng.univariate.exp

com.numericalmethod.suanshu.stats.random.rng.univariate.gamma - package com.numericalmethod.suanshu.stats.random.rng.univariate.gamma

com.numericalmethod.suanshu.stats.random.rng.univariate.normal - package com.numericalmethod.suanshu.stats.random.rng.univariate.normal

com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated - package com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated

com.numericalmethod.suanshu.stats.random.rng.univariate.poisson - package com.numericalmethod.suanshu.stats.random.rng.univariate.poisson

com.numericalmethod.suanshu.stats.random.rng.univariate.uniform - package com.numericalmethod.suanshu.stats.random.rng.univariate.uniform

com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear - package com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear

com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister - package com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister

com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation - package com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation

com.numericalmethod.suanshu.stats.random.sampler.resampler - package com.numericalmethod.suanshu.stats.random.sampler.resampler

com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap - package com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap

com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block - package com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block

com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate - package com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate

com.numericalmethod.suanshu.stats.random.variancereduction - package com.numericalmethod.suanshu.stats.random.variancereduction

com.numericalmethod.suanshu.stats.regression - package com.numericalmethod.suanshu.stats.regression

com.numericalmethod.suanshu.stats.regression.linear - package com.numericalmethod.suanshu.stats.regression.linear

com.numericalmethod.suanshu.stats.regression.linear.glm - package com.numericalmethod.suanshu.stats.regression.linear.glm

com.numericalmethod.suanshu.stats.regression.linear.glm.distribution - package com.numericalmethod.suanshu.stats.regression.linear.glm.distribution

com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection - package com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection

com.numericalmethod.suanshu.stats.regression.linear.glm.quasi - package com.numericalmethod.suanshu.stats.regression.linear.glm.quasi

com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family - package com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family

com.numericalmethod.suanshu.stats.regression.linear.lasso - package com.numericalmethod.suanshu.stats.regression.linear.lasso

com.numericalmethod.suanshu.stats.regression.linear.lasso.lars - package com.numericalmethod.suanshu.stats.regression.linear.lasso.lars

com.numericalmethod.suanshu.stats.regression.linear.logistic - package com.numericalmethod.suanshu.stats.regression.linear.logistic

com.numericalmethod.suanshu.stats.regression.linear.ols - package com.numericalmethod.suanshu.stats.regression.linear.ols

com.numericalmethod.suanshu.stats.regression.linear.panel - package com.numericalmethod.suanshu.stats.regression.linear.panel

com.numericalmethod.suanshu.stats.regression.linear.residualanalysis - package com.numericalmethod.suanshu.stats.regression.linear.residualanalysis

com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random

com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde

com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients

com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete

com.numericalmethod.suanshu.stats.stochasticprocess.timegrid - package com.numericalmethod.suanshu.stats.stochasticprocess.timegrid

com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration

com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration

com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random

com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde

com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients

com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete

com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process

com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou

com.numericalmethod.suanshu.stats.test - package com.numericalmethod.suanshu.stats.test

com.numericalmethod.suanshu.stats.test.distribution - package com.numericalmethod.suanshu.stats.test.distribution

com.numericalmethod.suanshu.stats.test.distribution.kolmogorov - package com.numericalmethod.suanshu.stats.test.distribution.kolmogorov

com.numericalmethod.suanshu.stats.test.distribution.normality - package com.numericalmethod.suanshu.stats.test.distribution.normality

com.numericalmethod.suanshu.stats.test.distribution.pearson - package com.numericalmethod.suanshu.stats.test.distribution.pearson

com.numericalmethod.suanshu.stats.test.mean - package com.numericalmethod.suanshu.stats.test.mean

com.numericalmethod.suanshu.stats.test.rank - package com.numericalmethod.suanshu.stats.test.rank

com.numericalmethod.suanshu.stats.test.rank.wilcoxon - package com.numericalmethod.suanshu.stats.test.rank.wilcoxon

com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity - package com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity

com.numericalmethod.suanshu.stats.test.timeseries.portmanteau - package com.numericalmethod.suanshu.stats.test.timeseries.portmanteau

com.numericalmethod.suanshu.stats.test.variance - package com.numericalmethod.suanshu.stats.test.variance

com.numericalmethod.suanshu.stats.timeseries.datastructure - package com.numericalmethod.suanshu.stats.timeseries.datastructure

com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate - package com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate

com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime - package com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime

com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime - package com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime

com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate - package com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate

com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime - package com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime

com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime - package com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime

com.numericalmethod.suanshu.stats.timeseries.linear.multivariate - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate

com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima

com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess

com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma

com.numericalmethod.suanshu.stats.timeseries.linear.univariate - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch

combination(int, int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
Compute the combination function or binomial coefficient.
combination(int, int) - Static method in class com.numericalmethod.suanshu.number.big.BigIntegerUtils
Compute the combination function or the binomial coefficient.
CombinedCetaMaximizer - Class in com.numericalmethod.suanshu.model.lai2010.ceta.maximizer
Searches the maximum C(η) by an array of given maximizers, being tried in sequence.
CombinedCetaMaximizer(CetaMaximizer[]) - Constructor for class com.numericalmethod.suanshu.model.lai2010.ceta.maximizer.CombinedCetaMaximizer
Constructs a combined maximizer.
CombinedCetaMaximizer() - Constructor for class com.numericalmethod.suanshu.model.lai2010.ceta.maximizer.CombinedCetaMaximizer
Constructs a combined maximizer.
CombinedVectorByRef - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles
For efficiency, this wrapper concatenates two or more vectors by references (without data copying).
CombinedVectorByRef(Vector, Vector, Vector...) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.CombinedVectorByRef

commission(double) - Method in class com.numericalmethod.suanshu.model.dai2011.Dai2011Solver.Builder

commission(double, double) - Method in class com.numericalmethod.suanshu.model.dai2011.Dai2011Solver.Builder

CommonRandomNumbers - Class in com.numericalmethod.suanshu.stats.random.variancereduction
The common random numbers is a variance reduction technique to apply when we are comparing two random systems, e.g., $$E(f(X_1) - g(X_2))$$.
CommonRandomNumbers(UnivariateRealFunction, UnivariateRealFunction, RandomLongGenerator, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.CommonRandomNumbers
Estimates $$E(f(X_1) - g(X_2))$$, where f and g are functions of uniform random variables.
CommonRandomNumbers(UnivariateRealFunction, UnivariateRealFunction, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.CommonRandomNumbers
Estimates $$E(f(X_1) - g(X_2))$$, where f and g are functions of uniform random variables.
CommonRandomNumbers(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.CommonRandomNumbers
Estimate $$E(f(X_1) - g(X_2))$$, where f and g are functions of uniform random variables.
compare(SparseMatrix.Entry, SparseMatrix.Entry) - Method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry.TopLeftFirstComparator

compare(SparseVector.Entry, SparseVector.Entry) - Method in enum com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry.Comparator

compare(Pair, Pair) - Method in class com.numericalmethod.suanshu.analysis.function.tuple.PairComparatorByAbscissaFirst

compare(Pair, Pair) - Method in class com.numericalmethod.suanshu.analysis.function.tuple.PairComparatorByAbscissaOnly

compare(BigDecimal, BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
Compare two BigDecimals up to a precision.
compare(Number, double) - Method in class com.numericalmethod.suanshu.number.complex.Complex

compare(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Compares two doubles up to a precision.
compare(Number, double) - Method in interface com.numericalmethod.suanshu.number.NumberUtils.Comparable
Compare this and that numbers up to a precision.
compare(Number, Number, double) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
Compare two numbers.
compareTo(Pair) - Method in class com.numericalmethod.suanshu.analysis.function.tuple.Pair

compareTo(GraphTraversal.Node<V>) - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.GraphTraversal.Node

compareTo(SortableArray) - Method in class com.numericalmethod.suanshu.misc.datastructure.SortableArray

compareTo(Real) - Method in class com.numericalmethod.suanshu.number.Real

compareTo(BoxConstraints.Bound) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.BoxConstraints.Bound

compareTo(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome

compareTo(PanelData.Row) - Method in class com.numericalmethod.suanshu.stats.regression.linear.panel.PanelData.Row

