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# Classes and Interfaces in #SuanShu - 1276 results found.
NameDescriptionTypePackageFrameworkJavaDoc
AbelianGroupAn Abelian group is a group with a binary additive operation (+), satisfying the group axioms: closureassociativityexistence of additive identityexistence of additive oppositecommutativity of additionInterfacecom.numericalmethod.suanshu.algebra.structureSuanShujavadoc
ABMPredictorCorrectorThe Adams-Bashforth predictor and the Adams-Moulton corrector pair.Interfacecom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoultonSuanShujavadoc
ABMPredictorCorrector1The Adams-Bashforth predictor and the Adams-Moulton corrector of order 1.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoultonSuanShujavadoc
ABMPredictorCorrector2The Adams-Bashforth predictor and the Adams-Moulton corrector of order 2.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoultonSuanShujavadoc
ABMPredictorCorrector3The Adams-Bashforth predictor and the Adams-Moulton corrector of order 3.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoultonSuanShujavadoc
ABMPredictorCorrector4The Adams-Bashforth predictor and the Adams-Moulton corrector of order 4.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoultonSuanShujavadoc
ABMPredictorCorrector5The Adams-Bashforth predictor and the Adams-Moulton corrector of order 5.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoultonSuanShujavadoc
AbsoluteErrorPenaltyThis penalty function sums up the absolute error penalties.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShujavadoc
AbsoluteToleranceThe stopping criteria is that the norm of the residual r is equal to or smaller than the specified tolerance, that is,Classcom.numericalmethod.suanshu.misc.algorithm.iterative.toleranceSuanShujavadoc
AbstractBivariateEVDClasscom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
AbstractBivariateProbabilityDistributionClasscom.numericalmethod.suanshu.stats.distribution.multivariateSuanShujavadoc
AbstractBivariateRealFunctionA bivariate real function takes two real arguments and outputs one real value.Classcom.numericalmethod.suanshu.analysis.function.rn2r1SuanShujavadoc
AbstractGridExecutorProvides basic default implementations of GridExecutor functions on top of the map operation.Classcom.numericalmethod.suanshu.grid.executorSuanShujavadoc
AbstractHybridMCMCHybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional Metropolis algorithm, with molecular dynamics simulation.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShujavadoc
AbstractMetropolisThe 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.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShujavadoc
AbstractR1RnFunctionThis is a function that takes one real argument and outputs one vector value.Classcom.numericalmethod.suanshu.analysis.function.rn2rmSuanShujavadoc
AbstractRealScalarFunctionThis abstract implementation implements Function.Classcom.numericalmethod.suanshu.analysis.function.rn2r1SuanShujavadoc
AbstractRealVectorFunctionThis abstract implementation implements Function.Classcom.numericalmethod.suanshu.analysis.function.rn2rmSuanShujavadoc
AbstractTrivariateRealFunctionA trivariate real function takes three real arguments and outputs one real value.Classcom.numericalmethod.suanshu.analysis.function.rn2r1SuanShujavadoc
AbstractUnivariateRealFunctionA univariate real function takes one real argument and outputs one real value.Classcom.numericalmethod.suanshu.analysis.function.rn2r1.univariateSuanShujavadoc
ACERAnalysisAverage Conditional Exceedance Rate (ACER) method is for estimating the cdf of the maxima (M) distribution from observations.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShujavadoc
ACERByCountingEstimate epsilons by counting conditional exceedances from the observations.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShujavadoc
ACERConfidenceIntervalUsing 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.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShujavadoc
ACERFunctionThe ACER (Average Conditional Exceedance Rate) function (epsilon_k(eta)) approximates the epsilon_k(eta) = Pr(X_k > eta Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShujavadoc

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ACERInverseFunctionThe inverse of the ACER function.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShujavadoc
ACERLogFunctionThe ACER function in log scale (base e), i.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShujavadoc
ACERReturnLevelGiven an ACER function, compute the return level (eta) for a given return period (R).Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShujavadoc
ACERUtilsUtility functions used in ACER empirical analysis.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShujavadoc
ActiveListThis interface defines the node popping strategy used in a branch-and-bound algorithm, e.Interfacecom.numericalmethod.suanshu.misc.algorithm.bbSuanShujavadoc
ActiveSetThis class keeps track of the active and inactive indices.Classcom.numericalmethod.suanshu.misc.algorithmSuanShujavadoc
ActorPropsStatic factory class that contains all of the common Props, to make the code that uses them more readable.Classcom.numericalmethod.suanshu.grid.executor.remote.akkaSuanShujavadoc
AdamsBashforthMoultonThis class uses an Adams-Bashford predictor and an Adams-Moulton corrector of the specified order.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoultonSuanShujavadoc
AdditiveModelThe additive model of a time series is an additive composite of the trend, seasonality and irregular random components.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocessSuanShujavadoc
ADFAsymptoticDistributionThis class computes the asymptotic distribution of the Augmented Dickey-Fuller (ADF) test There are three main versions of the test and thus three possible asymptotic distributions:Classcom.numericalmethod.suanshu.stats.test.timeseries.adfSuanShujavadoc
ADFAsymptoticDistribution1This is the asymptotic distribution of the Augmented Dickey-Fuller test statistic, for the TrendType.Classcom.numericalmethod.suanshu.stats.test.timeseries.adfSuanShujavadoc
ADFDistributionThis represents an Augmented Dickey Fuller distribution.Classcom.numericalmethod.suanshu.stats.test.timeseries.adfSuanShujavadoc
ADFDistributionTableA table contains the simulated observations/values of an empirical ADF distribution for a given set of parameters.Classcom.numericalmethod.suanshu.stats.test.timeseries.adf.tableSuanShujavadoc
ADFDistributionTable_CONSTANT_lag0This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for theClasscom.numericalmethod.suanshu.stats.test.timeseries.adf.tableSuanShujavadoc
ADFDistributionTable_CONSTANT_TIME_lag0This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for theClasscom.numericalmethod.suanshu.stats.test.timeseries.adf.tableSuanShujavadoc
ADFDistributionTable_NO_CONSTANT_lag0This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for theClasscom.numericalmethod.suanshu.stats.test.timeseries.adf.tableSuanShujavadoc
ADFFiniteSampleDistributionThis class computes the finite sample distribution of the Augmented Dickey-Fuller (ADF) test There are three main versions of the test and thus three possible asymptotic distributions:Classcom.numericalmethod.suanshu.stats.test.timeseries.adfSuanShujavadoc
AfterIterationsStops after a given number of iterations.Classcom.numericalmethod.suanshu.misc.algorithm.stopconditionSuanShujavadoc
AfterNoImprovementClasscom.numericalmethod.suanshu.misc.algorithm.stopconditionSuanShujavadoc
AhatEstimationEstimates the coefficient of a VAR(1) model by penalized maximum likelihood.Classcom.numericalmethod.suanshu.model.daspremont2008SuanShujavadoc
AkkaGridExecutorUses Akka to distribute the computational load between multiple machines.Classcom.numericalmethod.suanshu.grid.executor.remote.akkaSuanShujavadoc
AkkaGridExecutorFactoryCreates instances of GridExecutorFactory that use Akka's remoting to distribute computation, from a configuration object.Classcom.numericalmethod.suanshu.grid.executor.remote.akkaSuanShujavadoc
AkkaUtilsUtility methods for Akka.Classcom.numericalmethod.suanshu.grid.executor.remote.akkaSuanShujavadoc
AlternatingDirectionImplicitMethodAlternating direction implicit (ADI) method is an implicit method for obtaining numerical approximations to the solution of a HeatEquation2D.Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2SuanShujavadoc
AndersonDarlingThis algorithm calculates the Anderson-Darling k-sample test statistics and p-values.Classcom.numericalmethod.suanshu.stats.test.distributionSuanShujavadoc

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AndersonDarlingPValueThis algorithm calculates the p-value when the Anderson-Darling statistic and the number of samples are given.Classcom.numericalmethod.suanshu.stats.test.distributionSuanShujavadoc
AndStopConditionsCombines an arbitrary number of stop conditions, terminating when all conditions are met.Classcom.numericalmethod.suanshu.misc.algorithm.stopconditionSuanShujavadoc
AnnealingFunctionAn annealing function or a tempered proposal function gives the next proposal/state from the current state and temperature.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShujavadoc
AntitheticVariatesThe 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, forClasscom.numericalmethod.suanshu.stats.random.variancereductionSuanShujavadoc
AntoniouLu2007This implementation is based on Algorithm 14.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpointSuanShujavadoc
AR1GARCH11ModelAn AR1-GARCH11 model takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarchSuanShujavadoc
ArcAn arc is an ordered pair of vertices.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
ArgumentAssertionUtility class for checking numerical arguments.Classcom.numericalmethod.suanshu.miscSuanShujavadoc
ARIMAForecastForecasts an ARIMA time series using the innovative algorithm.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.arimaSuanShujavadoc
ARIMAForecastMultiStepMakes forecasts for a time series assuming an ARIMA model using the innovative algorithm.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.arimaSuanShujavadoc
ARIMAModelAn ARIMA(p, d, q) process, Xt, is such that (1 - B)^d X_t = Y_tClasscom.numericalmethod.suanshu.stats.timeseries.linear.univariate.arimaSuanShujavadoc
ARIMASimThis class simulates an ARIMA (AutoRegressive Integrated Moving Average) process.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.arimaSuanShujavadoc
ARIMAXModelThe ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.arimaSuanShujavadoc
ARMAFitInterfacecom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
ARMAForecastForecasts an ARMA time series using the innovative algorithm.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
ARMAForecastMultiStepComputes the h-step ahead prediction of a causal ARMA model, by the innovative algorithm.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
ARMAForecastOneStepComputes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
ARMAGARCHFitThis implementation fits, for a data set, an ARMA-GARCH model by Quasi-Maximum Likelihood "QMLE" stands for Quasi-Maximum Likelihood Estimation, which assumes Normal distribution andClasscom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarchSuanShujavadoc
ARMAGARCHModelAn ARMA-GARCH model takes this form: X_t = mu + sum_{i=1}^p phi_i X_{t-i} + sum_{i=1}^q heta_j epsilon_{t-j} + epsilon_t,Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarchSuanShujavadoc
ARMAModelA univariate ARMA model, Xt, takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
ARMAXModelThe ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
ARModelThis class represents an AR model.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
ArrayUtilsGet a left shifted array.Classcom.numericalmethod.suanshu.miscSuanShujavadoc
ARResamplerFactoryClasscom.numericalmethod.suanshu.model.lai2010.ceta.npeb.resamplerSuanShujavadoc
AS159Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the lists of row and column sums.Classcom.numericalmethod.suanshu.stats.test.distribution.pearsonSuanShujavadoc

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AtThresholdStops when the value reaches a given value with a given precision.Classcom.numericalmethod.suanshu.misc.algorithm.stopconditionSuanShujavadoc
AugmentedDickeyFullerThe Augmented Dickey Fuller test tests whether a one-time differencing (d = 1) will make the time That is, whether the series has a unit root.Classcom.numericalmethod.suanshu.stats.test.timeseries.adfSuanShujavadoc
AutoARIMAFitSelects the order and estimates the coefficients of an ARIMA model automatically by AIC or AICC.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.arimaSuanShujavadoc
AutoCorrelationCompute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
AutoCorrelationFunctionThis is the auto-correlation function of a univariate time series {xt}.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariateSuanShujavadoc
AutoCovarianceComputes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by The R equivalent functions are ARMAacf and TacvfAR in package FitAR.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
AutoCovarianceFunctionThis is the auto-covariance function of a univariate time series {xt}.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariateSuanShujavadoc
AutoParallelMatrixMathOperationThis class uses ParallelMatrixMathOperation when the first input matrix argument's size is greater than the defined threshold; otherwise, it uses SimpleMatrixMathOperation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperationSuanShujavadoc
BackwardEliminationConstructs a GLM model for a set of observations using the backward elimination method.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShujavadoc
BackwardSubstitutionBackward substitution solves a matrix equation in the form Ux = b by an iterative process for an upper triangular matrix U.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc
BartlettBartlett's test is used to test if k samples are from populations with equal variances, hence homoscedasticity.Classcom.numericalmethod.suanshu.stats.test.varianceSuanShujavadoc
BasisA basis is a set of linearly independent vectors spanning a vector space.Classcom.numericalmethod.suanshu.algebra.linear.vector.doubles.operationSuanShujavadoc
BaumWelchClasscom.numericalmethod.suanshu.stats.hmm.discreteSuanShujavadoc
BBNodeA branch-and-bound algorithm maintains a tree of nodes to keep track of the search paths and the pruned paths.Interfacecom.numericalmethod.suanshu.misc.algorithm.bbSuanShujavadoc
BernoulliTrialA 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 timeClasscom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc
Best1BinThe 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.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShujavadoc
Best2BinThe Best-1-Bin rule always picks the best chromosome as the base.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShujavadoc
BetaThe 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 > 0Classcom.numericalmethod.suanshu.analysis.function.special.betaSuanShujavadoc
BetaDistributionClasscom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
BetaMixtureDistributionThe HMM states use the Beta distribution to model the observations.Classcom.numericalmethod.suanshu.stats.hmm.mixture.distributionSuanShujavadoc
BetaRegularizedThe 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 > 0Classcom.numericalmethod.suanshu.analysis.function.special.betaSuanShujavadoc
BetaRegularizedInverseThe inverse of the Regularized Incomplete Beta function is defined at: x = I^{-1}_{(p,q)}(u), 0 le u le 1Classcom.numericalmethod.suanshu.analysis.function.special.betaSuanShujavadoc
BFGSMinimizerThe Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShujavadoc
BFSThis class implements the breadth-first-search using iteration.Classcom.numericalmethod.suanshu.graph.algorithm.traversalSuanShujavadoc
BiconjugateGradientSolverThe Biconjugate Gradient method (BiCG) is useful for solving non-symmetric n-by-n linear systems.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc

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BiconjugateGradientStabilizedSolverThe Biconjugate Gradient Stabilized (BiCGSTAB) method is useful for solving non-symmetric n-by-n linear systems.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc
BicubicInterpolationBicubic interpolation is the two-dimensional equivalent of cubic Hermite spline interpolation.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariateSuanShujavadoc
BicubicSplineBicubic splines are the two-dimensional equivalent of cubic splines.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariateSuanShujavadoc
BiDiagonalizationInterfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalizationSuanShujavadoc
BiDiagonalizationByGolubKahanLanczosThis implementation uses Golub-Kahan-Lanczos algorithm with reorthogonalization.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalizationSuanShujavadoc
BiDiagonalizationByHouseholderClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalizationSuanShujavadoc
BidiagonalMatrixA bi-diagonal matrix is either upper or lower diagonal.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonalSuanShujavadoc
BidiagonalSVDbyMR3Given a bidiagonal matrix A, computes the singular value decomposition (SVD) of A, using "Algorithm of Multiple Relatively Robust Representations" (MRRR).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3SuanShujavadoc
BigDecimalUtilsThese are the utility functions to manipulate BigDecimal.Classcom.numericalmethod.suanshu.number.bigSuanShujavadoc
BigIntegerUtilsThese are the utility functions to manipulate BigInteger.Classcom.numericalmethod.suanshu.number.bigSuanShujavadoc
BilinearInterpolationBilinear interpolation is the 2-dimensional equivalent of linear interpolation.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariateSuanShujavadoc
BinomialDistributionThe 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.Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
BinomialMixtureDistributionThe HMM states use the Binomial distribution to model the observations.Classcom.numericalmethod.suanshu.stats.hmm.mixture.distributionSuanShujavadoc
BinomialRNGThis random number generator samples from the binomial distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc
BinsThis class divides the items based on their keys into a number of bins.Classcom.numericalmethod.suanshu.misc.algorithmSuanShujavadoc
BisectionRootThe bisection method repeatedly bisects an interval and then selects a subinterval in which a root must lie for further processing.Classcom.numericalmethod.suanshu.analysis.root.univariateSuanShujavadoc
BivariateArrayGridClasscom.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariateSuanShujavadoc
BivariateEVDBivariate Extreme Value (BEV) distribution is the joint distribution of component-wise maxima of two-dimensional iid random vectors.Interfacecom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
BivariateEVDAsymmetricLogisticThe bivariate asymmetric logistic model.Classcom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
BivariateEVDAsymmetricMixedThe asymmetric mixed model.Classcom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
BivariateEVDAsymmetricNegativeLogisticThe bivariate asymmetric negative logistic model.Classcom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
BivariateEVDBilogisticThe bilogistic model.Classcom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
BivariateEVDColesTawnThe Coles-Tawn model.Classcom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
BivariateEVDHuslerReissThe Husler-Reiss model.Classcom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
BivariateEVDLogisticThe bivariate logistic model.Classcom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc

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BivariateEVDNegativeBilogisticThe negative bilogistic model.Classcom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
BivariateEVDNegativeLogisticThe bivariate negative logistic model.Classcom.numericalmethod.suanshu.stats.evt.evd.bivariateSuanShujavadoc
BivariateGridA rectilinear (meaning that grid lines are not necessarily equally-spaced) bivariate grid of double values.Interfacecom.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariateSuanShujavadoc
BivariateGridInterpolationA bivariate interpolation, which requires the input to form a rectilinear grid.Interfacecom.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariateSuanShujavadoc
BivariateProbabilityDistributionA 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, .Interfacecom.numericalmethod.suanshu.stats.distribution.multivariateSuanShujavadoc
BivariateRealFunctionA bivariate real function takes two real arguments and outputs one real value.Interfacecom.numericalmethod.suanshu.analysis.function.rn2r1SuanShujavadoc
BivariateRegularGridA regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes (e.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariateSuanShujavadoc
BlockSplitPointSearchComputes the splitting points with the given threshold.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
BlockWinogradAlgorithmThis implementation accelerates matrix multiplication via a combination of the Strassen algorithm and block matrix multiplication.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShujavadoc
BMSDEA Brownian motion is a stochastic process with the following properties.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discreteSuanShujavadoc
BoltzAnnealingFunctionMatlab: @annealingboltz - The step has length square root of temperature, with direction uniformly at random.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShujavadoc
BoltzTemperatureFunction(T_k = T_0 / ln(k)).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShujavadoc
BootstrapEstimatorThis class estimates the statistic of a sample using a bootstrap method.Classcom.numericalmethod.suanshu.stats.random.sampler.resamplerSuanShujavadoc
BorderedHessianA bordered Hessian matrix consists of the Hessian of a multivariate function f, and the gradient of a multivariate function g.Classcom.numericalmethod.suanshu.analysis.differentiation.multivariateSuanShujavadoc
BottomUpThis implementation traverses a directed acyclic graph starting from the leaves at the bottom, and reaches the roots.Classcom.numericalmethod.suanshu.graph.algorithm.traversalSuanShujavadoc
BoxConstraintsThis represents the lower and upper bounds for a variable.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShujavadoc
BoxGeneralizedSimulatedAnnealingMinimizerThis is an extension to GeneralizedSimulatedAnnealingMinimizer, which allows adding box constraints to bound solutions.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.boxSuanShujavadoc
BoxGSAAcceptanceProbabilityFunctionThis probability function boxes an unconstrained probability function so that when a proposed state is outside the box, it has a probability of 0.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunctionSuanShujavadoc
BoxGSAAnnealingFunctionClasscom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShujavadoc
BoxMinimizerA box minimizer solves a BoxOptimProblem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShujavadoc
BoxMullerThe 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 standardClasscom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShujavadoc
BoxOptimProblemA box constrained optimization problem, for which a solution must be within fixed bounds.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.problemSuanShujavadoc
BoxPierceThe Box-Pierce test (named for George E.Classcom.numericalmethod.suanshu.stats.test.timeseries.portmanteauSuanShujavadoc
BracketSearchMinimizerThis class provides implementation support for those univariate optimization algorithms that are based on bracketing.Classcom.numericalmethod.suanshu.optimization.univariate.bracketsearchSuanShujavadoc
BranchAndBoundBranch-and-Bound (BB or B&B) is a general algorithm for finding optimal solutions of various optimization problems, especially in discrete and combinatorial optimization.Classcom.numericalmethod.suanshu.misc.algorithm.bbSuanShujavadoc

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BrentCetaMaximizerClasscom.numericalmethod.suanshu.model.lai2010.ceta.maximizerSuanShujavadoc
BrentMinimizerBrent's algorithm is the preferred method for finding the minimum of a univariate function.Classcom.numericalmethod.suanshu.optimization.univariate.bracketsearchSuanShujavadoc
BrentRootBrent's root-finding algorithm combines super-linear convergence with reliability of bisection.Classcom.numericalmethod.suanshu.analysis.root.univariateSuanShujavadoc
BreuschPaganThe Breusch-Pagan test tests for conditional heteroskedasticity.Classcom.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticitySuanShujavadoc
BroadcastMessageA message that is sent to each slave by the master.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.messageSuanShujavadoc
BrownForsytheThe 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.Classcom.numericalmethod.suanshu.stats.test.varianceSuanShujavadoc
BruteForceIPMinimizerThis implementation solves an integral constrained minimization problem by brute force search for all possible integer combinations.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforceSuanShujavadoc
BruteForceIPProblemThis implementation is an integral constrained minimization problem that has enumerable integral domains.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforceSuanShujavadoc
BtThis is a FiltrationFunction that returns (B(t_i)), the Brownian motion value at the i-th time point.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtrationSuanShujavadoc
BurlischStoerExtrapolationBurlisch-Stoer extrapolation (or Gragg-Bulirsch-Stoer (GBS)) algorithm combines three powerful ideas: Richardson extrapolation, the use of rational function extrapolation in Richardson-typeClasscom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolationSuanShujavadoc
BurnInRNGA burn-in random number generator discards the first M samples.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc
BurnInRVGA burn-in random number generator discards the first M samples.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShujavadoc
C1See Also:Wikipedia: Smooth functionGet the gradient function, g, of a real valued function f.Interfacecom.numericalmethod.suanshu.analysis.differentiation.differentiabilitySuanShujavadoc
C2Interfacecom.numericalmethod.suanshu.analysis.differentiation.differentiabilitySuanShujavadoc
C2OptimProblemThis is an optimization problem of a real valued function that is twice differentiable.Interfacecom.numericalmethod.suanshu.optimization.problemSuanShujavadoc
C2OptimProblemImplThis is an optimization problem of a real valued function: (max_x f(x)).Classcom.numericalmethod.suanshu.optimization.problemSuanShujavadoc
CartesianProductThe Cartesian product can be generalized to the n-ary Cartesian product over n sets X1, .Classcom.numericalmethod.suanshu.misc.algorithmSuanShujavadoc
CaseResamplingReplacementThis is the classical bootstrap method described in the reference.Classcom.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrapSuanShujavadoc
CauchyPolynomialThe Cauchy's polynomial of a polynomial takes this form: C(x) = Classcom.numericalmethod.suanshu.analysis.function.polynomialSuanShujavadoc
CentralPathA central path is a solution to both the primal and dual problems of a semi-definite programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShujavadoc
CetaClasscom.numericalmethod.suanshu.model.lai2010.cetaSuanShujavadoc
CetaMaximizerInterfacecom.numericalmethod.suanshu.model.lai2010.ceta.maximizerSuanShujavadoc
ChangeOfVariableChange of variable can easy the computation of some integrals, such as improper integrals.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemannSuanShujavadoc
CharacteristicPolynomialThe characteristic polynomial of a square matrix is the function The zeros of this polynomial are the eigenvalues of A.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShujavadoc
ChebyshevRuleClasscom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.ruleSuanShujavadoc

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Cheng1978Cheng, 1978, is a new rejection method for generating beta variates.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.betaSuanShujavadoc
ChiSquareDistributionThe Chi-square distribution is the distribution of the sum of the squares of a set of statistically independent standard Gaussian random variables.Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
ChiSquareIndependenceTestPearson's chi-square test of independence assesses whether paired observations on two variables, expressed in a contingency table, are independent of each other.Classcom.numericalmethod.suanshu.stats.test.distribution.pearsonSuanShujavadoc
CholCholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShujavadoc
CholeskyCholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShujavadoc
CholeskyBanachiewiczCholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShujavadoc
CholeskyBanachiewiczParallelizedThis is a parallelized version of CholeskyBanachiewicz.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShujavadoc
CholeskySparseCholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShujavadoc
CholeskyWang2006Cholesky decomposition works only for a positive definite matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShujavadoc
ChromosomeA chromosome is a representation of a solution to an optimization problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithmSuanShujavadoc
ClusterAnalyzerThis 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.Classcom.numericalmethod.suanshu.stats.evt.clusterSuanShujavadoc
ClustersStore cluster information obtained by cluster analysis.Classcom.numericalmethod.suanshu.stats.evt.clusterSuanShujavadoc
CointegrationMLETwo 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,Classcom.numericalmethod.suanshu.stats.cointegrationSuanShujavadoc
CollectWorkerCountsRequest to collect the number of workers managed by the slaves.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.messageSuanShujavadoc
ColumnBindMatrixA fast "cbind" matrix from vectors.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
CombinedCetaMaximizerClasscom.numericalmethod.suanshu.model.lai2010.ceta.maximizerSuanShujavadoc
CombinedVectorByRefFor efficiency, this wrapper concatenates two or more vectors by references (without data copying).Classcom.numericalmethod.suanshu.algebra.linear.vector.doublesSuanShujavadoc
CommonRandomNumbersThe common random numbers is a variance reduction technique to apply when we are comparing two random systems, e.Classcom.numericalmethod.suanshu.stats.random.variancereductionSuanShujavadoc
ComplexA complex number is a number consisting of a real number part and an imaginary number part.Classcom.numericalmethod.suanshu.number.complexSuanShujavadoc
ComplexMatrixThis is a Complex matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtypeSuanShujavadoc
CompositeDoubleArrayOperationIt is desirable to have multiple implementations and switch between them for, e.Classcom.numericalmethod.suanshu.number.doublearraySuanShujavadoc
CompositeLinearCongruentialGeneratorA 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.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShujavadoc
ConcurrentCachedGeneratorA generic wrapper that makes an underlying item generator thread-safe by caching generated items in a concurrently-accessible list.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.cacheSuanShujavadoc
ConcurrentCachedRLGThis is a fast thread-safe wrapper for random long generators.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.cacheSuanShujavadoc
ConcurrentCachedRNGThis is a fast thread-safe wrapper for random number generators.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.cacheSuanShujavadoc

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ConcurrentCachedRVGThis is a fast thread-safe wrapper for random vector generators.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.cacheSuanShujavadoc
ConcurrentStandardNormalRNGClasscom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShujavadoc
ConditionalSumOfSquaresThe method Conditional Sum of Squares (CSS) fits an ARIMA model by minimizing the conditional sum of squares.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
ConfidenceIntervalThis class stores information for a list of confidence intervals, with the same confidence level.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fittingSuanShujavadoc
CongruentMatrixGiven a matrix A and an invertible matrix P, we create the congruent matrixSee Also:Wikipedia: Matrix congruenceClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
ConjugateGradientMinimizerA conjugate direction optimization method is performed by using sequential line search along directions that bear a strict mathematical relationship to one another.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirectionSuanShujavadoc
ConjugateGradientNormalErrorSolverFor an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc
ConjugateGradientNormalResidualSolverFor an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc
ConjugateGradientSolverThe Conjugate Gradient method (CG) is useful for solving a symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc
ConjugateGradientSquaredSolverThe Conjugate Gradient Squared method (CGS) is useful for solving a non-symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc
ConstantDriftVectorThe class represents a constant drift function.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficientsSuanShujavadoc
ConstantsThis class lists the global parameters and constants in this SuanShu library.Classcom.numericalmethod.suanshu.miscSuanShujavadoc
ConstantSeederA wrapper that seeds each given seedable random number generator with the given seed(s).Classcom.numericalmethod.suanshu.stats.random.rngSuanShujavadoc
ConstantSigma1The class represents a constant diffusion coefficient function.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficientsSuanShujavadoc
ConstantSigma2The class represents a constant diffusion coefficient function.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficientsSuanShujavadoc
ConstrainedCellFactoryThis defines a Differential Evolution operator that takes in account constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrainedSuanShujavadoc
ConstrainedLASSObyLARSThis class solves the constrained form of LASSO by modified least angle regression (LARS) and linear interpolation:Classcom.numericalmethod.suanshu.stats.regression.linear.lassoSuanShujavadoc
ConstrainedLASSOProblemA LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization.Classcom.numericalmethod.suanshu.stats.regression.linear.lassoSuanShujavadoc
ConstrainedMinimizerA constrained minimizer solves a constrained optimization problem, namely, ConstrainedOptimProblem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShujavadoc
ConstrainedOptimProblemA constrained optimization problem takes this form.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.problemSuanShujavadoc
ConstrainedOptimProblemImpl1This implements a constrained optimization problem for a function f subject to equality and less-than-or-equal-to constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.problemSuanShujavadoc
ConstrainedOptimSubProblemA constrained optimization sub-problem takes this form.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShujavadoc
ConstraintsA set of constraints for a (real-valued) optimization problem is a set of functions.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShujavadoc
ConstraintsUtilsThese are the utility functions for manipulating Constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShujavadoc
ContextRNGThis uniform number generator generates independent sequences of random numbers per context.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.contextSuanShujavadoc

