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A[col, ] = scale * A[col, ]
* : F × V → V
The result of applying this function to scalar, c, in F and v in V is denoted cv.
scalar * this
s * A
scalar * this
Here is a way to get a unit version of the vector:
vector.scaled(1. / vector.norm())
scalar * that
If scalar is 1, it simply returns itself.
A[row, ] = scale * A[row, ]
x = a * 10b
a is called significand or mantissa, and 1 ≤ |a| < 10.x = a * 10b.
x = a * 10b.
a so that f(x + a * d) is (approximately) minimized.
[lower, upper].
[lower, upper].
[lower, upper].
MRG.nextLong() to seed the generator.
from up to to with increments inc.
from up to to with increments inc.
from to to with increments 1.
[row, col] to value.
[row, col] to value.
[row, col] to value.
v[from : replacement.length] by a replacement starting at position from.
UnsupportedOperationException.
[*, column].
[*, column].
[row, *].
[row, *].
tol in Steward's deflation criterion.
sigma
The smaller it is, e.g., 0.1, the more accurate the result is.
σ(t, Xt, Zt, ...)
- sigma -
Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
- σ, the diffusion constant
- sigma -
Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.SDE
- the diffusion coefficient
σ(t, Xt, Zt, ...)
- sigma -
Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel
- the covariance matrix of white noise
- sigma() -
Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel
- Get the covariance matrix of white noise.
- sigma() -
Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.Vecm
- Get the covariance matrix of white noise.
- sigma -
Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel
- the white noise variance
- sigma() -
Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel
- Get the white noise variance.
- sigma2(double[], double[]) -
Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GarchModel
- Compute the conditional variance based on the past information.
- sigma2() -
Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GarchSim
- Get a copy of the conditional variances.
- sigma_i_j(int, int) -
Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
- Deprecated.
- sigma_i_j(int, int) -
Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Sigma
- Get the Ft adapted function D[i,j] element in the diffusion matrix.
- sign() -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
- Get the sign of this Permutation matrix which is essentially the determinant.
- significand -
Variable in class com.numericalmethod.suanshu.number.ScientificNotation
- the significand or mantissa
- simObservations(int[]) -
Method in class com.numericalmethod.suanshu.stats.hmm.HmmSim
- Simulate the observations {O_t} (t = 1, 2, ..., T) for a (discrete) hidden Markov model.
- SimpleDoubleArrayOperation - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation
-
- SimpleDoubleArrayOperation() -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleDoubleArrayOperation
-
- SimpleMatrixMathOperation - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation
- This class is one implementation of
MatrixMathOperation. - SimpleMatrixMathOperation() -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
-
- SimpleMultiVariateTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime
- A simple multivariate time series has its vectored values indexed by integers.
- SimpleMultiVariateTimeSeries(Matrix) -
Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
- Construct a SimpleMultiVariateTimeSeries.
- SimpleMultiVariateTimeSeries(double[]...) -
Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
- Construct a SimpleMultiVariateTimeSeries.
- SimpleMultiVariateTimeSeries(Vector...) -
Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
- Construct a SimpleMultiVariateTimeSeries.
- SimpleMultiVariateTimeSeries(TimeSeries) -
Constructor for class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
- Construct a SimpleMultiVariateTimeSeries from a univariate time series.
- SimpleMultiVariateTimeSeries.Iterator - Class in com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime
- the Iterator to read a SimpleMultiVariateTimeSeries
- SimpleTimeSeries - Class in com.numericalmethod.suanshu.stats.timeseries.univariate.realtime
- A simple time series has its values indexed by integers.
- SimpleTimeSeries(double[]) -
Constructor for class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
- Construct a SimpleTimeSeries from an array of values.
- SimpleTimeSeries.Iterator - Class in com.numericalmethod.suanshu.stats.timeseries.univariate.realtime
- the Iterator to read a SimpleTimeSeries
- SimplexPivoting - Interface in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting
- This is the interface for choosing a pivot in the simplex iteration to reduce the cost function.