Complex - Class in com.numericalmethod.suanshu.number.complex
A complex number is a number consisting of a real number part and an imaginary number part.
Complex(double, double) - Constructor for class com.numericalmethod.suanshu.number.complex.Complex
Construct a complex number from the real and imaginary parts.
Complex(double) - Constructor for class com.numericalmethod.suanshu.number.complex.Complex
Construct a complex number from a real number.
ComplexMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype
This is a Complex matrix.
ComplexMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
Construct a Complex matrix.
ComplexMatrix(Complex[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
Construct a Complex matrix.
ComplexMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
Construct a Complex matrix.
CompositeDoubleArrayOperation - Class in com.numericalmethod.suanshu.number.doublearray
It is desirable to have multiple implementations and switch between them for, e.g., performance reason.
CompositeDoubleArrayOperation(CompositeDoubleArrayOperation.ImplementationChooser) - Constructor for class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
Construct a CompositeDoubleArrayOperation by supplying the multiplexing criterion and the multiple DoubleArrayOperations.
CompositeDoubleArrayOperation(int, DoubleArrayOperation, DoubleArrayOperation) - Constructor for class com.numericalmethod.suanshu.number.doublearray.CompositeDoubleArrayOperation
Construct a CompositeDoubleArrayOperation that chooses an implementation by array length.
CompositeDoubleArrayOperation.ImplementationChooser - Interface in com.numericalmethod.suanshu.number.doublearray
Specify which implementation to use.
CompositeLinearCongruentialGenerator - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear
A composite generator combines a number of simple LinearCongruentialGenerator, such as Lehmer, to form one longer period generator by first summing values and then taking modulus.
CompositeLinearCongruentialGenerator(LinearCongruentialGenerator[]) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
Construct a linear congruential generator from some simpler and shorter modulus generators.
compute(Vector, Vector, Vector) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.BlockSplitPointSearch
Searches splitting points in the symmetric tridiagonal matrix.
compute(Matrix) - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion.MatrixNorm

compute() - Method in class com.numericalmethod.suanshu.model.daspremont2008.AhatEstimation

compute(Matrix) - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004
Estimates the covariance matrix for a given matrix Y (each column in Y is a time-series), with the optimal shrinkage parameter computed by the algorithm.
compute(Matrix, double) - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004
Estimates the covariance matrix for a given matrix Y (each column in Y is a time-series), with the given shrinkage parameter.
compute(double[][], double[][], int) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.EpsilonStatisticsCalculator
Compute the statistics.
computeGershgorinIntervals(Vector, Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenBoundUtils
Computes the Gershgorin bounds for all eigenvalues in a symmetric tridiagonal matrix T.
computeOptimalPositions(int) - Method in class com.numericalmethod.suanshu.model.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch

computeOptimalPositions(int) - Method in class com.numericalmethod.suanshu.model.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
Computes the solution to the problem described in Section 3.2 in reference.
computeOptimalPositions(int) - Method in interface com.numericalmethod.suanshu.model.daspremont2008.ExtremalGeneralizedEigenvalueSolver
Computes the solution to the problem described in Section 3.2 in reference.
computeWeightedRSS(ACERFunction.ACERParameter, double[], double[], double[]) - Static method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit
Measure how fit the estimated log-ACER function to the empirical epsilons by weighted sum of squared residuals (RSS).
computeWeights(double[], double[]) - Static method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.NonlinearFit
Compute weights from epsilon values and their corresponding confidence interval half-width.
computeWeightsByPeriodLength(double[][]) - Static method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical.ACERUtils
Compute the weights for periods, proportional to the lengths of the periods.
concat(Vector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
Concatenates an array of vectors into one vector.
concat(Collection<Vector>) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
Concatenates an array of vectors into one vector.
concat(SparseVector...) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
Concatenates an array of sparse vectors into one sparse vector.
concat(double[]...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Concatenate an array of arrays into one array.
concat(LinearConstraints...) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear.LinearConstraints
Concatenate collections of linear constraints into one collection.
concurrency() - Method in interface com.numericalmethod.suanshu.grid.config.local.LocalConfiguration

concurrency - Variable in class com.numericalmethod.suanshu.grid.config.xml.schema.LocalConfig

CONCURRENT_RNORM - Static variable in class com.numericalmethod.suanshu.stats.random.rng.RNGUtils

ConcurrentCachedGenerator<T> - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
A generic wrapper that makes an underlying item generator thread-safe by caching generated items in a concurrently-accessible list.
ConcurrentCachedGenerator(ConcurrentCachedGenerator.Generator<T>, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedGenerator
Creates a new instance which wraps the given item generator and uses a cache of the specified size.
ConcurrentCachedGenerator.Generator<T> - Interface in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
Defines a generic generator of type T.
ConcurrentCachedRLG - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
This is a fast thread-safe wrapper for random long generators.
ConcurrentCachedRLG(RandomLongGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRLG
Constructs a new instance which wraps the given random long generator and uses a cache of the specified size.
ConcurrentCachedRLG(RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRLG
Construct a new instance which wraps the given random long generator and uses a cache which has 1000 entries per available core.
ConcurrentCachedRNG - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
This is a fast thread-safe wrapper for random number generators.
ConcurrentCachedRNG(RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRNG
Constructs a new instance which wraps the given random number generator and uses a cache of the specified size.
ConcurrentCachedRNG(RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRNG
Construct a new instance which wraps the given random number generator and uses a cache which has 8 entries per available core.
ConcurrentCachedRVG - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.cache
This is a fast thread-safe wrapper for random vector generators.
ConcurrentCachedRVG(RandomVectorGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRVG
Constructs a new instance which wraps the given random vector generator and uses a cache of the specified size.
ConcurrentCachedRVG(RandomVectorGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.cache.ConcurrentCachedRVG
Constructs a new instance which wraps the given random vector generator and uses a cache which has 8 entries per available core.
ConcurrentStandardNormalRNG - Class in com.numericalmethod.suanshu.stats.random.rng.univariate.normal

ConcurrentStandardNormalRNG(RandomStandardNormalGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.ConcurrentStandardNormalRNG

ConcurrentStandardNormalRNG() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.univariate.normal.ConcurrentStandardNormalRNG

conditionalCopula(double, double) - Method in interface com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVD
The conditional copula function conditioning on either margin.
conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed

conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic

conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn

conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss

conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic

conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic

conditionalCopula(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic

conditionalForEach(boolean, Iterable<T>, IterationBody<T>) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
Calls forEach only if conditionToParallelize is true.
conditionalForLoop(boolean, int, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
Runs a parallel for-loop only if conditionToParallelize is true.
conditionalForLoop(boolean, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.misc.parallel.ParallelExecutor
Calls conditionalForLoop with increment of 1.
conditionalMean(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Compute the multivariate ARMA conditional mean, given all the lags.
conditionalMean(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Compute the univariate ARMA conditional mean, given all the lags.
conditionalMean(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
Compute the univariate AR1 conditional mean, given the last lag.
ConditionalSumOfSquares - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma
The method Conditional Sum of Squares (CSS) fits an ARIMA model by minimizing the conditional sum of squares.
ConditionalSumOfSquares(double[], int, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Fit an ARIMA model for the observations using CSS.
ConditionalSumOfSquares(double[], int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Fit an ARIMA model for the observations using CSS.
CONDUCTANCE_QUANTUM_G0 - Static variable in class com.numericalmethod.suanshu.misc.PhysicalConstants
The conductance quantum $$G_0$$ in siemens (s).
ConfidenceInterval - Class in com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting
This class stores information for a list of confidence intervals, with the same confidence level.
ConfidenceInterval(double, Vector, Vector, Vector) - Constructor for class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.ConfidenceInterval
Create an instance with the confidence interval information.
confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.EstimateByLogLikelihood
Compute the $$(1 - \alpha)100\%$$ confidence intervals for each element of the fitted parameter, given the required confidence level.
confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.mean.T
Get the confidence interval.
confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.variance.F
Compute the confidence interval.
configuration() - Method in interface com.numericalmethod.suanshu.grid.executor.local.LocalGridExecutor
Gets the configuration that is used by this instance.
configuration() - Method in class com.numericalmethod.suanshu.grid.executor.local.LocalParallelGridExecutor

configuration() - Method in class com.numericalmethod.suanshu.grid.executor.local.ThreadLocalRngGridExecutor

configuration() - Method in class com.numericalmethod.suanshu.grid.executor.remote.akka.AkkaGridExecutor

configuration() - Method in interface com.numericalmethod.suanshu.grid.executor.remote.RemoteGridExecutor
Gets the configuration that is used by this instance.
configWithSlaves(Host[]) - Method in interface com.numericalmethod.suanshu.grid.test.TestRemoteConfigurationFactory
Gets the configuration that uses the given slaves.
configWithSlaves(Host[]) - Method in class com.numericalmethod.suanshu.grid.test.TestRemoteConfigurationFactory.DefaultConfig

CongruentMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation
Given a matrix A and an invertible matrix P, we create the congruent matrix B s.t., B = P'AP
CongruentMatrix(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.CongruentMatrix
Constructs the congruent matrix B = P'AP.
conjugate() - Method in class com.numericalmethod.suanshu.number.complex.Complex
Get the conjugate of the complex number, namely, (a - bi).
A conjugate direction optimization method is performed by using sequential line search along directions that bear a strict mathematical relationship to one another.
Construct a multivariate minimizer using the Conjugate-Gradient method.
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.
Construct a Conjugate Gradient Normal Error (CGNE) solver.
Construct a Conjugate Gradient Normal Error (CGNE) solver.
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.
Construct a Conjugate Gradient Normal Residual method (CGNR) solver.
Construct a Conjugate Gradient Normal Residual method (CGNR) solver.
The Conjugate Gradient method (CG) is useful for solving a symmetric n-by-n linear system.
Construct a Conjugate Gradient (CG) solver.
Construct a Conjugate Gradient (CG) solver.
The Conjugate Gradient Squared method (CGS) is useful for solving a non-symmetric n-by-n linear system.
Construct a Conjugate Gradient Squared (CGS) solver.
Construct a Conjugate Gradient Squared (CGS) solver.
ConstantDriftVector - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
The class represents a constant drift function.
ConstantDriftVector(Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantDriftVector
Construct a constant drift function.
Constants - Class in com.numericalmethod.suanshu.misc
This class lists the global parameters and constants in this SuanShu library.
ConstantSeeder<T extends Seedable> - Class in com.numericalmethod.suanshu.stats.random.rng
A wrapper that seeds each given seedable random number generator with the given seed(s).
ConstantSeeder(Iterable<T>, long...) - Constructor for class com.numericalmethod.suanshu.stats.random.rng.ConstantSeeder
Constructs a wrapper with the underlying RNGs and the seeds for seeding each RNG.
ConstantSigma1 - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
The class represents a constant diffusion coefficient function.
ConstantSigma1(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
Construct a constant diffusion coefficient function.
ConstantSigma2 - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
Deprecated.
This implementation is slow. Use ConstantSigma1 instead.
ConstantSigma2(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
Deprecated.
Construct a constant diffusion coefficient function.
ConstrainedCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell

ConstrainedCellFactory - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained
This defines a Differential Evolution operator that takes in account constraints.
ConstrainedCellFactory(DEOptimCellFactory) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
Construct an instance of a ConstrainedCellFactory that define the constrained Differential Evolution operators.
ConstrainedCellFactory.ConstrainedCell - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained
A ConstrainedCell is a chromosome for a constrained optimization problem.
ConstrainedLASSObyLARS - Class in com.numericalmethod.suanshu.stats.regression.linear.lasso
This class solves the constrained form of LASSO by modified least angle regression (LARS) and linear interpolation: $\min_w \left \{ \left \| Xw - y \right \|_2^2 \right \}\) subject to $$\left \| w \right \|_1 \leq t$ ConstrainedLASSObyLARS(ConstrainedLASSOProblem, boolean, boolean, double, int) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyLARS Solves a constrained LASSO problem by modified least angle regression (LARS) and linear interpolation. ConstrainedLASSObyLARS(ConstrainedLASSOProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyLARS Solves a constrained LASSO problem by modified least angle regression (LARS) and linear interpolation. ConstrainedLASSObyQP - Class in com.numericalmethod.suanshu.stats.regression.linear.lasso This class solves the constrained form of LASSO (i.e.\(\min_w \left \{ \left \| Xw - y \right \|_2^2 \right \}$$ subject to $$\left \| w \right \|_1 \leq t$$) by transforming it into a single quadratic programming problem with (2 * m + 1) constraints, where m is the number of columns of the design matrix.
ConstrainedLASSObyQP(ConstrainedLASSOProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSObyQP
Solves a constrained LASSO problem by transforming it into a single quadratic programming problem.
ConstrainedLASSOProblem - Class in com.numericalmethod.suanshu.stats.regression.linear.lasso
A LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization.
ConstrainedLASSOProblem(Vector, Matrix, double) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSOProblem
Constructs a LASSO problem in the constrained form.
ConstrainedLASSOProblem(ConstrainedLASSOProblem) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.lasso.ConstrainedLASSOProblem
Copy constructor.
ConstrainedMinimizer<P extends ConstrainedOptimProblem,S extends MinimizationSolution<?>> - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained
A constrained minimizer solves a constrained optimization problem, namely, ConstrainedOptimProblem.
ConstrainedOptimProblem - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.problem
A constrained optimization problem takes this form.
ConstrainedOptimProblemImpl1 - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.problem
This implements a constrained optimization problem for a function f subject to equality and less-than-or-equal-to constraints.
ConstrainedOptimProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
Constructs a constrained optimization problem.
ConstrainedOptimProblemImpl1(ConstrainedOptimProblemImpl1) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
Copy constructor.
ConstrainedOptimSubProblem - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained
A constrained optimization sub-problem takes this form.
constraints() - Method in class com.numericalmethod.suanshu.model.corvalan2005.constraint.MinimumWeights

constraints() - Method in class com.numericalmethod.suanshu.model.corvalan2005.constraint.NoConstraints

constraints() - Method in class com.numericalmethod.suanshu.model.corvalan2005.constraint.NoShortSelling

constraints() - Method in interface com.numericalmethod.suanshu.model.corvalan2005.Corvalan2005.WeightsConstraint
Gets the less-than constraints on weights.
Constraints - Interface in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint
A set of constraints for a (real-valued) optimization problem is a set of functions.
constraints - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.MultiplierPenalty
the constraint/cost functions
ConstraintsUtils - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint
These are the utility functions for manipulating Constraints.
ConstraintsUtils() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.ConstraintsUtils

ConstraintViolationException() - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.ConstraintViolationException
Constructs a ConstraintViolationException.
ConstraintViolationException(String) - Constructor for exception com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.ConstraintViolationException
Constructs a ConstraintViolationException with an error message.
containActive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
Check if the active set contains a certain index.
containInactive(int) - Method in class com.numericalmethod.suanshu.misc.algorithm.ActiveSet
Check if the inactive set contains a certain index.
contains(UndirectedEdge<V>) - Method in class com.numericalmethod.suanshu.graph.community.EdgeBetweeness
Checks if the graph contains an edge.
contains(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseGraph
Check if this graph contains a vertex.
contains(Object) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet

containsAll(Collection<?>) - Method in class com.numericalmethod.suanshu.misc.datastructure.IdentityHashSet

containsEdge(Graph<V, ?>, HyperEdge<V>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
Returns true if this graph's edge collection contains e
containsVertex(Graph<V, ?>, V) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
Returns true if this graph's vertex collection contains v
ContextRNG<T> - Class in com.numericalmethod.suanshu.stats.random.rng.concurrent.context
This uniform number generator generates independent sequences of random numbers per context.
ContextRNG() - Constructor for class com.numericalmethod.suanshu.stats.random.rng.concurrent.context.ContextRNG