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ContinuedFractionA 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,}}}}Classcom.numericalmethod.suanshu.analysis.function.rn2r1.univariateSuanShujavadoc
ControlVariatesControl 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 unknownClasscom.numericalmethod.suanshu.stats.random.variancereductionSuanShujavadoc
ConvectionDiffusionEquation1DClasscom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequationSuanShujavadoc
ConvergenceFailureThis exception is thrown by IterativeLinearSystemSolver.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterativeSuanShujavadoc
CorrelationMatrixThe correlation matrix of n random variables X1, .Classcom.numericalmethod.suanshu.stats.descriptive.correlationSuanShujavadoc
Corvalan2005Classcom.numericalmethod.suanshu.model.corvalan2005SuanShujavadoc
CounterA counter keeps track of the number of occurrences of numbers.Classcom.numericalmethod.suanshu.combinatoricsSuanShujavadoc
CountMonitorThis IterationMonitor counts the number of iterates generated, hence the number of iterations.Classcom.numericalmethod.suanshu.misc.algorithm.iterative.monitorSuanShujavadoc
CourantPenaltyThis penalty function sums up the squared error penalties.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShujavadoc
CovarianceCovariance is a measure of how much two variables change together.Classcom.numericalmethod.suanshu.stats.descriptive.covarianceSuanShujavadoc
CovarianceEstimationEstimates the covariance matrix by maximum likelihood.Classcom.numericalmethod.suanshu.model.daspremont2008SuanShujavadoc
CovarianceSelectionGLASSOFASTGLASSOFAST is the Graphical LASSO algorithm to solve the covariance selection problem.Classcom.numericalmethod.suanshu.model.covarianceselection.lassoSuanShujavadoc
CovarianceSelectionLASSOThe LASSO approach of covariance selection.Classcom.numericalmethod.suanshu.model.covarianceselection.lassoSuanShujavadoc
CovarianceSelectionProblemThis class defines the covariance selection problem outlined in d'Aspremont (2008).Classcom.numericalmethod.suanshu.model.covarianceselectionSuanShujavadoc
CovarianceSelectionSolverGet the estimated Covariance matrix of the selection problem.Interfacecom.numericalmethod.suanshu.model.covarianceselectionSuanShujavadoc
CramerVonMises2SamplesThis algorithm calculates the two sample Cramer-Von Mises test statistic and p-value.Classcom.numericalmethod.suanshu.stats.test.distributionSuanShujavadoc
CrankNicolsonConvectionDiffusionEquation1DThis class uses the Crank-Nicolson scheme to obtain a numerical solution of a one-dimensional convection-diffusion PDE.Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequationSuanShujavadoc
CrankNicolsonHeatEquation1DThe Crank-Nicolson method is an algorithm for obtaining a numerical solution to parabolic PDE problems.Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequationSuanShujavadoc
CSDPMinimizerSee Also:"Borchers, Brian, "CSDP, a C Library for Semidefinite Programming", Optimization Methods and Software 11(1): 613-623, 1999.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShujavadoc
CSRSparseMatrixThe Compressed Sparse Row (CSR) format for sparse matrix has this representation: (value, col_ind, row_ptr).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShujavadoc
CubicHermiteCubic 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.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.univariateSuanShujavadoc
CubicRootThis is a cubic equation solver.Classcom.numericalmethod.suanshu.analysis.function.polynomial.rootSuanShujavadoc
CubicSplineThe (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.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.univariateSuanShujavadoc
CumulativeNormalHastingsHastings algorithm is faster but less accurate way to compute the cumulative standard Normal.Classcom.numericalmethod.suanshu.analysis.function.special.gaussianSuanShujavadoc
CumulativeNormalInverseThe inverse of the cumulative standard Normal distribution function is defined as: This implementation uses the Beasley-Springer-Moro algorithm.Classcom.numericalmethod.suanshu.analysis.function.special.gaussianSuanShujavadoc

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CumulativeNormalMarsagliaMarsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal.Classcom.numericalmethod.suanshu.analysis.function.special.gaussianSuanShujavadoc
CurveFittingCurve 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.Interfacecom.numericalmethod.suanshu.analysis.curvefitSuanShujavadoc
DAgostinoD'Agostino's K2 test is a goodness-of-fit measure of departure from normality.Classcom.numericalmethod.suanshu.stats.test.distribution.normalitySuanShujavadoc
DAGraphA directed acyclic graph (DAG), is a directed graph with no directed cycles.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
Dai2011HMMCreates a two-state Geometric Brownian Motion with a constant volatility.Classcom.numericalmethod.suanshu.model.dai2011SuanShujavadoc
Dai2011SolverSolves the stochastic control problem in the referenced paper to get the two Min Dai, Qing Zhang and Qiji Jim Zhu, "Optimal Trend Following TradingClasscom.numericalmethod.suanshu.model.dai2011SuanShujavadoc
DateTimeGenericTimeSeriesClasscom.numericalmethod.suanshu.stats.timeseries.datastructureSuanShujavadoc
DateTimeTimeSeriesThis is a time series has its double values indexed by DateTime.Classcom.numericalmethod.suanshu.stats.timeseries.datastructure.univariateSuanShujavadoc
DBetaThis is the first order derivative function of the Beta function w.Classcom.numericalmethod.suanshu.analysis.differentiation.univariateSuanShujavadoc
DBetaRegularizedThis is the first order derivative function of the Regularized Incomplete Beta function, BetaRegularized, w.Classcom.numericalmethod.suanshu.analysis.differentiation.univariateSuanShujavadoc
DeepCopyableThis interface provides a way to do polymorphic copying.Interfacecom.numericalmethod.suanshu.miscSuanShujavadoc
DefaultDynamicCreatorConfigurationDefault settings for DynamicCreatorConfiguration.Classcom.numericalmethod.suanshu.grid.config.dcSuanShujavadoc
DefaultGridExecutorFactoryThe default factory that creates instances of GridExecutor.Classcom.numericalmethod.suanshu.grid.executorSuanShujavadoc
DefaultMatrixStorageThere are multiple ways to implement the storage data structure depending on the matrix type for optimization purpose.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtypeSuanShujavadoc
DefaultSimplexA simplex optimization algorithm, e.Classcom.numericalmethod.suanshu.optimization.multivariate.initializationSuanShujavadoc
DefaultTestRemoteConfigurationSimple 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.Classcom.numericalmethod.suanshu.grid.test.configSuanShujavadoc
DeflationA deflation found in a Hessenberg (or tridiagonal in symmetric case) matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShujavadoc
DeflationCriterionDetermines whether a sub-diagonal entry is sufficiently small to be neglected.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShujavadoc
DenseDataThis implementation of the storage of a dense matrix stores the data of a 2D matrix as an 1D In general, computing index for a double[] is faster than that for a double[][].Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.denseSuanShujavadoc
DenseMatrixThis class implements the standard, dense, double based matrix representation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.denseSuanShujavadoc
DenseMatrixMultiplicationMatrix operation that multiplies two matrices.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShujavadoc
DenseMatrixMultiplicationByBlockClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShujavadoc
DenseMatrixMultiplicationByIjk parallel execution with multiple threads.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShujavadoc
DenseVectorThis class implements the standard, dense, double based vector representation.Classcom.numericalmethod.suanshu.algebra.linear.vector.doubles.denseSuanShujavadoc
DensifiableThis interface specifies whether a matrix implementation can be efficiently converted to the standard dense matrix representation.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.denseSuanShujavadoc

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DEOptimDifferential Evolution (DE) is a global optimization method.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShujavadoc
DEOptimCellFactoryA DEOptimCellFactory produces DEOptimCellFactory.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShujavadoc
DErfThis is the first order derivative function of the Error function, Erf.Classcom.numericalmethod.suanshu.analysis.differentiation.univariateSuanShujavadoc
DerivativeFunctionDefines the derivative function F(x, y) for ODE problems.Interfacecom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problemSuanShujavadoc
DfdxThe first derivative is a measure of how a function changes as its input changes.Classcom.numericalmethod.suanshu.analysis.differentiation.univariateSuanShujavadoc
DFPMinimizerThe Davidon-Fletcher-Powell method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShujavadoc
DFSThis class implements the depth-first-search using iteration.Classcom.numericalmethod.suanshu.graph.algorithm.traversalSuanShujavadoc
DGammaThis is the first order derivative function of the Gamma function, ({d mathrm{Gamma}(x) over dx}).Classcom.numericalmethod.suanshu.analysis.differentiation.univariateSuanShujavadoc
DGaussianThis is the first order derivative function of a Gaussian function, ({d mathrm{phi}(x) over dx}).Classcom.numericalmethod.suanshu.analysis.differentiation.univariateSuanShujavadoc
DiagonalMatrixA diagonal matrix has non-zero entries only on the main diagonal.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonalSuanShujavadoc
DiagonalSumAdd diagonal elements to a matrix, an efficient implementation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
DifferencedIntTimeTimeSeriesDifferencing of a time series xt in discrete time t is the transformation of the series to a new time series (1-B)xt where the new valuesClasscom.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttimeSuanShujavadoc
DiffusionInterfacecom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficientsSuanShujavadoc
DiffusionMatrixInterfacecom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficientsSuanShujavadoc
DiffusionSigmaThis class implements the diffusion term in the form of a diffusion matrix.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficientsSuanShujavadoc
DigammaThe digamma function is defined as the logarithmic derivative of the gamma function.Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
DiGraphA directed graph or digraph is a graph, or set of nodes connected by edges, where the edges have a direction associated with them.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
DimensionCheckThese are the utility functions for checking table dimension.Classcom.numericalmethod.suanshu.misc.datastructureSuanShujavadoc
DirichletDistributionThe Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted Dir(a), is a family of continuous multivariate probability distributions parametrized by a vectorClasscom.numericalmethod.suanshu.stats.distribution.multivariateSuanShujavadoc
DiscreteHMMThis is the discrete hidden Markov model as defined by Rabiner.Classcom.numericalmethod.suanshu.stats.hmm.discreteSuanShujavadoc
DiscreteSDEThis interface represents the discrete approximation of a univariate SDE.Interfacecom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discreteSuanShujavadoc
DiversificationMeasureDefines the diversification of a portfolio.Interfacecom.numericalmethod.suanshu.model.corvalan2005.diversificationSuanShujavadoc
DividedDifferencesDivided differences is recursive division process for calculating the coefficients in the interpolation polynomial in the Newton form.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.univariateSuanShujavadoc
DLMThis is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.Classcom.numericalmethod.suanshu.stats.dlm.univariateSuanShujavadoc
DLMSeriesThis is a simulator for a multivariate controlled dynamic linear model process.Classcom.numericalmethod.suanshu.stats.dlm.univariateSuanShujavadoc

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DLMSimThis is a simulator for a univariate controlled dynamic linear model process.Classcom.numericalmethod.suanshu.stats.dlm.univariateSuanShujavadoc
DOKSparseMatrixThe Dictionary Of Key (DOK) format for sparse matrix uses the coordinates of non-zero entries in the matrix as keys.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShujavadoc
DoolittleDoolittle algorithm is a LU decomposition algorithm which decomposes a square matrix P is an n x n permutation matrix;L is an n x n (unit) lower triangular matrix;U is an n x n upper triangular matrix,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangleSuanShujavadoc
DoubleArrayMathThese are the math functions that operate on double[].Classcom.numericalmethod.suanshu.number.doublearraySuanShujavadoc
DoubleArrayOperationIt is possible to provide different implementations for different platforms, hardware, etc.Interfacecom.numericalmethod.suanshu.number.doublearraySuanShujavadoc
DoubleExponentialThis transformation speeds up the convergence of the Trapezoidal Rule exponentially.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
DoubleExponential4HalfRealLineThis transformation is good for the region ((0, +infty)).Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
DoubleExponential4RealLineThis transformation is good for the region ((-infty, +infty)).Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
DoubleUtilsThese are the utility functions to manipulate double and int.Classcom.numericalmethod.suanshu.numberSuanShujavadoc
DPolynomialThis is the first order derivative function of a Polynomial, which, again, is a polynomial.Classcom.numericalmethod.suanshu.analysis.differentiation.univariateSuanShujavadoc
DQDSComputes all the eigenvalues of the symmetric positive definite tridiagonal matrix associated with the qd-array Z to high relative accuracy.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqdsSuanShujavadoc
DriftInterfacecom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficientsSuanShujavadoc
DriftVectorInterfacecom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficientsSuanShujavadoc
DuplicatedAbscissaeThis exception is thrown when a function has two same x-abscissae, hence ill-defined.Classcom.numericalmethod.suanshu.analysis.function.tupleSuanShujavadoc
DynamicCreatorPerforms the Dynamic Creation algorithm (DC) to generate parameters for MersenneTwister.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreationSuanShujavadoc
DynamicCreatorConfigJava class for dynamicCreatorConfig complex type.Classcom.numericalmethod.suanshu.grid.config.xml.schemaSuanShujavadoc
DynamicCreatorConfigurationConfiguration for the Mersenne Twister Dynamic Creator (MT-DC), that is used to generate independent random number generators.Interfacecom.numericalmethod.suanshu.grid.config.dcSuanShujavadoc
DynamicCreatorExceptionIndicates that a problem has occurred in the dynamic creation process, usually because suitable parameters were not found.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreationSuanShujavadoc
EdgeAn edge connects a pair of vertices.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
EdgeBetweenessThe edge betweenness centrality is defined as the number of the shortest paths that go through an edge in a graph or network.Classcom.numericalmethod.suanshu.graph.communitySuanShujavadoc
EigenClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShujavadoc
EigenBoundUtilsUtility methods for computing bounds of eigenvalues.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
EigenCountCounts the number of eigenvalues in a symmetric tridiagonal matrix T that are less than aSee Also:"W.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
EigenCountInRangeClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
EigenDecompositionClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShujavadoc

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EigenPropertyEigenProperty is a read-only structure that contains the information about a particular eigenvalue,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShujavadoc
EigenvalueByDQDSComputes all the eigenvalues of a symmetric tridiagonal matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqdsSuanShujavadoc
ElementaryFunctionThis class contains some elementary functions for complex number, Complex.Classcom.numericalmethod.suanshu.number.complexSuanShujavadoc
ElementaryOperationThere are three elementary row operations which are equivalent to left multiplying an elementary They are row switching, row multiplication, and row addition.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
EliminationByAICIn each step, a factor is dropped if the resulting model has the least AIC, until no factor removal can result in a model with AIC lower than the current AIC.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShujavadoc
EliminationByZValueIn each step, the factor with the least z-value is dropped, until all z-values are greater than the critical value (given by the significance level).Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShujavadoc
Elliott2005DLMThis class implements the Kalman filter model as in Elliott's paper.Classcom.numericalmethod.suanshu.model.elliott2005SuanShujavadoc
ElliottOnlineFilterIt is important to note that this algorithm does not guarantee that Therefore, we need to check the outputs.Classcom.numericalmethod.suanshu.model.elliott2005SuanShujavadoc
EmpiricalACERThis class contains empirical ACER (hat{epsilon_k}(eta_i)) values and other related statistics estimated from observations.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShujavadoc
EmpiricalACEREstimationThis class estimates empirical ACER values from the given observations.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShujavadoc
EmpiricalACERStatisticsThis class contains the computed statistics of the estimated ACERs.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShujavadoc
EmpiricalDistributionAn empirical cumulative probability distribution function is a cumulative probability distribution function thatClasscom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
EpsilonStatisticsCalculatorCompute statistics: mean, confidence interval of estimated ACER values (epsilon_k(eta_i)).Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShujavadoc
EqualityConstraintsThe domain of an optimization problem may be restricted by equality constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShujavadoc
ErfThe Error function is defined as: operatorname{erf}(x) = frac{2}{sqrt{pi}}int_{0}^x e^{-t^2} dtClasscom.numericalmethod.suanshu.analysis.function.special.gaussianSuanShujavadoc
ErfcThis complementary Error function is defined as: operatorname{erfc}(x)Classcom.numericalmethod.suanshu.analysis.function.special.gaussianSuanShujavadoc
ErfInverseThe inverse of the Error function is defined as: operatorname{erf}^{-1}(x)Classcom.numericalmethod.suanshu.analysis.function.special.gaussianSuanShujavadoc
ErgodicHybridMCMCA simple decorator which will randomly vary dt between each sample.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShujavadoc
EstimatorGets the expectation of the estimator.Interfacecom.numericalmethod.suanshu.stats.randomSuanShujavadoc
EulerMethodThe Euler method is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solverSuanShujavadoc
EulerSDEThe Euler scheme is the first order approximation of an SDE.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discreteSuanShujavadoc
EvenlySpacedGridThis is an evenly spaced time grid.Classcom.numericalmethod.suanshu.stats.stochasticprocess.timegridSuanShujavadoc
ExceptionUtilsException-related utility functions.Classcom.numericalmethod.suanshu.miscSuanShujavadoc
ExpectationAtEndTimeThis class computes the expectation (mean) and the variance of a stochastic process, by Monte Carlo simulation, at the end of an interval: (E(X_T)).Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.randomSuanShujavadoc
ExplicitCentralDifference1DThis explicit central difference method is a numerical technique for solving the one-dimensional wave equation by the following explicitClasscom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1SuanShujavadoc