- SimplexPivoting.Pivot - Class in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting
- the pivot
- SimplexPivoting.Pivot(int, int) -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting.SimplexPivoting.Pivot
- Construct a Pivot.
- Simpson - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
- Simpson's rule is often an accurate integration rule.
- Simpson(double, int) -
Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson
- Construct an integrator that uses Simpson's rule.
- simStates(int) -
Method in class com.numericalmethod.suanshu.stats.hmm.HmmSim
- Simulate the hidden states {q_t} (t = 1, 2, ..., T) for a (discrete) hidden Markov model.
- simulation() -
Method in class com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistributionSimulation
-
- sin(Complex) -
Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
- Sine of a complex number
(a + bi).
- singular(Matrix, double) -
Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
- Dimension if a square matrix is singular, i.e having no inverse.
- singularValues() -
Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.GloubKahanSVD
-
- singularValues() -
Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
-
- singularValues() -
Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVDDecomposition
- Get an array of the normalized, hence positive, singular values.
- sinh(Complex) -
Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
- Hyperbolic sine of a complex number
(a + bi).
- size() -
Method in class com.numericalmethod.suanshu.interval.Intervals
- Get the number of disjoint intervals.
- size() -
Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
- Get the number of distinct eigenvalue.
- size() -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
-
- size() -
Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
-
- size() -
Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid
-
- size() -
Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.TimeGrid
- the number of time points
- size() -
Method in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.UnitGrid
-
- size() -
Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
- Get the length of the history, excluding the initial value (0).
- size() -
Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.PathByIdImpl
- the number of time points
- size() -
Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk.Realization
-
- size() -
Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
-
- size() -
Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
-
- size() -
Method in interface com.numericalmethod.suanshu.stats.timeseries.TimeSeries
- the length of the time series
- size() -
Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
-
- size() -
Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.OneDimensionTimeSeries
-
- size() -
Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
-
- size() -
Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
-
- size() -
Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
-
- size() -
Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
- Get the length of this vector.
- Sk -
Variable in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
- the approximate inverse of the Hessian matrix
An implementation of
QuasiNewton.QuasiNewtonImpl.updateSk(com.numericalmethod.suanshu.matrix.doubles.Matrix)
will modify this incrementally.
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
-
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
-
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
-
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
-
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
- Get the skewness of this distribution.
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
-
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
-
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
-
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
- Get the skewness of this distribution.
- skew() -
Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.UnivariateDistribution
- Get the skewness of this distribution.
- skew() -
Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
-
- skew() -
Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
- Deprecated. Not supported yet.
- skew() -
Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
- Deprecated. Not supported yet.
- skew() -
Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
- Deprecated. Not supported yet.
- skew() -
Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
- Deprecated. Not supported yet.
- skew() -
Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
- Deprecated. Not supported yet.
- skew() -
Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
- Deprecated. Not supported yet.
- Skewness - Class in com.numericalmethod.suanshu.stats.descriptive.moment
- Skewness is a measure of the asymmetry of the probability distribution.
- Skewness() -
Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
- Construct an empty Skewness calculator.
- Skewness(double[]) -
Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
- Construct a Skewness calculator,
initialized with a sample.
- Skewness(Skewness) -
Constructor for class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
- Copy constructor.
- skewSymmetric(Matrix) -
Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
- Check if a matrix is skew symmetric.
- SmallestSubscriptRule - Class in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting
- Bland's smallest-subscript rule is for anti-cycling in choosing a pivot.
- SmallestSubscriptRule() -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting.SmallestSubscriptRule
-
- solve(Polynomial) -
Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.Cubic
- Solve
ax3 + bx2 + cx + d = 0
- solve(Polynomial) -
Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub.JenkinsTraubReal
- Compute the roots for a polynomial.
- solve(Polynomial) -
Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.Linear
- Solve
ax + b = 0
- solve(Polynomial) -
Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.Polyroot
- Call the appropriate solver to find roots/zeros for the polynomial.