ContinuedFraction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
A continued fraction representation of a number has this form: $z = b_0 + \cfrac{a_1}{b_1 + \cfrac{a_2}{b_2 + \cfrac{a_3}{b_3 + \cfrac{a_4}{b_4 + \ddots\,}}}}$ ai and bi can be functions of x, which in turn makes z a function of x.
ContinuedFraction(ContinuedFraction.Partials, double, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
Construct a continued fraction.
ContinuedFraction(ContinuedFraction.Partials, int, int) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
Construct a continued fraction.
ContinuedFraction(ContinuedFraction.Partials) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.univariate.ContinuedFraction
Construct a continued fraction.
ContinuedFraction.MaxIterationsExceededException - Exception in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
RuntimeException thrown when the continued fraction fails to converge for a given epsilon before a certain number of iterations.
ContinuedFraction.Partials - Interface in com.numericalmethod.suanshu.analysis.function.rn2r1.univariate
This interface defines a continued fraction in terms of the partial numerators an, and the partial denominators bn.
ControlVariates - Class in com.numericalmethod.suanshu.stats.random.variancereduction
Control variates method is a variance reduction technique that exploits information about the errors in estimates of known quantities to reduce the error of an estimate of an unknown quantity.
ControlVariates(UnivariateRealFunction, UnivariateRealFunction, double, double, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.variancereduction.ControlVariates

ControlVariates.Estimator - Interface in com.numericalmethod.suanshu.stats.random.variancereduction

ConvectionDiffusionEquation1D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
The convectionâ€“diffusion equation is a combination of the diffusion and convection (advection) equations, and describes physical phenomena where particles, energy, or other physical quantities are transferred inside a physical system due to two processes: diffusion and convection.
ConvectionDiffusionEquation1D(BivariateRealFunction, BivariateRealFunction, BivariateRealFunction, double, double, UnivariateRealFunction, double, UnivariateRealFunction, double, UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Constructs a convection-diffusion equation problem.
ConvergenceFailure - Exception in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
ConvergenceFailure(ConvergenceFailure.Reason) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
Construct an exception with reason.
ConvergenceFailure(ConvergenceFailure.Reason, String) - Constructor for exception com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
Construct an exception with reason and error message.
ConvergenceFailure.Reason - Enum in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
the reasons for the convergence failure
convertToDerivativeFunction(RealVectorFunction, int) - Static method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Converts the given vector function to a first order derivative function.
cookDistances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMDiagnostics
Cook distances.
coordinates - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
the coordinates of this entry
copy2D(double[][]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Copies a 2D array.
correct(DerivativeFunction, double, double[], Vector[]) - Method in interface com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector

correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1

correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2

correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3

correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4

correct(DerivativeFunction, double, double[], Vector[]) - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5

correlation() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
Get the correlation, i.e., Pearson's correlation coefficient.
CorrelationMatrix - Class in com.numericalmethod.suanshu.stats.descriptive.correlation
The correlation matrix of n random variables X1, ..., Xn is the n × n matrix whose i,j entry is corr(Xi, Xj), the correlation between X1 and Xn.
CorrelationMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.correlation.CorrelationMatrix
Construct a correlation matrix from a covariance matrix.
Corvalan2005 - Class in com.numericalmethod.suanshu.model.corvalan2005
This paper tackles the corner solution problem of many portfolio optimizers, by optimizing the portfolio diversification with some relaxation on the volatility σ and the expected return R of a given optimized (but non-diversified) portfolio.
Corvalan2005(double, double) - Constructor for class com.numericalmethod.suanshu.model.corvalan2005.Corvalan2005
Constructs an instance of the Corvalan model.
Corvalan2005(Minimizer<? super ConstrainedOptimProblem, IterativeSolution<Vector>>, DiversificationMeasure, double, double) - Constructor for class com.numericalmethod.suanshu.model.corvalan2005.Corvalan2005
Constructs an instance of the Corvalan model.
Corvalan2005.WeightsConstraint - Interface in com.numericalmethod.suanshu.model.corvalan2005
Constraints on weights which are defined by a set of less-than constraints.
cos(Vector) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the cosine of a vector, element-by-element.
cos(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
Cosine of a complex number.
cosec(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the cosecant of an angle.
cosh(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
Hyperbolic cosine of a complex number.
cost() - Method in class com.numericalmethod.suanshu.graph.type.SimpleArc

cost() - Method in class com.numericalmethod.suanshu.graph.type.SimpleEdge

cost() - Method in interface com.numericalmethod.suanshu.graph.WeightedEdge
Gets the cost or weight of this edge.
COST - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

cot(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the cotangent of an angle.
coth(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the hyperbolic cotangent of a hyperbolic angle.
count(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCount
Counts the number of eigenvalues that are less than a given value x.
count(double, double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCountInRange
Counts the number of eigenvalues of T that are in the given interval.
count(double) - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.SturmCount
Computes the Sturm count.
count(double) - Method in class com.numericalmethod.suanshu.combinatorics.Counter
Get the count, i.e., the number of occurrences, of a particular number.
countEntriesInEachColumn(List<SparseMatrix.Entry>, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Counts the number of entries in each column.
countEntriesInEachRow(List<SparseMatrix.Entry>, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Counts the number of entries in each row.
Counter - Class in com.numericalmethod.suanshu.combinatorics
A counter keeps track of the number of occurrences of numbers.
Counter() - Constructor for class com.numericalmethod.suanshu.combinatorics.Counter
Construct a counter with no rounding.
Counter(int) - Constructor for class com.numericalmethod.suanshu.combinatorics.Counter
Construct a counter.
CountMonitor<S> - Class in com.numericalmethod.suanshu.misc.algorithm.iterative.monitor
This IterationMonitor counts the number of iterates generated, hence the number of iterations.
CountMonitor() - Constructor for class com.numericalmethod.suanshu.misc.algorithm.iterative.monitor.CountMonitor

CourantPenalty - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod
This penalty function sums up the squared error penalties.
CourantPenalty(EqualityConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.CourantPenalty
Construct a CourantPenalty penalty function from a collection of equality constraints.
CourantPenalty(EqualityConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.CourantPenalty
Construct a CourantPenalty penalty function from a collection of equality constraints.
CourantPenalty(EqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.CourantPenalty
Construct a CourantPenalty penalty function from a collection of equality constraints.
cov() - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.AntitheticVariates

cov() - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.CommonRandomNumbers
Gets the covariance between f and g.
cov() - Method in class com.numericalmethod.suanshu.stats.random.variancereduction.ControlVariates

covariance() - Method in interface com.numericalmethod.suanshu.model.covarianceselection.CovarianceSelectionSolver
Get the estimated Covariance matrix of the selection problem.
covariance() - Method in class com.numericalmethod.suanshu.model.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
Gets the estimated covariance matrix.
covariance() - Method in class com.numericalmethod.suanshu.model.covarianceselection.lasso.CovarianceSelectionLASSO
Get the estimated covariance matrix.
covariance() - Method in class com.numericalmethod.suanshu.model.daspremont2008.CovarianceEstimation