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ExplicitCentralDifference2DThis explicit central difference method is a numerical technique for solving the two-dimensional wave equation by the following explicitClasscom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2SuanShujavadoc
ExponentialThis transformation is good for when the lower limit is finite, the upper limit is infinite, and the integrand falls off exponentially.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
ExponentialDistributionThe exponential distribution describes the times between events in a Poisson process, a process in which events occur continuously and independently at a constant average rate.Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
ExponentialMixtureDistributionThe HMM states use the Exponential distribution to model the observations.Classcom.numericalmethod.suanshu.stats.hmm.mixture.distributionSuanShujavadoc
ExpTemperatureFunctionLogarithmic decay, where (T_k = T_0 * 0.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShujavadoc
ExtremalGeneralizedEigenvalueByGreedySearch [ min_x frac{x'Ax}{x'Bx} \ extup{s.Classcom.numericalmethod.suanshu.model.daspremont2008SuanShujavadoc
ExtremalGeneralizedEigenvalueBySDPSolves the problem described in Section 3.Classcom.numericalmethod.suanshu.model.daspremont2008SuanShujavadoc
ExtremalGeneralizedEigenvalueSolverComputes the solution to the problem described in Section 3.Interfacecom.numericalmethod.suanshu.model.daspremont2008SuanShujavadoc
ExtremalIndexByClusterSizeReciprocalThis class estimates the extremal index by the reciprocal of the average cluster size.Classcom.numericalmethod.suanshu.stats.evt.exiSuanShujavadoc
ExtremalIndexByFerroSeegersThis class estimates the extremal index from observations by the algorithm proposed by Ferro and The R equivalent function is evd::exi.Classcom.numericalmethod.suanshu.stats.evt.exiSuanShujavadoc
ExtremalIndexEstimationThe extremal index ( heta in [0,1]) characterizes the degree of local dependence in the extremes of a stationary time series.Interfacecom.numericalmethod.suanshu.stats.evt.exiSuanShujavadoc
ExtremeValueMCSimulation of first order extreme value Markov chains such that each pair of consecutive values has the dependence structure of given bivariate extreme value distributions.Classcom.numericalmethod.suanshu.stats.evt.markovchainSuanShujavadoc
FThe F-test tests whether two normal populations have the same variance.Classcom.numericalmethod.suanshu.stats.test.varianceSuanShujavadoc
F_Sum_BtDtThis represents a function of this integral I = int_{0}^{1} B(t)dtClasscom.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtrationSuanShujavadoc
F_Sum_tBtDtThis represents a function of this integral int_{0}^{1} (t - 0.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtrationSuanShujavadoc
FactorAnalysisFactor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobservedClasscom.numericalmethod.suanshu.stats.factoranalysisSuanShujavadoc
FAEstimatorThese are the estimators (estimated psi, loading matrix, scores, degrees of freedom, test statistics, p-value, etc.Classcom.numericalmethod.suanshu.stats.factoranalysisSuanShujavadoc
FailureDetectingTestRemoteConfigurationSimilar to DefaultTestRemoteConfiguration but adds failure detection.Classcom.numericalmethod.suanshu.grid.test.configSuanShujavadoc
FailureDetectionConfigJava class for failureDetectionConfig complex type.Classcom.numericalmethod.suanshu.grid.config.xml.schemaSuanShujavadoc
FailureDetectorActs on behalf of the master and keeps track of all the work that was delegated, as well as the responses that were received.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.actorSuanShujavadoc
FastAnnealingFunctionMatlab default: @annealingfast - The step has length temperature, with direction uniformly at random.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShujavadoc
FastKroneckerProductThis is a fast and memory-saving implementation of computing the Kronecker product.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
FastTemperatureFunctionLinear decay, where (T_k = T_0 / k).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShujavadoc
FDistributionThe F distribution is the distribution of the ratio of two independent chi-squared variates.Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
FerrisMangasarianWrightPhase1The phase 1 procedure finds a feasible table from an infeasible one by pivoting the simplex table of a related problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShujavadoc

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FerrisMangasarianWrightPhase2This implementation solves a canonical linear programming problem that does not need preprocessing its simplex table.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solverSuanShujavadoc
FerrisMangasarianWrightScheme2The scheme 2 procedure removes equalities and free variables.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShujavadoc
FibonacciA Fibonacci sequence starts with 0 and 1 as the first two numbers.Classcom.numericalmethod.suanshu.analysis.sequenceSuanShujavadoc
FibonaccMinimizerThe Fibonacci search is a dichotomous search where a bracketing interval is sub-divided by the Fibonacci ratio.Classcom.numericalmethod.suanshu.optimization.univariate.bracketsearchSuanShujavadoc
FieldAs an algebraic structure, every field is a ring, but not every ring is a field.Classcom.numericalmethod.suanshu.algebra.structureSuanShujavadoc
FilterA filter, for signal processing, takes (real) input signal and transforms it to (real) output signal.Interfacecom.numericalmethod.suanshu.dsp.univariate.operation.system.doublesSuanShujavadoc
FiltrationThis class represents the filtration information known at the end of time.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtrationSuanShujavadoc
FiltrationFunctionA filtration function, parameterized by a fixed filtration, is a function of time, (f(mathfrak{F_{t_i}})).Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtrationSuanShujavadoc
FiniteDifferenceA finite difference (divided by a small increment) is an approximation of the derivative of a The accuracy depends on the function to take the derivative of.Classcom.numericalmethod.suanshu.analysis.differentiation.univariateSuanShujavadoc
FirstGenerationThis interface allows customization of how the first pool of chromosomes is generated.Interfacecom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgenerationSuanShujavadoc
FirstOrderMinimizerThis implements the steepest descent line search using the first order expansion of the Taylor's series.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShujavadoc
FisherExactDistributionFisher's exact test distribution is, as its name states, exact, and can therefore be used regardless of the sample characteristics.Classcom.numericalmethod.suanshu.stats.test.distribution.pearsonSuanShujavadoc
FixedEffectsModelFits the panel data to this linear model: y_{it} = alpha_{i}+X_{it}mathbf{eta}+u_{it}Classcom.numericalmethod.suanshu.stats.regression.linear.panelSuanShujavadoc
FletcherLineSearchThis is Fletcher's inexact line search method.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearchSuanShujavadoc
FletcherPenaltyThis penalty function sums up the squared costs penalties.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShujavadoc
FletcherReevesMinimizerThe Fletcher-Reeves method is a variant of the Conjugate-Gradient method.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirectionSuanShujavadoc
FlexibleTableThis is a 2D table that can shrink or grow by row or by column.Classcom.numericalmethod.suanshu.misc.datastructureSuanShujavadoc
ForestA forest is a disjoint union of trees.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
ForwardBackwardProcedureThe forward-backward procedure is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variablesClasscom.numericalmethod.suanshu.stats.hmmSuanShujavadoc
ForwardSelectionConstructs a GLM model for a set of observations using the forward selection method.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShujavadoc
ForwardSubstitutionForward substitution solves a matrix equation in the form Lx = b by an iterative process for a lower triangular matrix L.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc
FrechetDistributionClasscom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShujavadoc
FtThis represents the concept 'Filtration', the information available at time t.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sdeSuanShujavadoc
FtAdaptedFunctionInterfacecom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sdeSuanShujavadoc
FtAdaptedRealFunctionInterfacecom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sdeSuanShujavadoc

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FtAdaptedVectorFunctionInterfacecom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sdeSuanShujavadoc
FtWtClasscom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sdeSuanShujavadoc
FunctionThe mathematical concept of a function expresses the idea that one quantity (the argument of the function, also known as the input) completely determines another quantity (the value, or output).Interfacecom.numericalmethod.suanshu.analysis.functionSuanShujavadoc
FunctionA function in the classical sense, that it maps from an input to an output.Interfacecom.numericalmethod.suanshu.grid.functionSuanShujavadoc
FunctionOpsThese are some commonly used mathematical functions.Classcom.numericalmethod.suanshu.analysis.functionSuanShujavadoc
GammaThe Gamma function is an extension of the factorial function to real and complex numbers, with its argument shifted down by 1.Interfacecom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
GammaDistributionThis gamma distribution, when k is an integer, is the distribution of the sum of k independent exponentially distributed random variables,Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
GammaGergoNemesThe Gergo Nemes' algorithm is very simple and quick to compute the Gamma function, if accuracy is not critical.Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
GammaLanczosLanczos approximation provides a way to compute the Gamma function such that the accuracy can be made arbitrarily precise.Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
GammaLanczosQuickLanczos approximation, computations are done in double.Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
GammaLowerIncompleteThe Lower Incomplete Gamma function is defined as: gamma(s,x) = int_0^x t^{s-1},e^{-t},{ m d}t = P(s,x)Gamma(s)Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
GammaMixtureDistributionThe HMM states use the Gamma distribution to model the observations.Classcom.numericalmethod.suanshu.stats.hmm.mixture.distributionSuanShujavadoc
GammaRegularizedPThe Regularized Incomplete Gamma P function is defined as: P(s,x) = frac{gamma(s,x)}{Gamma(s)} = 1 - Q(s,x), s geq 0, x geq 0Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
GammaRegularizedPInverseThe inverse of the Regularized Incomplete Gamma P function is defined as: x = P^{-1}(s,u), 0 geq u geq 1Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
GammaRegularizedQThe Regularized Incomplete Gamma Q function is defined as: Q(s,x)=frac{Gamma(s,x)}{Gamma(s)}=1-P(s,x), s geq 0, x geq 0Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
GammaUpperIncompleteThe Upper Incomplete Gamma function is defined as: Gamma(s,x) = int_x^{infty} t^{s-1},e^{-t},{ m d}t = Q(s,x) imes Gamma(s)Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
GARCH11ModelAn GARCH11 model takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garchSuanShujavadoc
GARCHFitThis implementation fits, for a data set, a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modelClasscom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garchSuanShujavadoc
GARCHModelThe GARCH(p, q) model takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garchSuanShujavadoc
GARCHResamplerFactoryClasscom.numericalmethod.suanshu.model.lai2010.ceta.npeb.resamplerSuanShujavadoc
GARCHSimThis class simulates the GARCH models of this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garchSuanShujavadoc
GaussChebyshevQuadratureGauss-Chebyshev Quadrature uses the following weighting function: w(x) = frac{1}{sqrt{1 - x^2}}Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussianSuanShujavadoc
GaussHermiteQuadratureGauss-Hermite quadrature exploits the fact that quadrature approximations are open integration formulas (that is, the values of the endpoints are not required) to evaluate of integrals in theClasscom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussianSuanShujavadoc
GaussianThe Gaussian function is defined as: f(x) = a e^{- { frac{(x-b)^2 }{ 2 c^2} } }Classcom.numericalmethod.suanshu.analysis.function.special.gaussianSuanShujavadoc
GaussianEliminationThe Gaussian elimination performs elementary row operations to reduce a matrix to the row echelon form.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianeliminationSuanShujavadoc

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GaussianElimination4SquareMatrixThis is a wrapper for GaussianElimination but applies only to square matrices.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianeliminationSuanShujavadoc
GaussianProposalFunctionA proposal generator where each perturbation is a random vector, where each element is drawn from a standard Normal distribution, multiplied by a scale matrix.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunctionSuanShujavadoc
GaussianQuadratureA quadrature rule is a method of numerical integration in which we approximate the integral of a function by a weighted sum of sample points.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussianSuanShujavadoc
GaussianQuadratureRuleThis interface defines a Gaussian quadrature rule used in Gaussian quadrature.Interfacecom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.ruleSuanShujavadoc
GaussJordanEliminationGauss-Jordan elimination performs elementary row operations to reduce a matrix to the reduced row echelon form.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianeliminationSuanShujavadoc
GaussLaguerreQuadratureGauss-Laguerre quadrature exploits the fact that quadrature approximations are open integration formulas (i.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussianSuanShujavadoc
GaussLegendreQuadratureGauss-Legendre quadrature considers the simplest case of uniform weighting: (w(x) = 1).Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussianSuanShujavadoc
GaussNewtonMinimizerThe Gauss-Newton method is a steepest descent method to minimize a real vector function in the form: f(x) = [f_1(x), f_2(x), .Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShujavadoc
GaussSeidelSolverSimilar to the Jacobi method, the Gauss-Seidel method (GS) solves each equation in sequential order.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShujavadoc
GBMProcessA Geometric Brownian motion (GBM) (occasionally, exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.processSuanShujavadoc
GeneralConstraintsThe real-valued constraints define the domain (feasible regions) for a real-valued objective function in a constrained optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.generalSuanShujavadoc
GeneralEqualityConstraintsThis is the collection of equality constraints for an optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.generalSuanShujavadoc
GeneralGreaterThanConstraintsThis is the collection of greater-than-or-equal-to constraints for an optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.generalSuanShujavadoc
GeneralizedConjugateResidualSolverThe Generalized Conjugate Residual method (GCR) is useful for solving a non-symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc
GeneralizedEVDClasscom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShujavadoc
GeneralizedLinearModelThe Generalized Linear Model (GLM) is a flexible generalization of the Ordinary Least Squares regression.Classcom.numericalmethod.suanshu.stats.regression.linear.glmSuanShujavadoc
GeneralizedLinearModelQuasiFamilyGLM for the quasi-families.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.quasiSuanShujavadoc
GeneralizedMinimalResidualSolverThe Generalized Minimal Residual method (GMRES) is useful for solving a non-symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc
GeneralizedParetoDistributionGeneralized Pareto distribution (GPD) is used for modeling exceedances over (or shortfalls below) a threshold.Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShujavadoc
GeneralizedSimulatedAnnealingMinimizerTsallis and Stariolo (1996) proposed this variant of SimulatedAnnealingMinimizer (SA).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealingSuanShujavadoc
GeneralLessThanConstraintsThis is the collection of less-than or equal-to constraints for an optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.generalSuanShujavadoc
GenericFieldMatrixThis is a generic matrix over a Field.Classcom.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtypeSuanShujavadoc
GenericMatrixThis class defines a matrix over a field.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.genericSuanShujavadoc
GenericMatrixAccessThis interface defines the methods for accessing entries in a matrix over a field.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.genericSuanShujavadoc
GenericTimeTimeSeriesThis is a univariate time series indexed by some notion of time.Classcom.numericalmethod.suanshu.stats.timeseries.datastructure.univariateSuanShujavadoc