- solve(Polynomial) -
Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.Quadratic
- Solve
ax2 + bx + c = 0
- solve(double, double, double, double, double) -
Method in interface com.numericalmethod.suanshu.analysis.function.polynomial.root.Quartic.QuarticSolver
- Solve
ax4 + bx3 + cx2 + dx + e = 0
- solve(Polynomial) -
Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.Quartic
- Solve
ax4 + bx3 + cx2 + dx + e = 0
- solve(double, double, double, double, double, double) -
Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticByFerrari
- Solve
ax4 + bx3 + cx2 + dx + e = 0
- solve(double, double, double, double, double) -
Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticByFerrari
-
- solve(double, double, double, double, double) -
Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.QuarticByFormula
-
- solve(Polynomial) -
Method in interface com.numericalmethod.suanshu.analysis.function.polynomial.root.Solver
- Compute the roots for a polynomial.
- solve(int, double, double, double...) -
Method in class com.numericalmethod.suanshu.analysis.uniroot.Brent
- The implementation of a specific uniroot finding algorithm.
- solve(int, double) -
Method in class com.numericalmethod.suanshu.analysis.uniroot.Halley
- Solve
f(x) = 0 using Halley's algorithm
with an initial guess for the maximum number of iterations.
- solve(int, double, double, double...) -
Method in class com.numericalmethod.suanshu.analysis.uniroot.Halley
-
- solve(int, double) -
Method in class com.numericalmethod.suanshu.analysis.uniroot.Newton
- Solve
f(x) = 0 using Newton's algorithm
with an initial guess for the maximum number of iterations.
- solve(int, double, double, double...) -
Method in class com.numericalmethod.suanshu.analysis.uniroot.Newton
-
- solve(int, double, double, double) -
Method in class com.numericalmethod.suanshu.analysis.uniroot.Uniroot
- Search for a root in the interval
[lower, upper]
for the maximum number of maxIterations.
- solve(int, double, double, double...) -
Method in class com.numericalmethod.suanshu.analysis.uniroot.Uniroot
- The implementation of a specific uniroot finding algorithm.
- solve(Vector) -
Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.BackwardSubstitution
- Solve
Ux = b
- solve(Vector) -
Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.ForwardSubstitution
- Solve
Lx = b
- solve(Vector) -
Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.LU
- Solve
Ax = b
- solve(Vector) -
Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.OLSSolver
- In the ordinary least square sense, solve
Ax = y.
- solve(Vector) -
Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Solver
- Get a particular solution for the linear system,
Ax = b
- solve(IterativeSolver.Problem) -
Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver
- Solve iteratively
Ax = b
until the solution is close enough, i.e., the norm of residual
(b - Ax) is less than or equal to the specified iteration.
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver
- Solve iteratively
Ax = b
until the solution is close enough, i.e., the norm of residual
(b - Ax) is less than or equal to the specified iteration.
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
- solve(Vector) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
- Returns the input
Vector x.
- solve(Vector) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
- Return P-1x, where P is the diagonal matrix
with the same diagonal as A.
- solve(Vector) -
Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner
- Solve Mv = x, where M is the preconditioner matrix.
- solve(Vector) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SsorPreconditioner
- Solve Mz = x using this SSOR preconditioner.
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
-
- solve(IterativeSolver.Problem) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
- solve(IterativeSolver.Problem, IterationMonitor) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
-
- solve(ConstrainedProblem, double) -
Method in interface com.numericalmethod.suanshu.optimization.constrained.general.ConstrainedMinimizer
-
- solve(ConstrainedProblem, double) -
Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethod
-
- solve(IntegerConstrainedProblem, double) -
Method in class com.numericalmethod.suanshu.optimization.constrained.integer.BruteForce
-
- solve(IntegerConstrainedProblem, double) -
Method in interface com.numericalmethod.suanshu.optimization.constrained.integer.IntegerConstrainedMinimizer
-
- solve() -
Method in interface com.numericalmethod.suanshu.optimization.constrained.linearprogramming.LpSolver
- Solve the Linear Programming (LP) problem.