Covariance - Class in com.numericalmethod.suanshu.stats.descriptive.covariance
Covariance is a measure of how much two variables change together.
Covariance() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
Construct an empty Covariance calculator.
Covariance(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
Construct a Covariance calculator, initialized with two samples.
Covariance(Covariance) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.covariance.Covariance
Copy constructor.
covariance() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution

covariance() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution

covariance() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution

covariance() - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
Gets the covariance matrix of this distribution.
covariance() - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution

covariance() - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.AbstractBivariateEVD

covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMBeta

covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMBeta

covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LMBeta
Gets the covariance matrix of the coefficient estimates, β^.
covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticBeta

covariance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSBeta

covariance(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
Get the covariance matrix for prediction errors for $$\hat{x}_{n+1}$$, made at time n.
covariance(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
Get the covariance matrix for prediction errors for $$\hat{x}_{n+1}$$, made at time n.
covariance(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
Get the covariance matrix for prediction errors at time t for x^t+1.
covariance() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
Get the asymptotic covariance matrix of the estimators.
covariance() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Get the asymptotic covariance matrix of the estimated parameters, φ and θ.
CovarianceEstimation - Class in com.numericalmethod.suanshu.model.daspremont2008
Estimates the covariance matrix by maximum likelihood.
CovarianceEstimation(Matrix, double) - Constructor for class com.numericalmethod.suanshu.model.daspremont2008.CovarianceEstimation
Solves the maximum likelihood problem for covariance selection.
covarianceMatrix() - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.EstimateByLogLikelihood
Get the covariance matrix, which is estimated as the inverse of negative Hessian matrix of the log-likelihood function valued at the fitted parameter.
CovarianceSelectionGLASSOFAST - Class in com.numericalmethod.suanshu.model.covarianceselection.lasso
GLASSOFAST is the Graphical LASSO algorithm to solve the covariance selection problem.
CovarianceSelectionGLASSOFAST(CovarianceSelectionProblem) - Constructor for class com.numericalmethod.suanshu.model.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
Solves the maximum likelihood problem for covariance selection.
CovarianceSelectionLASSO - Class in com.numericalmethod.suanshu.model.covarianceselection.lasso
The LASSO approach of covariance selection.
CovarianceSelectionLASSO(CovarianceSelectionProblem, double) - Constructor for class com.numericalmethod.suanshu.model.covarianceselection.lasso.CovarianceSelectionLASSO
Estimate the covariance matrix directly by using LASSO.
CovarianceSelectionLASSO(CovarianceSelectionProblem) - Constructor for class com.numericalmethod.suanshu.model.covarianceselection.lasso.CovarianceSelectionLASSO
Estimate the covariance matrix directly by using LASSO.
CovarianceSelectionProblem - Class in com.numericalmethod.suanshu.model.covarianceselection
This class defines the covariance selection problem outlined in d'Aspremont (2008).
CovarianceSelectionProblem(Matrix, double) - Constructor for class com.numericalmethod.suanshu.model.covarianceselection.CovarianceSelectionProblem
Construct a covariance selection problem.
CovarianceSelectionProblem(MultivariateTimeSeries, double, boolean) - Constructor for class com.numericalmethod.suanshu.model.covarianceselection.CovarianceSelectionProblem
Construct a covariance selection problem from a multivariate time series.
CovarianceSelectionProblem(MultivariateTimeSeries, double) - Constructor for class com.numericalmethod.suanshu.model.covarianceselection.CovarianceSelectionProblem
Construct a covariance selection problem from a multivariate time series.
CovarianceSelectionProblem(CovarianceSelectionProblem) - Constructor for class com.numericalmethod.suanshu.model.covarianceselection.CovarianceSelectionProblem
Copy constructor.
CovarianceSelectionSolver - Interface in com.numericalmethod.suanshu.model.covarianceselection

covers(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the coversed sine or coversine of an angle.
Cr - Variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
the crossover probability
CramerVonMises2Samples - Class in com.numericalmethod.suanshu.stats.test.distribution
This algorithm calculates the two sample Cramer-Von Mises test statistic and p-value.
CramerVonMises2Samples(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.CramerVonMises2Samples
Calculate the statistics and p-value of two sample Cramer-Von Mises test.
CrankNicolsonConvectionDiffusionEquation1D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
This class uses the Crank-Nicolson scheme to obtain a numerical solution of a one-dimensional convection-diffusion PDE.
CrankNicolsonConvectionDiffusionEquation1D() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D
Constructs a Crank-Nicolson solver for a 1 dimensional convection-diffusion PDE.
CrankNicolsonConvectionDiffusionEquation1D.Coefficients - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
Gets the coefficients of a discretized 1D convection-diffusion equation for each time step.
CrankNicolsonHeatEquation1D - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
The Crank-Nicolson method is an algorithm for obtaining a numerical solution to parabolic PDE problems.
CrankNicolsonHeatEquation1D() - Constructor for class com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D

CrankNicolsonHeatEquation1D.Coefficients - Class in com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
Gets the coefficients of a discretized 1D heat equation for each time step.
createActorSystem(String) - Static method in class com.numericalmethod.suanshu.grid.executor.remote.akka.AkkaUtils
A way to create an actor system that explicitly specifies the classloader.
createCustomRoute(RouteeProvider) - Method in class com.numericalmethod.suanshu.grid.executor.remote.akka.actor.GridRouterConfig

createDynamicCreatorConfig() - Method in class com.numericalmethod.suanshu.grid.config.xml.schema.ObjectFactory
createFailureDetectionConfig() - Method in class com.numericalmethod.suanshu.grid.config.xml.schema.ObjectFactory
createGrid(GridConfig) - Method in class com.numericalmethod.suanshu.grid.config.xml.schema.ObjectFactory
Create an instance of JAXBElement<GridConfig>}
createGridConfig() - Method in class com.numericalmethod.suanshu.grid.config.xml.schema.ObjectFactory
Create an instance of GridConfig
createLocalConfig() - Method in class com.numericalmethod.suanshu.grid.config.xml.schema.ObjectFactory
Create an instance of LocalConfig
createRemoteConfig() - Method in class com.numericalmethod.suanshu.grid.config.xml.schema.ObjectFactory
Create an instance of RemoteConfig
createRngConfig() - Method in class com.numericalmethod.suanshu.grid.config.xml.schema.ObjectFactory
Create an instance of RngConfig
createSlaveConfig() - Method in class com.numericalmethod.suanshu.grid.config.xml.schema.ObjectFactory
Create an instance of SlaveConfig
createSlavesConfig() - Method in class com.numericalmethod.suanshu.grid.config.xml.schema.ObjectFactory
Create an instance of SlavesConfig
crossover(Chromosome) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.Chromosome
Construct a Chromosome by crossing over a pair of chromosomes.
crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell

crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell

crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell

crossover(Chromosome) - Method in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
Crossover by taking the midpoint.
csch(double) - Static method in class com.numericalmethod.suanshu.geometry.TrigMath
Returns the hyperbolic cosecant of a hyperbolic angle.
CSDPMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing
Implements the CSDP algorithm for semidefinite programming problem with equality constraints.
CSDPMinimizer(double, double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
CSDPMinimizer(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
CSDPMinimizer.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing

CSRSparseMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse
The Compressed Sparse Row (CSR) format for sparse matrix has this representation: (value, col_ind, row_ptr).
CSRSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix in CSR format.
CSRSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix in CSR format.
CSRSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix in CSR format by a list of non-zero entries.
CSRSparseMatrix(int, int, List<SparseMatrix.Entry>, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix in CSR format by a list of non-zero entries.
CSRSparseMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix from a matrix.
CSRSparseMatrix(CSRSparseMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Copy constructor.
CSV_SEPARATOR - Static variable in class com.numericalmethod.suanshu.number.DoubleUtils
The default separator for CSV file parsing.
Ctor2x2(double, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Same as new GivensMatrix(2, 1, 2, c, s).
CtorFromRho(int, int, int, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix from ρ.
CtorToRotateColumns(int, int, int, double, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix such that [a b] * G = [* 0].
CtorToRotateRows(int, int, int, double, double) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix such that G * [a b]t = [* 0]t.
CtorToZeroOutEntry(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix such that G * A has 0 in the [i,j] entry.
CtorToZeroOutEntryByTranspose(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix such that Gt * A has 0 in the [i,j] entry.
CubicHermite - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
Cubic Hermite spline interpolation is a piecewise spline interpolation, in which each polynomial is in Hermite form which consists of two control points and two control tangents.
CubicHermite() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicHermite
Construct an instance with CubicHermite.Tangents.CATMULL_ROM as the method for computing tangents.
CubicHermite(CubicHermite.Tangent) - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicHermite
Construct an instance with the given method to compute tangents.
CubicHermite.Tangent - Interface in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
The method for computing the control tangent at a given index.
CubicHermite.Tangents - Enum in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate

CubicRoot - Class in com.numericalmethod.suanshu.analysis.function.polynomial.root
This is a cubic equation solver.
CubicRoot() - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.root.CubicRoot

CubicSpline - Class in com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate
The (natural) cubic spline interpolation fits a cubic polynomial between each pair of adjacent points such that adjacent cubics are continuous in their first and second derivative.
CubicSpline() - Constructor for class com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate.CubicSpline

cumsum(Vector[]) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
Gets the cumulative sums.
cumsum(double[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Gets the cumulative sums of the elements in an array.
cumsum(int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Gets the cumulative sums of the elements in an array.
cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial

cumulant(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMExponentialDistribution
The cumulant function of the exponential distribution.
cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma

cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian

cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian

cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson

CumulativeNormalHastings - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
Hastings algorithm is faster but less accurate way to compute the cumulative standard Normal.
CumulativeNormalHastings() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalHastings

CumulativeNormalInverse - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian
The inverse of the cumulative standard Normal distribution function is defined as: $N^{-1}(u) /] This implementation uses the Beasley-Springer-Moro algorithm. CumulativeNormalInverse() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalInverse CumulativeNormalMarsaglia - Class in com.numericalmethod.suanshu.analysis.function.special.gaussian Marsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal. CumulativeNormalMarsaglia() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.gaussian.CumulativeNormalMarsaglia cumulativeProportionVar() - Method in interface com.numericalmethod.suanshu.stats.pca.PCA Gets the cumulative proportion of overall variance explained by the principal components CurveFitting - Interface in com.numericalmethod.suanshu.analysis.curvefit Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints. cut(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer.MyCutter cut(SimplexTable) - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer.MyCutter cut(SimplexTable) - Method in interface com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer.CutterFactory.Cutter Cut a simplex table. ## D D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition Get the diagonal matrix D as in Q * D * Q' = A. D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition The diagonal entries of the diagonal matrix D. D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm Gets the D matrix as in the real Schur canonical form Q'AQ = D. D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3 D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3 D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD D() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition Get the D matrix as in SVD decomposition. D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD Returns the matrix D as in A=UDV'. D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.LDLt Get D the the diagonal matrix in the LDL decomposition. D() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies Gets the diagonal matrix D in the LDL decomposition. D() - Method in class com.numericalmethod.suanshu.model.elliott2005.Elliott2005DLM Gets D as in eq. D() - Method in class com.numericalmethod.suanshu.model.infantino2010.Infantino2010PCA.Signal d() - Method in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint Gets d. D() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMNewtonRaphson Computes D. d() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima.VARIMAXModel Get the order of integration. d() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima.ARIMAXModel Get the order of integration. DAgostino - Class in com.numericalmethod.suanshu.stats.test.distribution.normality D'Agostino's K2 test is a goodness-of-fit measure of departure from normality. DAgostino(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino Perform D'Agostino's test to test for the departure from normality. DAGraph<V,E extends Arc<V>> - Interface in com.numericalmethod.suanshu.graph A directed acyclic graph (DAG), is a directed graph with no directed cycles. Dai2011HMM - Class in com.numericalmethod.suanshu.model.dai2011 Creates a two-state Geometric Brownian Motion with a constant volatility. Dai2011HMM(double, double, double, double, double) - Constructor for class com.numericalmethod.suanshu.model.dai2011.Dai2011HMM Constructs a two-state Markov switching Geometric Brownian Motion. Dai2011HMM(Dai2011HMM.ModelParam) - Constructor for class com.numericalmethod.suanshu.model.dai2011.Dai2011HMM Dai2011HMM(Dai2011HMM) - Constructor for class com.numericalmethod.suanshu.model.dai2011.Dai2011HMM Copy constructor. Dai2011HMM.CalibrationParam - Class in com.numericalmethod.suanshu.model.dai2011 Dai2011HMM.ModelParam - Class in com.numericalmethod.suanshu.model.dai2011 Dai2011Solver - Class in com.numericalmethod.suanshu.model.dai2011 Solves the stochastic control problem in the referenced paper to get the two thresholds. Dai2011Solver.Boundaries - Class in com.numericalmethod.suanshu.model.dai2011 Dai2011Solver.Builder - Class in com.numericalmethod.suanshu.model.dai2011 dampedBFGSHessianUpdate(Matrix, Vector, Vector) - Static method in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer Damped BFGS Hessian update. data() - Method in class com.numericalmethod.suanshu.graph.type.VertexTree data() - Method in class com.numericalmethod.suanshu.grid.executor.remote.akka.message.Work DATE_FORMAT_STRING - Static variable in class com.numericalmethod.suanshu.misc.license.License Date format for all kinds of dates in a license file. DateTimeGenericTimeSeries<V> - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure This is a generic time series where time is indexed by DateTime and value can be any type. DateTimeGenericTimeSeries(DateTime[], V[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.DateTimeGenericTimeSeries Construct a time series. DateTimeGenericTimeSeries.Entry<V> - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure This is the TimeSeries.Entry for a DateTime -indexed time series. DateTimeTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate This is a time series has its double values indexed by DateTime. DateTimeTimeSeries(DateTime[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.DateTimeTimeSeries Construct a time series from DateTime and double. DateTimeTimeSeries(ArrayList<DateTime>, ArrayList<Double>) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.DateTimeTimeSeries Construct a time series from DateTime and double. dB(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random.MultivariateRandomProcess Get a Brownian motion increment. dB(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration Get the Brownian increment at the i-th time point. dB(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random.RandomProcess Get a Brownian motion increment. db(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete.MilsteinSDE \[ \frac{d\sigma}{dt}$
DBeta - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This is the first order derivative function of the Beta function w.r.t x, $${\partial \over \partial x} \mathrm{B}(x, y)$$.
DBeta() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBeta

DBetaRegularized - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This is the first order derivative function of the Regularized Incomplete Beta function, BetaRegularized, w.r.t the upper limit, x.
DBetaRegularized(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBetaRegularized
Construct the derivative function of the Regularized Incomplete Beta function, BetaRegularized.
dBt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration.Filtration
Get all the Brownian increments.
dcSeeds() - Method in class com.numericalmethod.suanshu.grid.config.dc.DefaultDynamicCreatorConfiguration

dcSeeds() - Method in interface com.numericalmethod.suanshu.grid.config.dc.DynamicCreatorConfiguration
Gets the seeds to be used by the dynamic creator algorithm to compute MT parameters.
dcSeeds - Variable in class com.numericalmethod.suanshu.grid.config.xml.schema.DynamicCreatorConfig

dcSeeds() - Method in class com.numericalmethod.suanshu.grid.executor.remote.akka.message.InitDynamicCreator

ddy() - Method in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative
Gets y'' = F(x,y).
DeBest2BinCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell

deColumnMean(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the de-mean (column means) matrix of a given matrix.
deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.ImmutableMatrix
Make a deep copy of the underlying matrix.
deepCopy() - Method in interface com.numericalmethod.suanshu.algebra.linear.matrix.doubles.Matrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.DiagonalSum

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
Return this as this Matrix is immutable.
deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.SubMatrixRef
Returns this as the reference is immutable.
deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.CombinedVectorByRef

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.ImmutableVector

deepCopy() - Method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.SubVectorRef

deepCopy() - Method in interface com.numericalmethod.suanshu.algebra.linear.vector.doubles.Vector

deepCopy() - Method in interface com.numericalmethod.suanshu.misc.DeepCopyable
The implementation returns an instance created from this by the copy constructor of the class, or just this if the instance itself is immutable.
deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFt

deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.MultivariateFtWt

deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft

deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt

DeepCopyable - Interface in com.numericalmethod.suanshu.misc
This interface provides a way to do polymorphic copying.
DEFAULT_CACHE_SIZE - Static variable in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
The default cache size = the number of available processors × 1000.
DEFAULT_CACHE_SIZE - Static variable in class com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.CaseResamplingReplacement
The default cache size = the number of available processors × 1000.
DEFAULT_CONFIG_PATH - Static variable in class com.numericalmethod.suanshu.grid.executor.DefaultGridExecutorFactory
The path (relative to the classpath), where the framework looks for the configuration by default.
DEFAULT_EPSILON - Static variable in class com.numericalmethod.suanshu.model.lai2010.ceta.maximizer.BrentCetaMaximizer