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GeneticAlgorithmA genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithmSuanShujavadoc
GetResultsBasic message that is used to ask the for the final result.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.messageSuanShujavadoc
GetvecClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvecSuanShujavadoc
GEVFittingByMaximumLikelihoodEstimate the GeneralizedEVD parameter from the observations by maximum likelihood approach.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fittingSuanShujavadoc
GirvanNewmanClasscom.numericalmethod.suanshu.graph.communitySuanShujavadoc
GirvanNewmanUnDiGraphClasscom.numericalmethod.suanshu.graph.communitySuanShujavadoc
GivensMatrixGivens rotation is a rotation in the plane spanned by two coordinates axes.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtypeSuanShujavadoc
GlejserThe Glejser test tests for conditional heteroskedasticity.Classcom.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticitySuanShujavadoc
GLMBetaClasscom.numericalmethod.suanshu.stats.regression.linear.glmSuanShujavadoc
GLMBinomialThis is the Binomial distribution of the error distribution in GLM model.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.distributionSuanShujavadoc
GLMExponentialDistributionThis interface represents a probability distribution from the exponential family.Interfacecom.numericalmethod.suanshu.stats.regression.linear.glm.distributionSuanShujavadoc
GLMFamilyFamily provides a convenient way to specify the error distribution and link function used in GLM model.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.distributionSuanShujavadoc
GLMFittingInterfacecom.numericalmethod.suanshu.stats.regression.linear.glmSuanShujavadoc
GLMGammaThis is the Gamma distribution of the error distribution in GLM model.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.distributionSuanShujavadoc
GLMGaussianThis is the Gaussian distribution of the error distribution in GLM model.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.distributionSuanShujavadoc
GLMInverseGaussianThis is the Inverse Gaussian distribution of the error distribution in GLM model.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.distributionSuanShujavadoc
GLMModelSelectionGiven a set of observations {y, X}, we would like to construct a GLM to explain the data.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShujavadoc
GLMPoissonThis is the Poisson distribution of the error distribution in GLM model.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.distributionSuanShujavadoc
GLMProblemThis is a Generalized Linear regression problem.Classcom.numericalmethod.suanshu.stats.regression.linear.glmSuanShujavadoc
GLMResidualsResidual analysis of the results of a Generalized Linear Model regression.Classcom.numericalmethod.suanshu.stats.regression.linear.glmSuanShujavadoc
GlobalSearchByLocalMinimizerThis minimizer is a global optimization method.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.localSuanShujavadoc
GoldenMinimizerThis is the golden section univariate minimization algorithm.Classcom.numericalmethod.suanshu.optimization.univariate.bracketsearchSuanShujavadoc
GoldfeldQuandtTrotterGoldfeld, Quandt and Trotter propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefiniteSuanShujavadoc
GolubKahanSVDGolub-Kahan algorithm does the SVD decomposition of a tall matrix in two stages.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svdSuanShujavadoc
GomoryMixedCutMinimizerThis cutting-plane implementation uses Gomory's mixed cut method.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShujavadoc

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GomoryPureCutMinimizerThis cutting-plane implementation uses Gomory's pure cut method for pure integer programming, in which all variables are integral.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShujavadoc
GradientThe gradient of a scalar field is a vector field which points in the direction of the greatest rate of increase of the scalar field, and of which the magnitude is the greatest rate of change.Classcom.numericalmethod.suanshu.analysis.differentiation.multivariateSuanShujavadoc
GradientFunctionThe gradient function, g(x), evaluates the gradient of a real scalar function f at a point x.Classcom.numericalmethod.suanshu.analysis.differentiation.multivariateSuanShujavadoc
GramSchmidtThe Gram-Schmidt process is a method for orthogonalizing a set of vectors in an inner product space.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qrSuanShujavadoc
GraphA graph is a representation of a set of objects where some pairs of the objects are connected by links.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
GraphTraversalA spanning tree T of a connected, undirected graph G is a tree composed of all the vertices and some (or perhaps all) of the edges of G.Interfacecom.numericalmethod.suanshu.graph.algorithm.traversalSuanShujavadoc
GraphUtilsThese are the utility functions to manipulate Graph.Classcom.numericalmethod.suanshu.graphSuanShujavadoc
GreaterThanConstraintsThe domain of an optimization problem may be restricted by greater-than or equal-to constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShujavadoc
GridConfigJava class for gridConfig complex type.Classcom.numericalmethod.suanshu.grid.config.xml.schemaSuanShujavadoc
GridExecutorInstances of this class can run code that can be distributed across multiple machines (or CPU Note that if a function invocation fails, for example by throwing an exception, null willInterfacecom.numericalmethod.suanshu.grid.executorSuanShujavadoc
GridExecutorFactoryFactory that creates GridExecutor.Interfacecom.numericalmethod.suanshu.grid.executorSuanShujavadoc
GridExecutorFactoryFromConfigGenerates GridExecutor according to the settings in the configuration file.Classcom.numericalmethod.suanshu.grid.config.xmlSuanShujavadoc
GridRouterConfigAssigns work to slaves (that is, routing) in an efficient manner.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.actorSuanShujavadoc
GridSearchCetaMaximizerClasscom.numericalmethod.suanshu.model.lai2010.ceta.maximizerSuanShujavadoc
GridSearchMinimizerThis performs a grid search to find the minimum of a univariate function.Classcom.numericalmethod.suanshu.optimization.univariateSuanShujavadoc
GroupResamplerClasscom.numericalmethod.suanshu.stats.random.sampler.resampler.multivariateSuanShujavadoc
GroupResamplerFactoryCreates re-samplers that do re-sampling for the whole group of stocks together.Classcom.numericalmethod.suanshu.model.lai2010.ceta.npeb.resamplerSuanShujavadoc
GSAAcceptanceProbabilityFunctionThe GSA acceptance probability function.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunctionSuanShujavadoc
GSAAnnealingFunctionThe GSA proposal/annealing function.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShujavadoc
GSATemperatureFunctionThe GSA temperature function.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShujavadoc
GumbelDistributionThe Gumbel distribution is a special case (Type I) of the generalized extreme value distribution, The cumulative distribution function isClasscom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShujavadoc
HalleyRootHalley's method is an iterative root finding method for a univariate function with a continuous second derivative, i.Classcom.numericalmethod.suanshu.analysis.root.univariateSuanShujavadoc
HarveyGodfreyThe Harvey-Godfrey test tests for conditional heteroskedasticity.Classcom.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticitySuanShujavadoc
HConstructionA construction of extreme and trade points based on H discretization, ignoring changes smaller than H.Classcom.numericalmethod.suanshu.model.hvolatilitySuanShujavadoc
HeatEquation1DA one-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the frac{partial u}{partial t} = eta frac{partial^2 u}{partial x^2},Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequationSuanShujavadoc

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HeatEquation2DA two-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the frac{partial u}{partial t}Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2SuanShujavadoc
HermitePolynomialsA Hermite polynomial is defined by the recurrence relation below.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.ruleSuanShujavadoc
HermiteRuleClasscom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.ruleSuanShujavadoc
HessenbergAn upper Hessenberg matrix is a square matrix which has zero entries below the first 0 & 9 & 10 & 11 & \Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShujavadoc
HessenbergDecompositionGiven a square matrix A, we find Q such that Q' * A * Q = H where H is a Hessenberg matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShujavadoc
HessenbergDeflationSearchGiven a Hessenberg matrix, this class searches the largest unreduced Hessenberg sub-matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShujavadoc
HessianThe Hessian matrix is the square matrix of the second-order partial derivatives of a multivariate function.Classcom.numericalmethod.suanshu.analysis.differentiation.multivariateSuanShujavadoc
HessianFunctionThe Hessian function, H(x), evaluates the Hessian of a real scalar function f at a point x.Classcom.numericalmethod.suanshu.analysis.differentiation.multivariateSuanShujavadoc
HeteroskedasticityA heteroskedasticity test tests, for a linear regression model, whether the estimated variance of the residuals from a regression is dependent on the values of the independent variables (regressors).Classcom.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticitySuanShujavadoc
HiddenMarkovModelClasscom.numericalmethod.suanshu.stats.hmmSuanShujavadoc
HilbertMatrixA Hilbert matrix, H, is a symmetric matrix with entries being the unit fractions H[i][j] = 1 / (i + j -1)Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtypeSuanShujavadoc
HilbertSpaceA Hilbert space is an inner product space, an abstract vector space in which distances and angles can be measured.Interfacecom.numericalmethod.suanshu.algebra.structureSuanShujavadoc
HmmInnovationAn HMM innovation consists of a state and an observation in the state.Classcom.numericalmethod.suanshu.stats.hmmSuanShujavadoc
HMMRNGIn a (discrete) hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible.Classcom.numericalmethod.suanshu.stats.hmmSuanShujavadoc
HomogeneousPathFollowingMinimizerThis implementation solves a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShujavadoc
HornerSchemeHorner scheme is an algorithm for the efficient evaluation of polynomials in monomial form.Classcom.numericalmethod.suanshu.analysis.function.polynomialSuanShujavadoc
HostDefines a host on a remote (or local) port.Classcom.numericalmethod.suanshu.grid.config.remoteSuanShujavadoc
Householder4SubVectorFaster implementation of Householder reflection for sub-vectors at a given index.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShujavadoc
Householder4ZeroGeneratorFaster implementation of Householder reflection for zero generator vector.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShujavadoc
HouseholderContextThis is the context information about a Householder transformation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShujavadoc
HouseholderInPlaceMaintains the matrix to be transformed by a sequence of Householder reflections.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShujavadoc
HouseholderQRSuccessive Householder reflections gradually transform a matrix A to the upper triangular For example, the first step is to multiply A with a Householder matrixClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qrSuanShujavadoc
HouseholderReflectionA Householder transformation in the 3-dimensional space is the reflection of a vector in the The plane, containing the origin, is uniquely defined by a unit vector,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShujavadoc
HpThis is the symmetrization operator as defined in equation (6) in the reference.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShujavadoc
HuangMinimizerHuang's updating formula is a family of formulas which encompasses the rank-one, DFP, BFGS as well as some other formulas.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShujavadoc

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HybridMCMCThis class implements a hybrid MCMC algorithm.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShujavadoc
HybridMCMCProposalFunctionClasscom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunctionSuanShujavadoc
HyperEdgeA hyper-edge connects a set of vertices of any size.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
HypersphereRVGGenerates uniformly distributed points on a unit hypersphere.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShujavadoc
HypothesisTestA statistical hypothesis test is a method of making decisions using experimental data.Classcom.numericalmethod.suanshu.stats.testSuanShujavadoc
IdentityHashSetThis class implements the Set interface with a hash table, using reference-equality in place of object-equality when comparing keys and values.Classcom.numericalmethod.suanshu.misc.datastructureSuanShujavadoc
IdentityPreconditionerThis identity preconditioner is used when no preconditioning is applied.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShujavadoc
IIDAn i.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShujavadoc
ILPBranchAndBoundMinimizerThis is a Branch-and-Bound algorithm that solves Integer Linear Programming problems.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bbSuanShujavadoc
ILPNodeThis is the branch-and-bound node used in conjunction with ILPBranchAndBoundMinimizer to solve an Integer Linear Programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bbSuanShujavadoc
ILPProblemA linear program in real variables is said to be integral if it has at least one optimal solution which is integral.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problemSuanShujavadoc
ILPProblemImpl1This implementation is an ILP problem, in which the variables can be real or integral.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problemSuanShujavadoc
ImmutableMatrixThis is a read-only view of a Matrix instance.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShujavadoc
ImmutableVectorThis is a read-only view of a Vector instance.Classcom.numericalmethod.suanshu.algebra.linear.vector.doublesSuanShujavadoc
ImportanceSamplingImportance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated from a different distribution rather than theClasscom.numericalmethod.suanshu.stats.random.variancereductionSuanShujavadoc
IndependentCoVARThis algorithm finds the independent variables based on the covariance matrix.Classcom.numericalmethod.suanshu.model.daspremont2008SuanShujavadoc
Infantino2010PCAThe objective is to predict the next H-period accumulated returns from the past H-period dimensionally reduced returns.Classcom.numericalmethod.suanshu.model.infantino2010SuanShujavadoc
Infantino2010RegimeDetects the current regime (mean reversion or momentum) by cross-sectional volatility.Classcom.numericalmethod.suanshu.model.infantino2010SuanShujavadoc
InitDynamicCreatorA message sent to Slaves to indicate that it should run the DynamicCreator algorithm for all workers according or to Workers to indicate that they should run it toClasscom.numericalmethod.suanshu.grid.executor.remote.akka.messageSuanShujavadoc
InitialsFactorySome optimization algorithms, e.Interfacecom.numericalmethod.suanshu.optimization.multivariate.initializationSuanShujavadoc
InnerProductThe Frobenius inner product is the component-wise inner product of two matrices as though they are vectors.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
InnovationsAlgorithmThe innovations algorithm is an efficient way to obtain a one step least square linear predictor for a univariate linear time series with known auto-covariance and these properties (not limitedClasscom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocessSuanShujavadoc
IntegralThe class represents an integral of a function, in the Lebesgue sense.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.integrationSuanShujavadoc
IntegralConstrainedCellFactoryThis implementation defines the constrained Differential Evolution operators that solve an Integer Programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrainedSuanShujavadoc
IntegralDBThis class evaluates the following class of integrals.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.integrationSuanShujavadoc

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IntegralDtThis class evaluates the following class of integrals.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.integrationSuanShujavadoc
IntegralExpectationThis class computes the expectation of the following class of integrals.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.integrationSuanShujavadoc
IntegratorThis defines the interface for the numerical integration of definite integrals of univariate functions.Interfacecom.numericalmethod.suanshu.analysis.integration.univariate.riemannSuanShujavadoc
InterpolationInterpolation is a method of constructing new data points within the range of a discrete set of known data points.Interfacecom.numericalmethod.suanshu.analysis.curvefit.interpolation.univariateSuanShujavadoc
IntervalClasscom.numericalmethod.suanshu.intervalSuanShujavadoc
IntervalRelationenum IntervalRelationAllen's Interval Algebra is a calculus for temporal reasoning that was introduced by James F.Classcom.numericalmethod.suanshu.intervalSuanShujavadoc
IntervalsThis is a disjoint set of intervals.Classcom.numericalmethod.suanshu.intervalSuanShujavadoc
IntTimeTimeSeriesThis is a univariate time series indexed by integers.Interfacecom.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttimeSuanShujavadoc
InvalidLicenseThis is the LicenseError thrown when calling a class or method that is not yet licensed.Classcom.numericalmethod.suanshu.misc.licenseSuanShujavadoc
InverseFor a square matrix A, the inverse, A-1, if exists, satisfies A.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
InverseIterationInverse iteration is an iterative eigenvalue algorithm.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShujavadoc
InverseTransformSamplingInverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, Smirnov transform, golden rule, etc.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc
InverseTransformSamplingEVDRNGGenerate random numbers according to a given univariate extreme value distribution, by inverse transform sampling.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.rngSuanShujavadoc
InverseTransformSamplingExpRNGThis is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.expSuanShujavadoc
InverseTransformSamplingGammaRNGThis is a pseudo random number generator that samples from the gamma distribution using the inverse transform sampling method.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShujavadoc
InverseTransformSamplingTruncatedNormalRNGA random variate x defined as x = Phi^{-1}( Phi(alpha) + Ucdot(Phi(eta)-Phi(alpha)))sigma + muClasscom.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncatedSuanShujavadoc
InvertingVariableThis is the inverting-variable transformation.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
IPMinimizerAn Integer Programming minimizer minimizes an objective function subject to equality/inequality constraints as well as integral constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.integerSuanShujavadoc
IPProblemAn Integer Programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.integerSuanShujavadoc
IPProblemImpl1This is an implementation of a general Integer Programming problem in which some variables take only integers.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integerSuanShujavadoc
IteratesMonitorThis IterationMonitor stores all states generated during iterations.Classcom.numericalmethod.suanshu.misc.algorithm.iterative.monitorSuanShujavadoc
IterationBodyThis interface defines the code snippet to be run in parallel.Interfacecom.numericalmethod.suanshu.misc.parallelSuanShujavadoc
IterationMonitorTo debug an iterative algorithm, such as in IterativeMethod, it is useful to keep track of the all states generated in the iterations.Interfacecom.numericalmethod.suanshu.misc.algorithm.iterative.monitorSuanShujavadoc
IterativeC2MaximizerA maximization problem is simply minimizing the negative of the objective function.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2SuanShujavadoc
IterativeC2MinimizerThis is a minimizer that minimizes a twice continuously differentiable, multivariate function.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2SuanShujavadoc