- solve() -
Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.Phase2ByFerrisMangasarianWright
-
- solve() -
Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.StandardSimplex
-
- solve(RealScalarFunction, RealVectorFunction, RntoMatrix, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.ConjugateGradient
-
- solve(RealScalarFunction, RealVectorFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.ConjugateGradient
-
- solve(RealScalarFunction, RealVectorFunction, double, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Zangwill
- Solve an instance of Zangwill to minimize a function
f.
- solve(RealScalarFunction, RealVectorFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.Zangwill
- Construct an instance of Zangwill to minimize a function.
- solve(UnconstrainedProblem, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead
-
- solve(RealScalarFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead
-
- solve(boolean, RealScalarFunction, RealVectorFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.BFGS
-
- solve(UnconstrainedProblem, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton
-
- solve(RealVectorFunction, RntoMatrix, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton
- Construct an instance of GaussNewton to minimize a real vector function
f.
- solve(RealVectorFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.GaussNewton
- Construct an instance of GaussNewton to minimize a real vector function
f.
- solve(RealScalarFunction, RealVectorFunction, RntoMatrix, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.NewtonRaphson
-
- solve(RealScalarFunction, RealVectorFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.NewtonRaphson
-
- solve(RealScalarFunction, RealVectorFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
-
- solve(RealScalarFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
-
- solve(UnconstrainedProblem, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
-
- solve(RealScalarFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.unconstrained.UnconstrainedMaximizer
-
- solve(UnconstrainedProblem, double) -
Method in interface com.numericalmethod.suanshu.optimization.unconstrained.UnconstrainedMinimizer
-
- solve(RealScalarFunction, double) -
Method in interface com.numericalmethod.suanshu.optimization.unconstrained.UnconstrainedMinimizer
-
- solve(UnivariateRealFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.univariate.BracketSearch
- Construct an instance of BracketSearch to minimize a function
f.
- solve(RealScalarFunction, double) -
Method in class com.numericalmethod.suanshu.optimization.univariate.Minimizer
-
- Solver - Interface in com.numericalmethod.suanshu.analysis.function.polynomial.root
- All analytical root finding formulae for polynomials implement this interface.
- Solver - Class in com.numericalmethod.suanshu.matrix.doubles.linearsystem
- Solve a system of linear equations in the form:
Ax = b,
where A has #rows <= #columns. - Solver(Matrix, double) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Solver
- Construct a Solver instance to solve for different
Vector b.
- Solver(Matrix) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Solver
- Construct a Solver instance to solve for different
Vector b.
- solver -
Variable in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethod
- the unconstrained solver class
- Solver.NoSolution - Exception in com.numericalmethod.suanshu.matrix.doubles.linearsystem
- RuntimeException thrown when it fails to solve a system of linear equations.
- Solver.NoSolution(String) -
Constructor for exception com.numericalmethod.suanshu.matrix.doubles.linearsystem.Solver.NoSolution
-
- Solver.RootFindingException - Exception in com.numericalmethod.suanshu.analysis.function.polynomial.root
- RuntimeException thrown when it fails to find a root for a polynomial.
- Solver.RootFindingException(String) -
Constructor for exception com.numericalmethod.suanshu.analysis.function.polynomial.root.Solver.RootFindingException
-
- Solver.Type - Enum in com.numericalmethod.suanshu.analysis.function.polynomial.root
- the type of polynomials the solver can solve
- SorSweep - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
- This is a building block for
SOR and
SSOR
to perform forward or backward sweep.
- SorSweep(Matrix, Vector, double) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SorSweep
- Construct an instance to perform forward or backward sweep for a linear
system Ax = b.
- spanningCoefficients(Vector) -
Method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.VectorSpace
- Find a linear combination of the basis that best approximates a vector in the linear least square sense.
- SparseElement - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
- This class represents a matrix element in a sparse matrix.
- SparseElement(Coordinates, double) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseElement
- Create a sparse element with its coordinates and value.
- SparseElement.TopLeftFirstComparator - Enum in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
- This
Comparator can be used when a list of matrix elements are to
be sorted according to their coordinates. - SparseMatrix - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
- This interface defines the sparse matrix which stores non-zero values only.