DEFAULT_GRID_SIZE - Static variable in class com.numericalmethod.suanshu.model.lai2010.ceta.maximizer.GridSearchCetaMaximizer

DEFAULT_INITIAL_TEMPERATURE - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
the default initial temperature
DEFAULT_MATRIX_SIZE_THRESHOLD - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
The default matrix size threshold.
DEFAULT_MAXIMUM_ITERATIONS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit

DEFAULT_MERSENNE_EXPONENT - Static variable in class com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher

DEFAULT_METHOD - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.SVD
The default algorithm for computing SVD.
DEFAULT_MIN_RELATIVE_GAP - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
Default value for the minimum relative gap threshold.
DEFAULT_MT_PERIOD - Static variable in class com.numericalmethod.suanshu.grid.executor.remote.akka.AkkaGridExecutor
The period of the MersenneTwister instance that are used by slaves.
DEFAULT_NLAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
the default number of lags
DEFAULT_NUMBER_OF_LAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
the default number of lags
DEFAULT_NUMBER_OF_LAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
the default number of lags
DEFAULT_PENALTY_FUNCTION_FACTORY - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
the default penalty function factory
DEFAULT_QA - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
the default acceptance parameter
DEFAULT_QV - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
the default visiting parameter
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_SAFETY_FACTOR - Static variable in class com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
Default value for the safety factor γ.
DEFAULT_SIGMA - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint.AntoniouLu2007
the default value of the centering parameter
DEFAULT_STABLE_ITERATION_COUNT - Static variable in class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer

DEFAULT_SYSTEM_NAME - Static variable in class com.numericalmethod.suanshu.grid.executor.remote.akka.AkkaUtils
The default name for a system.
DEFAULT_THRESHOLD - Static variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
The default tolerance parameter tol.
DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.AbsoluteTolerance
default tolerance
DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance.RelativeTolerance
default tolerance
DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit

DefaultConfig(boolean) - Constructor for class com.numericalmethod.suanshu.grid.test.TestRemoteConfigurationFactory.DefaultConfig

DefaultDeflationCriterion - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
The default deflation criterion is to use eq.
DefaultDeflationCriterion() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
Constructs the default deflation criterion.
DefaultDeflationCriterion(double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
Constructs the default deflation criterion.
DefaultDeflationCriterion(DefaultDeflationCriterion.MatrixNorm) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
Constructs the default deflation criterion, with the algorithm for computing matrix norm for the matrix argument in isNegligible().
DefaultDeflationCriterion(double, DefaultDeflationCriterion.MatrixNorm) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
Constructs the default deflation criterion, with the algorithm for computing matrix norm for the matrix argument in isNegligible().
DefaultDeflationCriterion.MatrixNorm - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
Computes the norm of a given matrix.
DefaultDynamicCreatorConfiguration - Class in com.numericalmethod.suanshu.grid.config.dc
DefaultDynamicCreatorConfiguration() - Constructor for class com.numericalmethod.suanshu.grid.config.dc.DefaultDynamicCreatorConfiguration

defaultFailureDetectorProps() - Static method in class com.numericalmethod.suanshu.grid.executor.remote.akka.ActorProps

DefaultGridExecutorFactory - Class in com.numericalmethod.suanshu.grid.executor
The default factory that creates instances of GridExecutor.
DefaultMatrixStorage - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype
There are multiple ways to implement the storage data structure depending on the matrix type for optimization purpose.
DefaultMatrixStorage(MatrixAccess) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
Construct a DefaultMatrixStorage to wrap a storage for access.
DefaultRoot() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.DefaultRoot

DefaultSimplex - Class in com.numericalmethod.suanshu.optimization.multivariate.initialization
A simplex optimization algorithm, e.g., Nelder-Mead, requires an initial simplex to start the search.
DefaultSimplex(double) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.initialization.DefaultSimplex
Construct a simplex builder.
DefaultSimplex() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.initialization.DefaultSimplex
Construct a simplex builder.
DefaultTestRemoteConfiguration - Class in com.numericalmethod.suanshu.grid.test.config
Simple remote configuration that replaces everything but the hosts with the default remote configuration - similar to leaving out the relevant elements in an XML configuration files.
DefaultTestRemoteConfiguration(Host...) - Constructor for class com.numericalmethod.suanshu.grid.test.config.DefaultTestRemoteConfiguration
Creates a test configuration with a list of host information.
definingVector() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector

definingVector() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Get the Householder defining vector which is orthogonal to the Householder hyperplane.
Deflation - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
A deflation found in a Hessenberg (or tridiagonal in symmetric case) matrix.
DeflationCriterion - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr
Determines whether a sub-diagonal entry is sufficiently small to be neglected.
deflationCriterion - Variable in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg

degree() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
Get the degree of this polynomial.
deleteCol(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
Deletes column i.
deleteRow(int) - Method in class com.numericalmethod.suanshu.misc.datastructure.FlexibleTable
Deletes row i.
delta - Variable in class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

delta() - Method in class com.numericalmethod.suanshu.stats.descriptive.covariance.LedoitWolf2004.Result
Gets the shrinkage parameter δ.
deltaX() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
Return the distance between two adjacent points along the x-axis.
deltaX(int) - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
Get the distance between two adjacent points along the axis with the given index.
deltaY() - Method in class com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
Return the distance between two adjacent points along the y-axis.
DenseData - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense
This implementation of the storage of a dense matrix stores the data of a 2D matrix as an 1D array.
DenseData(double[], int, int, DoubleArrayOperation) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Construct a storage, and specify the implementations of the element-wise operations.
DenseData(double[], int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Construct a storage.
DenseMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense
This class implements the standard, dense, double based matrix representation.
DenseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Constructs a 0 matrix of dimension nRows * nCols.
DenseMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Constructs a matrix from a 2D double[][] array.
DenseMatrix(double[], int, int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Constructs a matrix from a 1D double[].
DenseMatrix(Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Constructs a column matrix from a vector.
DenseMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Converts any matrix to the standard matrix representation.
DenseMatrix(DenseMatrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Copy constructor performing a deep copy.
DenseMatrix(DenseMatrix, boolean) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
This constructor is useful for subclass to pass in computed value.
DenseMatrixMultiplication - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
Matrix operation that multiplies two matrices.
DenseMatrixMultiplicationByBlock - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication

DenseMatrixMultiplicationByBlock() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock

DenseMatrixMultiplicationByBlock(DenseMatrixMultiplicationByBlock.BlockAlgorithm) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock

DenseMatrixMultiplicationByBlock.BlockAlgorithm - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication

DenseMatrixMultiplicationByIjk - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
Implements the naive IJK algorithm.
DenseMatrixMultiplicationByIjk() - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByIjk

DenseVector - Class in com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense
This class implements the standard, dense, double based vector representation.
DenseVector(int) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector.
DenseVector(int, double) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by repeating a value.
DenseVector(double...) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by a double[].
DenseVector(Collection<Double>) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by a collection, with order defined by its iterator.
DenseVector(int[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by a int[].
DenseVector(Matrix) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector from a column or row matrix.
DenseVector(Vector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
Casts any vector to a DenseVector.
DenseVector(DenseVector) - Constructor for class com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense.DenseVector
Copy constructor.
Densifiable - Interface in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense
This interface specifies whether a matrix implementation can be efficiently converted to the standard dense matrix representation.
density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.AbstractBivariateProbabilityDistribution

density(double, double) - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.BivariateProbabilityDistribution
The joint distribution density $$f_{X_1,X_2}(x_1,x_2)$$.
density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.DirichletDistribution

density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultinomialDistribution

density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateNormalDistribution

density(Vector) - Method in interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution
The density function, which, if exists, is the derivative of F.
density(Vector) - Method in class com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateTDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BinomialDistribution
This is the probability mass function.
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
This is the probability mass function for the discrete sample.
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.LogNormalDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.PoissonDistribution

density(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.ProbabilityDistribution
The density function, which, if exists, is the derivative of F.
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TriangularDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TruncatedNormalDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution

density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed

density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic

density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn

density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss

density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic

density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic

density(double, double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic

density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedEVD
The density function, which, if exists, is the derivative of F.
density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.GeneralizedParetoDistribution
The probability density function $f(x; \mu,\sigma,\xi) = \begin{cases} \frac{1}{\sigma}\left(1+ \frac{\xi (x-\mu)}{\sigma}\right)^{\left(-\frac{1}{\xi} - 1\right)} & \text{for} \; \xi \neq 0, \\ \frac{1}{\sigma}\exp \left(-\frac{x-\mu}{\sigma}\right) & \text{for} \; \xi = 0 \end{cases}$ for $$x \ge \mu$$ when $$\xi \ge 0$$, and $$\mu \le x \le \mu - \sigma /\xi$$ when $$\xi <0$$.
density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MaximaDistribution
The probability density function.
density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.MinimaDistribution
The probability density function.
density(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.univariate.OrderStatisticsDistribution

density(int, double) - Method in class com.numericalmethod.suanshu.stats.hmm.discrete.DiscreteHMM
Gets the (conditional) probability mass of making an observation in a particular state.
density(int, double) - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel
Gets the (conditional) probability density/mass of making an observation in a particular state.
density(int, double) - Method in class com.numericalmethod.suanshu.stats.hmm.mixture.MixtureHMM

density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated.
density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
density(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution

density(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution

DEOptim - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
Differential Evolution (DE) is a global optimization method.
DEOptim(DEOptim.NewCellFactory, RandomLongGenerator, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
Construct a DEOptim to solve unconstrained minimization problems.
DEOptim(double, double, RandomLongGenerator, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
Construct a DEOptim to solve unconstrained minimization problems.
DEOptim(double, double, double, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
Construct a DEOptim to solve unconstrained minimization problems.
DEOptim.NewCellFactory - Interface in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
This factory constructs a new DEOptimCellFactory for each minimization problem.
DEOptim.Solution - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
This is the solution to a minimization problem using DEOptim.
DeOptimCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell

DEOptimCellFactory - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
A DEOptimCellFactory produces DEOptimCellFactory.DeOptimCells.
DEOptimCellFactory(double, double, RandomLongGenerator) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
Construct an instance of a DEOptimCellFactory.
DEOptimCellFactory(DEOptimCellFactory) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
Copy constructor.
DEOptimCellFactory.DeOptimCell - Class in com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim
A DeOptimCell is a chromosome for a real valued function (an optimization problem) and a candidate solution.
dependence(double) - Method in interface com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVD
The dependence function $$A$$ for the parametric bivariate extreme value model.
dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricMixed

dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDBilogistic

dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDColesTawn

dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDHuslerReiss

dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDLogistic

dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeBilogistic

dependence(double) - Method in class com.numericalmethod.suanshu.stats.evt.evd.bivariate.BivariateEVDNegativeLogistic

depth() - Method in class com.numericalmethod.suanshu.graph.algorithm.traversal.BFS.Node
Gets the depth of this node.
depth(V) - Method in interface com.numericalmethod.suanshu.graph.RootedTree
Gets the (unweighted) distance of a vertex from the root of the vertex.
depth(V) - Method in class com.numericalmethod.suanshu.graph.type.SparseTree

depth(VertexTree<T>) - Method in class com.numericalmethod.suanshu.graph.type.VertexTree

DeRand1BinCell(RealScalarFunction, Vector) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell

DErf - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This is the first order derivative function of the Error function, Erf.
DErf() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DErf

Derivative of the link function, i.e., g'(x).
Derivative of the link function, i.e., g'(x).

Derivative of the link function, i.e., g'(x).

Derivative of the link function, i.e., g'(x).

Derivative of the link function, i.e., g'(x).
DerivativeFunction - Interface in com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem
Defines the derivative function F(x, y) for ODE problems.
deRowMean(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the de-mean (row means) matrix of a given matrix.
det() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix
The determinant of a Hilbert matrix is the reciprocal of an integer.
det(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.MatrixMeasure
Compute the determinant of a matrix.
deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMBinomial

deviance(double, double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMExponentialDistribution
Deviance D(y;μ^) measures the goodness-of-fit of a model, which is defined as the difference between the maximum log likelihood achievable and that achieved by the model.
deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGamma

deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMGaussian

deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMInverseGaussian

deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.GLMPoisson

deviance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMResiduals
Gets the (total) deviance.
deviance() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticResiduals
Gets the residual deviance.
devianceResiduals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMResiduals
Gets the deviances residuals.
devianceResiduals() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticResiduals
Gets the residuals, ε.
deviances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.GLMResiduals
Gets the deviances of the observations.
deviances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.QuasiGLMResiduals

df(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
Compute the finite difference for f at x with an increment h for the n-th order using either forward, backward, or central difference.
df() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMResiduals
Gets the degree of freedom.
df() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
Get the degree of freedom.
df1() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
Get the first degree of freedom.
df2() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
Get the second degree of freedom.
Dfdx - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
The first derivative is a measure of how a function changes as its input changes.
Dfdx(UnivariateRealFunction, Dfdx.Method) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
Construct the first order derivative function of a univariate function f.
Dfdx(UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
Construct, using the central finite difference, the first order derivative function of a univariate function f.
Dfdx.Method - Enum in com.numericalmethod.suanshu.analysis.differentiation.univariate
the available methods to compute the numerical derivative
DFFITS() - Method in class com.numericalmethod.suanshu.stats.regression.linear.residualanalysis.LMDiagnostics
DFFITS, Welsch and Kuh Measure.
DFPMinimizer - Class in com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton
The Davidon-Fletcher-Powell method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.
DFPMinimizer(double, int) - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton.DFPMinimizer
Construct a multivariate minimizer using the DFP method.
DFS<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
This class implements the depth-first-search using iteration.
DFS(Graph<? extends V, ? extends Edge<V>>) - Constructor for class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS
Constructs a DFS tree of a graph.
DFS(Graph<W, ? extends Edge<V>>, V, int) - Static method in class com.numericalmethod.suanshu.graph.algorithm.traversal.DFS
Runs the depth-first-search on a graph from a designated root.
DFS.Node<V> - Class in com.numericalmethod.suanshu.graph.algorithm.traversal
This is a node in a DFS-spanning tree.
DFS.Node.Color - Enum in com.numericalmethod.suanshu.graph.algorithm.traversal
This is the coloring scheme of visits.
DGamma - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This is the first order derivative function of the Gamma function, $${d \mathrm{\Gamma}(x) \over dx}$$.
DGamma() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGamma

DGaussian - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This is the first order derivative function of a Gaussian function, $${d \mathrm{\phi}(x) \over dx}$$.
DGaussian(Gaussian) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGaussian
Construct the derivative function of a Gaussian function.
Dhat() - Method in class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
Gets the modified diagonal matrix which is positive definite.
di2UnDiGraph(DiGraph<V, ? extends Arc<V>>) - Static method in class com.numericalmethod.suanshu.graph.GraphUtils
Converts a directed graph into an undirected graph by removing the direction of all arcs.
diagnostics() - Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.OLSRegression
Gets the diagnostic measures of an OLS regression.
diagonal(Matrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
Gets the diagonal of a matrix as a vector.
diagonal(SparseMatrix) - Static method in class com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation.VectorFactory
Gets the diagonal of a sparse matrix as a sparse vector.
Diagonalization() - Constructor for class com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.Diagonalization

DiagonalMatrix - Class in com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
A diagonal matrix has non-zero entries only on the main diagonal.
DiagonalMatrix(double[]) - Constructor for class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Constructs a diagonal matrix from a double[].