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IterativeCentralDifferenceAn iterative central difference algorithm to obtain a numerical approximation to Poisson's equations with Dirichlet boundary conditions.Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2SuanShujavadoc
IterativeIntegratorAn iterative integrator computes an integral by a series of sums, which approximates the value of the integral.Interfacecom.numericalmethod.suanshu.analysis.integration.univariate.riemannSuanShujavadoc
IterativeLinearSystemSolverAn iterative method for solving an N-by-N (or non-square) linear system Ax = b involves a sequence of matrix-vector multiplications.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterativeSuanShujavadoc
IterativeMethodAn iterative method is a mathematical procedure that generates a sequence of improving approximate solutions for a class of problems.Interfacecom.numericalmethod.suanshu.misc.algorithm.iterativeSuanShujavadoc
IterativeMinimizerThis is an iterative multivariate minimizer.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrainedSuanShujavadoc
IterativeSolutionMany minimization algorithms work by starting from some given initials and iteratively moving toward an approximate solution.Interfacecom.numericalmethod.suanshu.optimizationSuanShujavadoc
IWLSClasscom.numericalmethod.suanshu.stats.regression.linear.glmSuanShujavadoc
JacobianThe Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function.Classcom.numericalmethod.suanshu.analysis.differentiation.multivariateSuanShujavadoc
JacobianFunctionThe Jacobian function, J(x), evaluates the Jacobian of a real vector-valued function f at a point x.Classcom.numericalmethod.suanshu.analysis.differentiation.multivariateSuanShujavadoc
JacobiPreconditionerThe Jacobi (or diagonal) preconditioner is one of the simplest forms of preconditioning, such that the preconditioner is the diagonal ofClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShujavadoc
JacobiSolverThe Jacobi method solves sequentially n equations in a linear system Ax = b in isolation in each iteration.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShujavadoc
JarqueBeraThe Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the sample kurtosis and skewness.Classcom.numericalmethod.suanshu.stats.test.distribution.normalitySuanShujavadoc
JarqueBeraDistributionJarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality.Classcom.numericalmethod.suanshu.stats.test.distribution.normalitySuanShujavadoc
JenkinsTraubRealThe Jenkins-Traub algorithm is a fast globally convergent iterative method for solving for polynomial roots.Classcom.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraubSuanShujavadoc
JodaTimeUtilsThese are the utility functions to manipulate JodaTime.Classcom.numericalmethod.suanshu.misc.datastructure.timeSuanShujavadoc
JohansenAsymptoticDistributionJohansen provides the asymptotic distributions of the two hypothesis testings (Eigen and Trace tests),Classcom.numericalmethod.suanshu.stats.cointegrationSuanShujavadoc
JohansenTestThe maximum number of cointegrating relations among a multivariate time series is the rank of the To determine the (most likely) number of cointegrating relations r,Classcom.numericalmethod.suanshu.stats.cointegrationSuanShujavadoc
JordanExchangeJordan Exchange swaps the r-th entering variable (row) with the s-th leaving variable (column) in a matrix A.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShujavadoc
KagiKAGI construction of a random process.Classcom.numericalmethod.suanshu.model.hvolatilitySuanShujavadoc
Kagi2KAGI construction of a random process which is assumed to be equi-distance in time.Classcom.numericalmethod.suanshu.model.hvolatilitySuanShujavadoc
KagiModelMaintains the states of a KAGI model.Classcom.numericalmethod.suanshu.model.hvolatilitySuanShujavadoc
KendallRankCorrelationClasscom.numericalmethod.suanshu.stats.descriptive.correlationSuanShujavadoc
KernelThe kernel or null space (also nullspace) of a matrix A is the set of all vectors x for which Ax = 0.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc
KernelA kernel that can be used for standalone operation of a Slave, i.Classcom.numericalmethod.suanshu.grid.executor.remote.akkaSuanShujavadoc
KnightSatchellTran1995See Also:Emmanual Acar, Stephen Satchell.Classcom.numericalmethod.suanshu.model.kst1995SuanShujavadoc

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KnightSatchellTran1995MLEFits a KST model from returns.Classcom.numericalmethod.suanshu.model.kst1995SuanShujavadoc
Knuth1969This is a random number generator that generates random deviates according to the Poisson Generating Poisson-distributed random variablesClasscom.numericalmethod.suanshu.stats.random.rng.univariate.poissonSuanShujavadoc
KolmogorovDistributionThe Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic.Classcom.numericalmethod.suanshu.stats.test.distribution.kolmogorovSuanShujavadoc
KolmogorovOneSidedDistributionClasscom.numericalmethod.suanshu.stats.test.distribution.kolmogorovSuanShujavadoc
KolmogorovSmirnovThe Kolmogorov-Smirnov test (KS test) compares a sample with a reference probability distribution (one-sample KS test), or to compare two samples (two-sample KS test).Classcom.numericalmethod.suanshu.stats.test.distribution.kolmogorovSuanShujavadoc
KolmogorovSmirnov1SampleThe one-sample Kolmogorov-Smirnov test (one-sample KS test) compares a sample with a reference probability distribution.Classcom.numericalmethod.suanshu.stats.test.distribution.kolmogorovSuanShujavadoc
KolmogorovSmirnov2SamplesThe two-sample Kolmogorov-Smirnov test (two-sample KS test) tests for the equality of the distributions of two samples.Classcom.numericalmethod.suanshu.stats.test.distribution.kolmogorovSuanShujavadoc
KolmogorovTwoSamplesDistributionCompute the p-values for the generalized (conditionally distribution-free) Smirnov homogeneity test.Classcom.numericalmethod.suanshu.stats.test.distribution.kolmogorovSuanShujavadoc
KroneckerProductGiven an m-by-n matrix A and a p-by-q matrix B, their Kronecker product C, also called their matrix direct product, isClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
KruskalWallisThe Kruskal-Wallis test is a non-parametric method for testing the equality of population medians among groups.Classcom.numericalmethod.suanshu.stats.test.rankSuanShujavadoc
KryoSerializerClasscom.numericalmethod.suanshu.grid.executor.remote.akka.serializationSuanShujavadoc
KunduGupta2007Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized exponential distribution,Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShujavadoc
KurtosisKurtosis measures the "peakedness" of the probability distribution of a real-valued random Higher kurtosis means that there are more infrequent extreme deviations than frequent modestlyClasscom.numericalmethod.suanshu.stats.descriptive.momentSuanShujavadoc
LaguerrePolynomialsLaguerre polynomials are defined by the recurrence relation below.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.ruleSuanShujavadoc
LaguerreRuleClasscom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.ruleSuanShujavadoc
Lai2010NPEBModelThe Non-Parametric Empirical Bayes (NPEB) model described in the reference computes the optimal weights for asset allocation.Classcom.numericalmethod.suanshu.model.lai2010SuanShujavadoc
LanczosThe Lanczos approximation is a method for computing the Gamma function numerically, published by Cornelius Lanczos in 1964.Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
LARSFittingThis class computes the entire LARS sequence of coefficients and fits, starting from zero to theSee Also:B.Classcom.numericalmethod.suanshu.stats.regression.linear.lasso.larsSuanShujavadoc
LARSProblemLeast Angle Regression (LARS) is a regression algorithm for high-dimensional data.Classcom.numericalmethod.suanshu.stats.regression.linear.lasso.larsSuanShujavadoc
LDDecompositionRepresents a L D LT decomposition of a shifted symmetric tridiagonal matrix where T is a symmetric tridiagonal matrix,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
LDFactorizationFromRootClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
LDLtThe LDL decomposition decomposes a real and symmetric (hence square) matrix A into A = L * D * Lt.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangleSuanShujavadoc
LeapFroggingThe leap-frogging algorithm is a method for simulating Molecular Dynamics, which isSee Also:"Jun S.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShujavadoc
LeastPthThe least p-th minmax algorithm minimizes the maximal error/loss (function): min_x max_{omega in S} e(x, omega)Classcom.numericalmethod.suanshu.optimization.multivariate.minmaxSuanShujavadoc
LeastSquaresThis method obtains a least squares estimate of a polynomial to fit the input data, by a weighted sum of orthogonal polynomials up to a specified order.Classcom.numericalmethod.suanshu.analysis.curvefitSuanShujavadoc

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LebesgueLebesgue integration is the general theory of integration of a function with respect to a general measure.Classcom.numericalmethod.suanshu.analysis.integration.univariateSuanShujavadoc
LEcuyerThis is the uniform random number generator recommended by L'Ecuyer in 1996.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShujavadoc
LedoitWolf2004To estimate the covariance matrix, Ledoit and Wolf (2004) suggests using the matrix obtained from the sample covariance matrix through a transformation called shrinkage.Classcom.numericalmethod.suanshu.stats.descriptive.covarianceSuanShujavadoc
LegendrePolynomialsA Legendre polynomial is defined by the recurrence relation below.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.ruleSuanShujavadoc
LegendreRuleClasscom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.ruleSuanShujavadoc
LehmerLehmer proposed a general linear congruential generator that generates pseudo-random numbers in xi+1 = (a * xi + c) mod mClasscom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShujavadoc
LessThanConstraintsThe domain of an optimization problem may be restricted by less-than or equal-to constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShujavadoc
LeveneThe Levene test tests for the equality of variance of groups.Classcom.numericalmethod.suanshu.stats.test.varianceSuanShujavadoc
LicenseThis is the license management system for the library.Classcom.numericalmethod.suanshu.misc.licenseSuanShujavadoc
LicenseErrorGeneral error regarding the license, e.Classcom.numericalmethod.suanshu.misc.licenseSuanShujavadoc
LillieforsLilliefors test tests the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance.Classcom.numericalmethod.suanshu.stats.test.distribution.normalitySuanShujavadoc
LILSparseMatrixThe list of lists (LIL) format for sparse matrix stores one list per row, where each entry stores a column index and value.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShujavadoc
LinearCongruentialGeneratorA linear congruential generator (LCG) produces a sequence of pseudo-random numbers based on a linear recurrence relation.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShujavadoc
LinearConstraintsThis is a collection of linear constraints for a real-valued optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShujavadoc
LinearEqualityConstraintsThis is a collection of linear equality constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShujavadoc
LinearFitFind the parameters for the ACER function from the given empirical epsilon, using OLS regression on the logarithm of the values.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShujavadoc
LinearGreaterThanConstraintsThis is a collection of linear greater-than-or-equal-to constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShujavadoc
LinearInterpolation(Piecewise-)Linear interpolation fits a curve by interpolating linearly between two adjacent data-points.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.univariateSuanShujavadoc
LinearInterpolatorDefine a univariate function by linearly interpolating between adjacent points.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolationSuanShujavadoc
LinearKalmanFilterThe Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time,Classcom.numericalmethod.suanshu.stats.dlm.univariateSuanShujavadoc
LinearLessThanConstraintsThis is a collection of linear less-than-or-equal-to constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShujavadoc
LinearModelA linear model provides fitting and the residual analysis (goodness of fit).Interfacecom.numericalmethod.suanshu.stats.regression.linearSuanShujavadoc
LinearRepresentationThe linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
LinearRootThis is a solver for finding the roots of a linear equation.Classcom.numericalmethod.suanshu.analysis.function.polynomial.rootSuanShujavadoc
LinearSystemSolverSolve a system of linear equations in the form: We assume that, after row reduction, A has no more rows than columns.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc

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LineSearchA line search is often used in another minimization algorithm to improve the current solution in one iteration step.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearchSuanShujavadoc
LineSegmentRepresent a line segment.Classcom.numericalmethod.suanshu.geometrySuanShujavadoc
LinkCloglogThis class represents the complementary log-log link function: g(x) = log(-log(1 - x))Classcom.numericalmethod.suanshu.stats.regression.linear.glm.distribution.linkSuanShujavadoc
LinkFunctionThis interface represents a link function g(x) in Generalized Linear Model (GLM).Interfacecom.numericalmethod.suanshu.stats.regression.linear.glm.distribution.linkSuanShujavadoc
LinkIdentityThis class represents the identity link function:See Also:GeneralizedLinearModelClasscom.numericalmethod.suanshu.stats.regression.linear.glm.distribution.linkSuanShujavadoc
LinkInverseThis class represents the inverse link function:See Also:GeneralizedLinearModelClasscom.numericalmethod.suanshu.stats.regression.linear.glm.distribution.linkSuanShujavadoc
LinkInverseSquaredThis class represents the inverse-squared link function:See Also:GeneralizedLinearModelClasscom.numericalmethod.suanshu.stats.regression.linear.glm.distribution.linkSuanShujavadoc
LinkLogThis class represents the log link function:See Also:GeneralizedLinearModelClasscom.numericalmethod.suanshu.stats.regression.linear.glm.distribution.linkSuanShujavadoc
LinkLogitThis class represents the logit link function: g(x) = log(frac{mu}{1-mu})Classcom.numericalmethod.suanshu.stats.regression.linear.glm.distribution.linkSuanShujavadoc
LinkProbitThis class represents the Probit link function, which is the inverse of cumulative distribution function of the standard Normal distribution N(0, 1).Classcom.numericalmethod.suanshu.stats.regression.linear.glm.distribution.linkSuanShujavadoc
LinkSqrtThis class represents the square-root link function:See Also:GeneralizedLinearModelClasscom.numericalmethod.suanshu.stats.regression.linear.glm.distribution.linkSuanShujavadoc
LjungBoxThe Ljung-Box test (named for Greta M.Classcom.numericalmethod.suanshu.stats.test.timeseries.portmanteauSuanShujavadoc
LMBetaBeta coefficients are the outcomes of fitting a linear regression model.Classcom.numericalmethod.suanshu.stats.regression.linearSuanShujavadoc
LMDiagnosticsThis class collects some diagnostics measures for the goodness of fit based on the residulas for a linear regression model.Classcom.numericalmethod.suanshu.stats.regression.linear.residualanalysisSuanShujavadoc
LMInformationCriteriaThe information criteria measure the goodness of fit of an estimated statistical model.Classcom.numericalmethod.suanshu.stats.regression.linear.residualanalysisSuanShujavadoc
LMProblemThis is a linear regression or a linear model (LM) problem.Classcom.numericalmethod.suanshu.stats.regression.linearSuanShujavadoc
LMResidualsThis is the residual analysis of the results of a linear regression model.Classcom.numericalmethod.suanshu.stats.regression.linear.residualanalysisSuanShujavadoc
LocalConfigJava class for localConfig complex type.Classcom.numericalmethod.suanshu.grid.config.xml.schemaSuanShujavadoc
LocalConfigurationDefines the configuration for a local execution.Interfacecom.numericalmethod.suanshu.grid.config.localSuanShujavadoc
LocalGridExecutorInterface for classes that execute all their tasks locally (in the same JVM).Interfacecom.numericalmethod.suanshu.grid.executor.localSuanShujavadoc
LocalGridExecutorFactoryCreates local instances of GridExecutors from a configuration object.Classcom.numericalmethod.suanshu.grid.executor.localSuanShujavadoc
LocalParallelGridExecutorGrid executor that executes everything locally.Classcom.numericalmethod.suanshu.grid.executor.localSuanShujavadoc
LocalSearchCellFactoryA LocalSearchCellFactory produces LocalSearchCellFactory.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.localSuanShujavadoc
LogBetaThis class represents the log of Beta function log(B(x, y)).Classcom.numericalmethod.suanshu.analysis.function.special.betaSuanShujavadoc
LogGammaThe log-Gamma function, (log (Gamma(z))), for positive real numbers, is the log of the Gamma function.Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc

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LogisticBetaClasscom.numericalmethod.suanshu.stats.regression.linear.logisticSuanShujavadoc
LogisticProblemA logistic regression problem is a variation of the OLS regression problem.Classcom.numericalmethod.suanshu.stats.regression.linear.logisticSuanShujavadoc
LogisticRegressionA logistic regression (sometimes called the logistic model or logit model) is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve.Classcom.numericalmethod.suanshu.stats.regression.linear.logisticSuanShujavadoc
LogisticResidualsResidual analysis of the results of a logistic regression.Classcom.numericalmethod.suanshu.stats.regression.linear.logisticSuanShujavadoc
LogNormalDistributionA log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
LogNormalMixtureDistributionThe HMM states use the Log-Normal distribution to model the observations.Classcom.numericalmethod.suanshu.stats.hmm.mixture.distributionSuanShujavadoc
LogNormalRNGThis random number generator samples from the log-normal distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc
LoopBodyThe implementation of this interface contains the code inside a for-loopThis method contains the code inside the for-loop, as in a nativeInterfacecom.numericalmethod.suanshu.misc.parallelSuanShujavadoc
LowerBoundConstraintsClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShujavadoc
LowerTriangularMatrixA lower triangular matrix has 0 entries where column index > row index.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangleSuanShujavadoc
LPBoundedMinimizerThis is the solution to a bounded linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShujavadoc
LPCanonicalProblem1Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShujavadoc
LPCanonicalProblem2Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShujavadoc
LPCanonicalSolverThis is an LP solver that solves a canonical LP problem in the following form.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solverSuanShujavadoc
LPDimensionNotMatchedThis is the exception thrown when the dimensions of the objective function and constraints of a linear programming problem are inconsistent.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShujavadoc
LPEmptyCostVectorThis is the exception thrown when there is no objective function in a linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShujavadoc
LPExceptionThis is the exception thrown when there is any problem when solving a linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShujavadoc
LPInfeasibleThis is the exception thrown when the LP problem is infeasible, i.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShujavadoc
LPMinimizerAn LP minimizer minimizes the objective of an LP problem, satisfying all the constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lpSuanShujavadoc
LPNoConstraintThis is the exception thrown when there is no linear constraint found for the LP problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShujavadoc
LPProblemA linear programming (LP) problem minimizes a linear objective function subject to a collection of linear constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShujavadoc
LPProblemImpl1This is an implementation of a linear programming problem, LPProblem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShujavadoc
LPRuntimeExceptionThis is the exception thrown when there is any problem when constructing a linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShujavadoc
LPSimplexMinimizerA simplex LP minimizer can be read off from the solution simplex table.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShujavadoc
LPSimplexSolutionThe solution to a linear programming problem using a simplex method contains an LPSimplexMinimizer.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShujavadoc

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LPSimplexSolverA simplex solver works toward an LP solution by sequentially applying Jordan exchange to a simplex table.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solverSuanShujavadoc
LPSolutionA solution to an LP problem contains all information about solving an LP problem such as whether the problem has a solution (bounded), how many minimizers it has, and the minimum.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lpSuanShujavadoc
LPSolverAn LP solver solves a Linear Programming (LP) problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lpSuanShujavadoc
LPStandardProblemClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShujavadoc
LPTwoPhaseSolverThis implementation solves a linear programming problem, LPProblem, using a two-step approach.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solverSuanShujavadoc
LPUnboundedThis is the exception thrown when the LP problem is unbounded.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShujavadoc
LPUnboundedMinimizerThis is the solution to an unbounded linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShujavadoc
LPUnboundedMinimizerScheme2This is the solution to an unbounded linear programming problem found in scheme 2.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShujavadoc
LSProblemThis is the problem of solving a system of linear equations.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc
LULU decomposition decomposes an n x n matrix A so that P * A = L * U.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangleSuanShujavadoc
LUDecompositionLU decomposition decomposes an n x n matrix A so that P * A = L * U.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangleSuanShujavadoc
LUSolverUse LU decomposition to solve Ax = b where A is square and The dimensions of A and b must match.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc
MADecompositionThis class decomposes a time series into the trend, seasonal and stationary random components using the Moving Average Estimation method with symmetric window.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocessSuanShujavadoc
MAModelThis class represents a univariate MA model.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armaSuanShujavadoc
MARMAModelSimulation of max autoregressive moving average processes, i.Classcom.numericalmethod.suanshu.stats.evt.timeseriesSuanShujavadoc
MARMASimGenerate random numbers based on a given MARMA model.Classcom.numericalmethod.suanshu.stats.evt.timeseriesSuanShujavadoc
MARModelThis is equivalent to MARMA(p, 0).Classcom.numericalmethod.suanshu.stats.evt.timeseriesSuanShujavadoc
MarsagliaBray1964The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method for generating a pair of independent standard normal random variables.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShujavadoc
MarsagliaTsang2000Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled normal variate.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShujavadoc
MasterDelegates Work to one or more slaves and forwards Results to a predefined Also forwards work and results to the failure detector, which will keep track of their relativeClasscom.numericalmethod.suanshu.grid.executor.remote.akka.actorSuanShujavadoc
MATMAT is the inverse operator of SVEC.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
MathTableA mathematical table consists of numbers showing the results of calculation with varying arguments.Classcom.numericalmethod.suanshu.misc.datastructureSuanShujavadoc
MatrixThis interface defines a Matrix as a Ring, a Table, and a few more methods not already defined in its mathematical definition.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShujavadoc
MatrixAccessThis interface defines the methods for accessing entries in a matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShujavadoc
MatrixAccessExceptionThis is the runtime exception thrown when trying to access an invalid entry in a matrix, e.Classcom.numericalmethod.suanshu.algebra.linear.matrixSuanShujavadoc

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MatrixCoordinateThe location of a matrix entry is specified by a 2D coordinates (i, j), where i and j are the row-index and column-index of the entry respectively.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShujavadoc
MatrixFactoryThese are the utility functions to create a new matrix/vector from existing ones.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
MatrixMathOperationThis interface defines some standard operations for generic matrices.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperationSuanShujavadoc
MatrixMeasureClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
MatrixMismatchExceptionThis is the runtime exception thrown when an operation acts on matrices that have incompatible dimensions.Classcom.numericalmethod.suanshu.algebra.linear.matrixSuanShujavadoc
MatrixPropertyUtilsThese are the boolean operators that take matrices or vectors and check if they satisfy aChecks if all matrices are SparseMatrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShujavadoc
MatrixRingInterfacecom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShujavadoc
MatrixRootByDiagonalizationThe square root of a matrix extends the notion of square root from numbers to matrices.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
MatrixSingularityExceptionThis is the runtime exception thrown when an operation acts on a singular matrix, e.Classcom.numericalmethod.suanshu.algebra.linear.matrixSuanShujavadoc
MatrixTableA matrix is represented by a rectangular table structure with accessors.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShujavadoc
MatrixUtilsThese are the utility functions to apply to matrices.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
MatthewsDaviesMatthews and Davies propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefiniteSuanShujavadoc
MaxThe maximum of a sample is the biggest value in the sample.Classcom.numericalmethod.suanshu.stats.descriptive.rankSuanShujavadoc
MaximaDistributionThe distribution of (M), where (M=max(x_1,x_2,.Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShujavadoc
MaximizationSolutionThis is the solution to a maximization problem.Interfacecom.numericalmethod.suanshu.optimizationSuanShujavadoc
MaximumLikelihoodFittingThis interface defines model fitting by maximum likelihood algorithm.Interfacecom.numericalmethod.suanshu.stats.evt.evd.univariate.fittingSuanShujavadoc
MaxmizerThis interface represents an optimization algorithm that maximizers a real valued objective function, one or multi dimension.Interfacecom.numericalmethod.suanshu.optimizationSuanShujavadoc
McCormickMinimizerThis is the McCormick method.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShujavadoc
MCUtilsThese are the utility functions to examine a Markov chain.Classcom.numericalmethod.suanshu.stats.markovchainSuanShujavadoc
MeanThe mean of a sample is the sum of all numbers in the sample, divided by the sample size.Classcom.numericalmethod.suanshu.stats.descriptive.momentSuanShujavadoc
MeanEstimatorDefines how to estimate the mean price.Interfacecom.numericalmethod.suanshu.model.volarbSuanShujavadoc
MeanEstimatorInterfacecom.numericalmethod.suanshu.stats.randomSuanShujavadoc
MeanEstimatorMaxLevelShiftClasscom.numericalmethod.suanshu.model.volarbSuanShujavadoc
MersenneExponentenum MersenneExponentThe value of a Mersenne Exponent p is a parameter for creating a Mersenne-Twister randomClasscom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreationSuanShujavadoc
MersenneTwisterMersenne Twister is one of the best pseudo random number generators available.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwisterSuanShujavadoc

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MersenneTwisterParamImmutable parameters for creating a MersenneTwister RNG.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwisterSuanShujavadoc
MersenneTwisterParamSearcherSearches for Mersenne-Twister parameters.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreationSuanShujavadoc
MetropolisThis basic Metropolis implementation assumes using symmetric proposal function.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShujavadoc
MetropolisAcceptanceProbabilityFunctionUses the classic Metropolis rule, f_{t+1}/f_t.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunctionSuanShujavadoc
MetropolisHastingsA generalization of the Metropolis algorithm, which allows asymmetric proposal Metropolis-HastingsLiu, Jun S.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShujavadoc
MetropolisUtilsUtility functions for Metropolis algorithms.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShujavadoc
MidpointThe midpoint rule computes an approximation to a definite integral, made by finding the area of a collection of rectangles whose heights are determined by the values of the function.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotesSuanShujavadoc
MilsteinSDEMilstein scheme is a first-order approximation to a continuous-time SDE.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discreteSuanShujavadoc
MinThe minimum of a sample is the smallest value in the sample.Classcom.numericalmethod.suanshu.stats.descriptive.rankSuanShujavadoc
MinimaDistributionThe distribution of (M), where (M=min(x_1,x_2,.Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShujavadoc
MinimalResidualSolverThe Minimal Residual method (MINRES) is useful for solving a symmetric n-by-n linear system (possibly indefinite or singular).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc
MinimizationSolutionThis is the solution to a minimization problem.Interfacecom.numericalmethod.suanshu.optimizationSuanShujavadoc
MinimizerThis interface represents an optimization algorithm that minimizes a real valued objective function, one or multi dimension.Interfacecom.numericalmethod.suanshu.optimizationSuanShujavadoc
MinimumWeightsThis constraint puts lower bounds on weights.Classcom.numericalmethod.suanshu.model.corvalan2005.constraintSuanShujavadoc
MinMaxMinimizerA minmax minimizer minimizes a minmax problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.minmaxSuanShujavadoc
MixedRuleThe mixed rule is good for functions that fall off rapidly at infinity, e.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
MixtureDistributionThis is the conditional distribution of the observations in each state (possibly differently parameterized) of a mixture hidden Markov model.Interfacecom.numericalmethod.suanshu.stats.hmm.mixture.distributionSuanShujavadoc
MixtureHMMThis is the mixture hidden Markov model (HMM).Classcom.numericalmethod.suanshu.stats.hmm.mixtureSuanShujavadoc
MixtureHMMEMThe EM algorithm is used to find the unknown parameters of a hidden Markov model (HMM) by making use of the forward-backward algorithm.Classcom.numericalmethod.suanshu.stats.hmm.mixtureSuanShujavadoc
MMAModelThis is equivalent to MARMA(0, q).Classcom.numericalmethod.suanshu.stats.evt.timeseriesSuanShujavadoc
ModelResamplerFactoryClasscom.numericalmethod.suanshu.model.lai2010.ceta.npeb.resamplerSuanShujavadoc
MomentsCompute the central moment of a data set incrementally.Classcom.numericalmethod.suanshu.stats.descriptive.momentSuanShujavadoc
MomentsEstimatorLedoitWolfClasscom.numericalmethod.suanshu.model.returns.momentsSuanShujavadoc
MonoidInterfacecom.numericalmethod.suanshu.algebra.structureSuanShujavadoc
MovingAverageThis applies a linear filter to a univariate time series using the moving average estimation.Classcom.numericalmethod.suanshu.dsp.univariate.operation.system.doublesSuanShujavadoc

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MovingAverageByExtensionThis implements a moving average filter with these properties: 1) both past and future observations are used in smoothing;Classcom.numericalmethod.suanshu.dsp.univariate.operation.system.doublesSuanShujavadoc
MR3Computes eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T using "Algorithm of Multiple Relatively Robust Representations" (MRRR).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
MRGA Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form: xi = (a1 * xi-1 + a2 * xi-2 + .Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShujavadoc
MRModelA Mean Reversion Model computes the target position given the current price.Interfacecom.numericalmethod.suanshu.model.volarbSuanShujavadoc
MRModelRangedClasscom.numericalmethod.suanshu.model.volarbSuanShujavadoc
MultiCubicSpline algorithm works by recursively calling lower order cubic spline interpolation.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariateSuanShujavadoc
MultiDimensionalArrayA generic multi-dimensional array, with an arbitrary number of dimensions.Classcom.numericalmethod.suanshu.misc.datastructureSuanShujavadoc
MultiDimensionalCollectionA generic collection with an arbitrary number of dimensions.Interfacecom.numericalmethod.suanshu.misc.datastructureSuanShujavadoc
MultiDimensionalGridAn arbitrary dimensional grid.Classcom.numericalmethod.suanshu.misc.datastructureSuanShujavadoc
MultiLinearInterpolation by recursively calling lower order linear interpolation.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariateSuanShujavadoc
MultinomialBetaFunctionA multinomial Beta function is defined as: frac{prod_{i=1}^K Gamma(alpha_i)}{Gammaleft(sum_{i=1}^KClasscom.numericalmethod.suanshu.analysis.function.special.betaSuanShujavadoc
MultinomialDistributionClasscom.numericalmethod.suanshu.stats.distribution.multivariateSuanShujavadoc
MultinomialRVGA multinomial distribution puts N objects into K bins according to the bins' probabilities.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShujavadoc
MultipleExecutionExceptionThis exception is thrown when any of the parallel tasks throws an exception during execution.Classcom.numericalmethod.suanshu.misc.parallelSuanShujavadoc
MultiplicativeModelThe multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocessSuanShujavadoc
MultiplierPenaltyA multiplier penalty function allows different weights to be assigned to the constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShujavadoc
MultipointHybridMCMCA multi-point Hybrid Monte Carlo is an extension of HybridMCMC, where during the proposal generation instead of considering only the last configuration after the dynamicsClasscom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShujavadoc
MultivariateArrayGrid MultiDimensionalCollection instance.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariateSuanShujavadoc
MultivariateAutoCorrelationFunctionThis is the auto-correlation function of a multi-dimensional time series {Xt}.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariateSuanShujavadoc
MultivariateAutoCovarianceFunctionThis is the auto-covariance function of a multi-dimensional time series {Xt}, K(i, j) = E((X_i - mu_i) imes (X_j - mu_j)')Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariateSuanShujavadoc
MultivariateBrownianRRGThis is the Random Walk construction of a multivariate Brownian motion.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.randomSuanShujavadoc
MultivariateBrownianSDEA multivariate Brownian motion is a stochastic process with the following properties.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discreteSuanShujavadoc
MultivariateDiscreteSDEThis interface represents the discrete approximation of a multivariate SDE.Interfacecom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discreteSuanShujavadoc
MultivariateDLMThis is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.Classcom.numericalmethod.suanshu.stats.dlm.multivariateSuanShujavadoc
MultivariateDLMSeriesThis is a simulator for a multivariate controlled dynamic linear model process.Classcom.numericalmethod.suanshu.stats.dlm.multivariateSuanShujavadoc