- SparseStructure - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
- This interface defines common operations on sparse structures like sparse
vector or sparse matrix.
- SparseVector - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
- This class represents sparse vector which stores the non-zero values only.
- SparseVector(int) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
- Create an instance of sparse vector of the specified
size.
- SparseVector(int, int[], double[]) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
- Create an instance of sparse vector with non-zero values.
- SparseVector(SparseVector) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
- Copy constructor.
- SparseVector.Entry - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
- This class represents an entry in a
SparseVector. - SparseVector.Iterator - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
- This wrapper class overrides the
Iterator.remove()
method for throwing exception when it is called. - sqrt(Complex) -
Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
- Square root of a complex number.
- Sqrt - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
- This class represents the link function:
g(x) = sqrt(x)
- Sqrt() -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Sqrt
-
- squareQ() -
Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
- Get a copy of the square
Q matrix.
- squareQ() -
Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection
-
- squareQ() -
Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QR
-
- squareQ() -
Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QRDecomposition
- Get a copy of the square
Q matrix.
- SsorPreconditioner - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
- SSOR preconditioner is derived from the symmetric coefficient matrix A
which is decomposed as
A = D + L + Lt
The SSOR preconditioning matrix is defined as
M = (D + L)D-1(D + L)t
or, parameterized by ω
M(ω) = (1/(2 - ω))(D / ω + L)(D / ω)-1(D / ω + L)t
The optimal value of ω will reduce the number of iterations to
a lower order.
- SsorPreconditioner(Matrix, double) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SsorPreconditioner
- Create a SSOR preconditioner with a symmetric coefficient matrix.
- standardDeviation() -
Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
- Get the standard deviation of the sample,
which is the square root of the variance.
- StandardGaussian - Class in com.numericalmethod.suanshu.stats.random.distribution
- Sample pseudo random numbers from the standard Normal distribution.
- StandardGaussian(Gaussian.Method, RandomNumberGenerator) -
Constructor for class com.numericalmethod.suanshu.stats.random.distribution.StandardGaussian
- Construct a pseudo-random number generator of the standard Gaussian distribution.
- StandardGaussian() -
Constructor for class com.numericalmethod.suanshu.stats.random.distribution.StandardGaussian
- Construct a pseudo-random number generator of the standard Gaussian distribution.
- StandardInterval - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
- This transformation is for mapping integral region from
[a, b] to [-1, 1]. - StandardInterval(double, double) -
Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval
- Construct an instance of the StandardInterval substitution rule.
- standardized() -
Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
- standard residual = residual / v1 / RSS / (m-n)
- StandardLpProblem1 - Class in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
- This class represents a linear programming in the following standard form.
- StandardLpProblem1(Vector, Matrix, Vector) -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.StandardLpProblem1
- Construct a linear programming problem in the standard form.
- StandardLpProblem1(StandardLpProblem2) -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.StandardLpProblem1
- Construct a linear programming problem in this standard form from form StandardLpProblem2.
- StandardLpProblem2 - Class in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
- This class represents a linear programming in the standard form,
following the convention in the reference.
- StandardLpProblem2(Vector, Matrix, Vector) -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.StandardLpProblem2
- Construct a linear programming problem in the standard form.
- StandardLpProblem2(StandardLpProblem1) -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.StandardLpProblem2
- Construct a linear programming problem in this standard form from form StandardLpProblem1.
- StandardSimplex - Class in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard
-
In our implementation,
we follow the convention in "Linear Programming with MATLAB," Michael C.
- StandardSimplex(StandardLpProblem2) -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.StandardSimplex
-
- StandardSimplex(StandardLpProblem1) -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.StandardSimplex
-
- StateEquation - Class in com.numericalmethod.suanshu.stats.dlm
- The state equation in a controlled dynamic linear model.
- StateEquation(R1toMatrix, R1toMatrix, R1toMatrix) -
Constructor for class com.numericalmethod.suanshu.stats.dlm.StateEquation
- Construct a state equation.
- StateEquation(R1toMatrix, R1toMatrix) -
Constructor for class com.numericalmethod.suanshu.stats.dlm.StateEquation
- Construct a state equation without control variables.
- StateEquation(Matrix, Matrix, Matrix) -
Constructor for class com.numericalmethod.suanshu.stats.dlm.StateEquation
- Construct a time-invariant state equation.
- StateEquation(Matrix, Matrix) -
Constructor for class com.numericalmethod.suanshu.stats.dlm.StateEquation
- Construct a time-invariant state equation without control variables.
- StateEquation(StateEquation) -
Constructor for class com.numericalmethod.suanshu.stats.dlm.StateEquation
- Copy constructor.
- Statistic - Interface in com.numericalmethod.suanshu.stats.descriptive
- A statistic (singular) is a single measure of some attribute of a sample (e.g. its arithmetic mean value).
- stderr -
Variable in class com.numericalmethod.suanshu.stats.regression.linear.Beta
- the standard errors of the coefficients β^
- stderr -
Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
- the standard error of the residuals
- stderr() -
Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaFitting
- Get the asymptotic standard errors of the estimators.
- stderr() -
Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ConditionalSumOfSquares
- Compute the asymptotic standard errors for the estimated parameters, φ and θ.
- SteepestDescent - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
- A steepest descent algorithm finds the minimum by moving along the negative of the steepest g direction.
- SteepestDescent() -
Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent
-
- SteepestDescent.LineSearch - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
- A steepest-descent method, in each iteration, searches along a direction to
find the next best minimizer along a direction.
- SteepestDescent.LineSearch(RntoMatrix) -
Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.LineSearch
- Construct a line search instance.
- SteepestDescentSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
- The Steepest Descent method (SDM) can solve symmetric n-by-n linear systems.
- SteepestDescentSolver() -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
-
- SteepestDescentSolver(int) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
- The solver recomputes the residual as b - Axi once per this number of iterations
- studentized() -
Method in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals
- studentized residual = standardized * sqrt((m-n-1) / (n-m-standardized))
- SuanShuUtils - Class in com.numericalmethod.suanshu.misc
- This class collects some miscellaneous utility functions that are commonly used.
- subarray(double[], int[]) -
Static method in class com.numericalmethod.suanshu.misc.R
- Get a subarray of the original array with the given indices.
- subarray(int[], int[]) -
Static method in class com.numericalmethod.suanshu.misc.R
- Get a subarray of the original array with the given indices.
- subDiagonal(Matrix) -
Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
- Take the sub-diagonal of a matrix.
- subMatrix(Matrix, int, int, int, int) -
Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
- CreateMatrix a sub-matrix from the four corners of a matrix.
- subMatrix(Matrix, int[], int[]) -
Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
- CreateMatrix a sub-matrix from the intersections of rows and columns of a matrix.
- SubMatrixRef - Class in com.numericalmethod.suanshu.matrix.doubles.operation
- This class creates a 'reference' to a sub-part of a large matrix without copying it.
- SubMatrixRef(Matrix, int, int, int, int) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
- Construct a sub-matrix reference.
- SubMatrixRef(Matrix) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
- Construct a sub-matrix reference.
- Substitution - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
- This class specifies a substitution rule.
- Substitution(UnivariateRealFunction, UnivariateRealFunction) -
Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Substitution
- Create a substitution by specifying the transformation rule.
- subVector(Vector, int, int) -
Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
- Get a sub-vector from a vector
v.
- SuccessiveOverrelaxationSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
- The Successive Overrelaxation method (SOR), is devised by applying
extrapolation to the Gauss-Seidel method.
- SuccessiveOverrelaxationSolver(double) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
- Construct a SOR solver with the extrapolation factor ω.
- sum(int, int) -
Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
- Sum up the terms from
from to to with the increment 1.
- sum(int, int, int) -
Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
- Sum up the terms from
from to to with the increment inc.
- sum(double, double, double) -
Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
- Sum up the terms from
from to to with the increment inc.
- sum(double[]) -
Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
- Finite summation of the
terms.
- sum(BigDecimal...) -
Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
- Compute the sum of an array of BigDecimals.
- sum(double...) -
Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
- Compute the sum of an array of doubles.
- sum(double...) -
Static method in class com.numericalmethod.suanshu.number.DoubleUtils
- Get the sum of the values.
- sum(int...) -
Static method in class com.numericalmethod.suanshu.number.DoubleUtils
- Get the sum of the values.
- sum_BtDt -
Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_BtDt
-
- sum_BtDt -
Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_tBtDt
-
- sum_tBtDt -
Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_tBtDt
-
- Summation - Class in com.numericalmethod.suanshu.analysis.sequence
- Summation sums up the
Summation.Terms. - Summation(Summation.Term, double) -
Constructor for class com.numericalmethod.suanshu.analysis.sequence.Summation
- Construct a Summation instance with a
term structure and a threshold.
- Summation(Summation.Term) -
Constructor for class com.numericalmethod.suanshu.analysis.sequence.Summation
- Constructor a Summation instance with a
term structure.
- Summation.Term - Interface in com.numericalmethod.suanshu.analysis.sequence
- Define the terms in a summation series.
- SumOfPenalties - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
- This penalty function sums up the costs from a set of constituent penalty functions.
- SumOfPenalties(PenaltyFunction...) -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.SumOfPenalties
- Construct a SumOfPenalties penalty function from a set of penalty functions.
- sumToInfinity(int) -
Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
- Sum up the terms from
from to infinity with increment 1 until the series converges.
- sumToInfinity(double, double) -
Method in class com.numericalmethod.suanshu.analysis.sequence.Summation
- Sum up the terms from
from to infinity with increment inc until the series converges.
- superDiagonal(Matrix) -
Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
- Take the super-diagonal of a matrix.
- SVD - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.svd
- The SVD decomposition of a matrix.
- SVD(Matrix, boolean, SVD.Method, double) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
- Construct an instance of the SVD decomposition.
- SVD(Matrix, boolean) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
- Construct an instance of the SVD decomposition.
- SVD.Method - Enum in com.numericalmethod.suanshu.matrix.doubles.factorization.svd
- the methods available to compute eigenvalues and eigenvectors
- SVDDecomposition - Interface in com.numericalmethod.suanshu.matrix.doubles.factorization.svd
- All SVD decomposition algorithms implements this interface.
- swap(int, int) -
Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.Tableau
- Perform a Jordan Exchange to swap row
r with column s.
- swapColumn(int, int) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
- This swaps two columns of a permutation matrix.
- swapColumn(int, int) -
Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
- Swap columns:
A[col1, ] = A[col2, ]
A[col2, ] = A[col1, ]
- swapRow(int, int) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
- This swaps two rows of a permutation matrix.
- swapRow(int, int) -
Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
- Swap rows:
A[row1, ] = A[row2, ]
A[row2, ] = A[row1, ]
- symmetric(Matrix) -
Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
- Check if a matrix is symmetric.
- SymmetricMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle
- A symmetric matrix is a square matrix such that its transpose equals to itself, i.e.,
A[i][j] = A[j][i]
We implement this class by storing the data using an lower triangular matrix, e.g., LowerTriangularMatrix. - SymmetricMatrix(int) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
- Construct a symmetric matrix of dimension
dim * dim.
- SymmetricMatrix(double[][]) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
- Construct a symmetric matrix from a 2D double[][] array.
- SymmetricMatrix(SymmetricMatrix) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
- Copy constructor performing a deep copy.
- symmetricPositiveDefinite(Matrix) -
Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
- Check if a square matrix is symmetric and positive definite.
- SymmetricSuccessiveOverrelaxationSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
- The Symmetric Successive Overrelaxation method (SSOR) is like
SOR, but it performs in each
iteration one forward sweep followed by one backward sweep.
- SymmetricSuccessiveOverrelaxationSolver(double) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
- Construct a SSOR solver with the extrapolation factor ω.
- SynchronizedStatistic - Class in com.numericalmethod.suanshu.stats.descriptive
- This provides a thread-safe version of Statistic by synchronizing all public methods
so that only one thread at a time can access the instance.
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