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MultivariateDLMSimThis is a simulator for a multivariate controlled dynamic linear model process.Classcom.numericalmethod.suanshu.stats.dlm.multivariateSuanShujavadoc
MultivariateEulerSDEThe Euler scheme is the first order approximation of an SDE.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discreteSuanShujavadoc
MultivariateFiniteDifferenceA partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant.Classcom.numericalmethod.suanshu.analysis.differentiation.multivariateSuanShujavadoc
MultivariateForecastOneStepThe innovation algorithm is an efficient way to obtain a one step least square linear predictor for a multivariate linear time seriesClasscom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocessSuanShujavadoc
MultivariateFtThis represents the concept 'Filtration', the information available at time t.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sdeSuanShujavadoc
MultivariateFtWtThis is a filtration implementation that includes the path-dependent information,See Also:MultivariateFtClasscom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sdeSuanShujavadoc
MultivariateGenericTimeTimeSeriesThis is a multivariate time series indexed by some notion of time.Classcom.numericalmethod.suanshu.stats.timeseries.datastructure.multivariateSuanShujavadoc
MultivariateGridA multivariate rectilinear (not necessarily uniform) grid of double values.Interfacecom.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariateSuanShujavadoc
MultivariateGridInterpolationInterpolation on a rectilinear multi-dimensional grid.Interfacecom.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariateSuanShujavadoc
MultivariateInnovationAlgorithmClasscom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocessSuanShujavadoc
MultivariateIntTimeTimeSeriesThis is a multivariate time series indexed by integers.Interfacecom.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttimeSuanShujavadoc
MultivariateLinearKalmanFilterThe Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time,Classcom.numericalmethod.suanshu.stats.dlm.multivariateSuanShujavadoc
MultivariateMinimizerThis is a minimizer that minimizes a multivariate function or a Vector function.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrainedSuanShujavadoc
MultivariateNormalDistributionThe multivariate Normal distribution or multivariate Gaussian distribution, is a generalization of the one-dimensional (univariate) Normal distribution to higher dimensions.Classcom.numericalmethod.suanshu.stats.distribution.multivariateSuanShujavadoc
MultivariateObservationEquationThis is the observation equation in a controlled dynamic linear model.Classcom.numericalmethod.suanshu.stats.dlm.multivariateSuanShujavadoc
MultivariateProbabilityDistributionA multivariate or joint probability distribution for X, Y, .Interfacecom.numericalmethod.suanshu.stats.distribution.multivariateSuanShujavadoc
MultivariateRandomProcessThis interface represents a multivariate random process a.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.randomSuanShujavadoc
MultivariateRandomRealizationGeneratorThis interface defines a generator to construct random realizations from a multivariate stochastic process.Interfacecom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.randomSuanShujavadoc
MultivariateRandomRealizationOfRandomProcessThis class generates random realizations from a multivariate random/stochastic process.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.randomSuanShujavadoc
MultivariateRandomWalkThis is the Random Walk construction of a multivariate stochastic process per SDE specification.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.randomSuanShujavadoc
MultivariateRealizationA multivariate realization is a multivariate time series indexed by real numbers, e.Interfacecom.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtimeSuanShujavadoc
MultivariateRegularGridA regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes (e.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariateSuanShujavadoc
MultivariateResamplerThis is the interface of a multivariate re-sampler method.Interfacecom.numericalmethod.suanshu.stats.random.sampler.resampler.multivariateSuanShujavadoc
MultivariateSDEThis class represents a multi-dimensional, continuous-time Stochastic Differential Equation (SDE) of this form: dX_t = mu(t,X_t,Z_t,.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sdeSuanShujavadoc
MultivariateSimpleTimeSeriesThis simple multivariate time series has its vectored values indexed by integers.Classcom.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttimeSuanShujavadoc

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MultivariateStateEquationThis is the state equation in a controlled dynamic linear model.Classcom.numericalmethod.suanshu.stats.dlm.multivariateSuanShujavadoc
MultivariateTDistributionThe multivariate T distribution or multivariate Student distribution, is a generalization of the one-dimensional (univariate) Student's t-distribution to higher dimensions.Classcom.numericalmethod.suanshu.stats.distribution.multivariateSuanShujavadoc
MultivariateTimeSeriesA multivariate time series is a sequence of vectors indexed by some notion of time.Interfacecom.numericalmethod.suanshu.stats.timeseries.datastructure.multivariateSuanShujavadoc
MutexProvides mutual exclusive execution of a Runnable.Classcom.numericalmethod.suanshu.misc.parallelSuanShujavadoc
MVOptimizerSolves for the optimal weight using Mean-Variance optimization.Interfacecom.numericalmethod.suanshu.model.lai2010.optimizerSuanShujavadoc
MVOptimizerMinWeightsSolves for weights by (active set) quadratic programming.Classcom.numericalmethod.suanshu.model.lai2010.optimizerSuanShujavadoc
MVOptimizerNoConstraintClasscom.numericalmethod.suanshu.model.lai2010.optimizerSuanShujavadoc
MWC8222Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniformSuanShujavadoc
NaiveRuleThis pivoting rule chooses the column with the most negative reduced cost.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivotingSuanShujavadoc
NelderMeadMinimizerThe Nelder-Mead method is a nonlinear optimization technique, which is well-defined for twice differentiable and unimodal problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2SuanShujavadoc
NevilleTableNeville's algorithm is a polynomial interpolation algorithm.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolationSuanShujavadoc
NewtonCotesThe Newton-Cotes formulae, also called the Newton-Cotes quadrature rules or simply Newton-Cotes rules, are a group of formulae for numerical integration (also called quadrature) based on evaluating the integrand at equally-spaced points.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotesSuanShujavadoc
NewtonPolynomialNewton polynomial is the interpolation polynomial for a given set of data points in the Newton form.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.univariateSuanShujavadoc
NewtonRaphsonMinimizerThe Newton-Raphson method is a second order steepest descent method that is based on the quadratic approximation of the Taylor series.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShujavadoc
NewtonRootThe Newton-Raphson method is as follows: one starts with an initial guess which is reasonably close to the true root, then the function is approximated by its tangent line (which can beClasscom.numericalmethod.suanshu.analysis.root.univariateSuanShujavadoc
NewtonSystemRootThis class solves the root for a non-linear system of equations.Classcom.numericalmethod.suanshu.analysis.root.multivariateSuanShujavadoc
NoChangeOfVariableThis is a dummy substitution rule that does not change any variable.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
NoConstraintsWeights are unconstrained and constraints() returns null.Classcom.numericalmethod.suanshu.model.corvalan2005.constraintSuanShujavadoc
NonlinearFitFit log-ACER function by sequential quadratic programming (SQP) minimization (of weighted RSS), using LinearFit's solution as the initial guess.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShujavadoc
NonNegativityConstraintOptimProblemThis is a constrained optimization problem for a function which has all non-negative variables.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.problemSuanShujavadoc
NonNegativityConstraintsThese constraints ensures that for all variables are non-negative.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShujavadoc
NoOpActorClasscom.numericalmethod.suanshu.grid.executor.remote.akka.actorSuanShujavadoc
NormalDistributionThe Normal distribution has its density a Gaussian function.Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
NormalMixtureDistributionThe HMM states use the Normal distribution to model the observations.Classcom.numericalmethod.suanshu.stats.hmm.mixture.distributionSuanShujavadoc
NormalOfExpFamily1Normal distribution, univariate, unknown mean, known variance.Classcom.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamilySuanShujavadoc

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NormalOfExpFamily2Normal distribution, univariate, unknown mean, unknown variance.Classcom.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamilySuanShujavadoc
NormalRNGThis is a random number generator that generates random deviates according to the NormalSee Also:Wikipedia: NormalClasscom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShujavadoc
NormalRVGA multivariate Normal random vector is said to be p-variate normally distributed if every linear combination of its p components has a univariate normal distribution.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShujavadoc
NoRootFoundExceptionThis is the Exception thrown when it fails to find a root.Classcom.numericalmethod.suanshu.analysis.root.univariateSuanShujavadoc
NoShortSellingWeights cannot be negative.Classcom.numericalmethod.suanshu.model.corvalan2005.constraintSuanShujavadoc
NPEBMomentsEstimatorClasscom.numericalmethod.suanshu.model.lai2010.ceta.npebSuanShujavadoc
NullMonitorThis IterationMonitor does nothing when a new iterate is added.Classcom.numericalmethod.suanshu.misc.algorithm.iterative.monitorSuanShujavadoc
NumberUtilsThese are the utility functions to manipulate Numbers.Classcom.numericalmethod.suanshu.numberSuanShujavadoc
ObjectFactoryThis object contains factory methods for each Java content interface and Java element interface Classcom.numericalmethod.suanshu.grid.config.xml.schemaSuanShujavadoc
ObservationEquationThis is the observation equation in a controlled dynamic linear model.Classcom.numericalmethod.suanshu.stats.dlm.univariateSuanShujavadoc
ODEAn ordinary differential equation (ODE) is an equation in which there is only one independent variable and one or more derivatives of a dependent variable with respect to the independentClasscom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problemSuanShujavadoc
ODE1stOrderA first order ordinary differential equation (ODE) initial value problem (IVP) takes the where y0 is known, and the solution of the problem is y(x) for theClasscom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problemSuanShujavadoc
ODE1stOrderWith2ndDerivativeSome ODE solvers require the second derivative for more accurate Taylor series approximation.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problemSuanShujavadoc
ODEIntegratorThis defines the interface for the numerical integration of a first order ODE, for a sequence of pre-defined steps.Interfacecom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solverSuanShujavadoc
ODESolutionSolution to an ODE problem.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solverSuanShujavadoc
ODESolverSolver for first order ODE problems.Interfacecom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solverSuanShujavadoc
OLSBetaClasscom.numericalmethod.suanshu.stats.regression.linear.olsSuanShujavadoc
OLSRegression(Weighted) Ordinary Least Squares (OLS) is a method for fitting a linear regression model.Classcom.numericalmethod.suanshu.stats.regression.linear.olsSuanShujavadoc
OLSResidualsThis is the residual analysis of the results of an ordinary linear regression model.Classcom.numericalmethod.suanshu.stats.regression.linear.olsSuanShujavadoc
OLSSolverThis class solves an over-determined system of linear equations in the ordinary least square sense.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc
OLSSolverByQRThis class solves an over-determined system of linear equations in the ordinary least square sense.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc
OLSSolverBySVDThis class solves an over-determined system of linear equations in the ordinary least square sense.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc
OneDimensionTimeSeriesThis class constructs a univariate realization from a multivariate realization by taking one of its dimension (coordinate).Classcom.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtimeSuanShujavadoc
OneWayANOVAThe One-Way ANOVA test tests for the equality of the means of several groups.Classcom.numericalmethod.suanshu.stats.test.meanSuanShujavadoc
OnlineInterpolatorAn online interpolator allows dynamically adding more points for interpolation.Interfacecom.numericalmethod.suanshu.analysis.curvefit.interpolationSuanShujavadoc

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OptimizerOptimization, or mathematical programming, refers to choosing the best element from some set of available alternatives.Interfacecom.numericalmethod.suanshu.optimizationSuanShujavadoc
OptimProblemThis is an optimization problem that minimizes a real valued objective function, one or multi dimension.Interfacecom.numericalmethod.suanshu.optimization.problemSuanShujavadoc
OrderedAccumulatorCollects all results in a list, ensuring that the order according to the indices is preserved in Results for indices may arrive multiple times and may be null due to a failure.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.actorSuanShujavadoc
OrderedPairsCartesian products and binary relations (and hence the ubiquitous functions) are defined in termsSee Also:Wikipedia: Ordered pairInterfacecom.numericalmethod.suanshu.analysis.function.tupleSuanShujavadoc
OrderStatisticsDistributionThe asymptotic nondegenerate distributions of the r-th smallest (largest) order statistic.Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShujavadoc
OrnsteinUhlenbeckProcessThis class represents a univariate Ornstein-Uhlenbeck (OU) process.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ouSuanShujavadoc
OrStopConditionsCombines an arbitrary number of stop conditions, terminating when the first condition is met.Classcom.numericalmethod.suanshu.misc.algorithm.stopconditionSuanShujavadoc
OrthogonalPolynomialFamilyThis factory class produces a family of orthogonal polynomials.Interfacecom.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.ruleSuanShujavadoc
OUFittingThis interface defines an estimation procedure to fit a univariate Ornstein-Uhlenbeck process.Interfacecom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ouSuanShujavadoc
OUFittingMLEThis class fits a univariate Ornstein-Uhlenbeck process by using MLE.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ouSuanShujavadoc
OUFittingOLSThis class fits a univariate Ornstein-Uhlenbeck process by using least squares regression.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ouSuanShujavadoc
OUProcessGet the overall mean.Interfacecom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ouSuanShujavadoc
OUSimThis class simulates a discrete path of a univariate Ornstein-Uhlenbeck (OU) process.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ouSuanShujavadoc
OuterProductThe outer product of two vectors a and b, is a row vector multiplied on the left by a column vector.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
PackageClasscom.numericalmethod.suanshu.misc.licenseSuanShujavadoc
PairAn ordered pair (x,y) is a pair of mathematical objects.Classcom.numericalmethod.suanshu.analysis.function.tupleSuanShujavadoc
PairComparatorByAbscissaFirstClasscom.numericalmethod.suanshu.analysis.function.tupleSuanShujavadoc
PairComparatorByAbscissaOnlyClasscom.numericalmethod.suanshu.analysis.function.tupleSuanShujavadoc
PanelDataA panel data refers to multi-dimensional data frequently involving measurements over time.Classcom.numericalmethod.suanshu.stats.regression.linear.panelSuanShujavadoc
PanelRegressionPanel (data) analysis is a statistical method, widely used in social science, epidemiology, and econometrics, which deals with two-dimensional (cross sectional/times series) panel data.Interfacecom.numericalmethod.suanshu.stats.regression.linear.panelSuanShujavadoc
ParallelDoubleArrayOperationThis is a multi-threaded implementation of the array math operations.Classcom.numericalmethod.suanshu.number.doublearraySuanShujavadoc
ParallelExecutorThis class provides a framework for executing an algorithm in parallel.Classcom.numericalmethod.suanshu.misc.parallelSuanShujavadoc
PartialDerivativesByCenteredDifferencingThis implementation computes the partial derivatives by centered differencing.Classcom.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariateSuanShujavadoc
PartialFunctionClasscom.numericalmethod.suanshu.analysis.function.tupleSuanShujavadoc
PattonPolitisWhite2009This class implements the stationary and circular block bootstrapping method with optimized blockSee Also:Politis, N.Classcom.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.blockSuanShujavadoc

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PCAPrincipal Component Analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set ofInterfacecom.numericalmethod.suanshu.stats.pcaSuanShujavadoc
PCAbyEigenThis class performs Principal Component Analysis (PCA) on a data matrix, using eigen decomposition on the correlation or covariance matrix.Classcom.numericalmethod.suanshu.stats.pcaSuanShujavadoc
PCAbySVDThis class performs Principal Component Analysis (PCA) on a data matrix, using the preferred Singular Value Decomposition (SVD) method.Classcom.numericalmethod.suanshu.stats.pcaSuanShujavadoc
PDEA partial differential equation (PDE) is a differential equation that contains unknown multivariable functions and their partial derivatives.Interfacecom.numericalmethod.suanshu.analysis.differentialequation.pdeSuanShujavadoc
PDESolutionGrid2DA solution to a bivariate PDE, which is applicable to methods which produce the solution as a two-dimensional grid.Interfacecom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifferenceSuanShujavadoc
PDESolutionTimeSpaceGrid1DA solution to an one-dimensional PDE, which is applicable to methods which produce the solutionInterfacecom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifferenceSuanShujavadoc
PDESolutionTimeSpaceGrid2DA solution to a two-dimensional PDE, which is applicable to methods which produce the solution as a three-dimensional grid of time and space.Interfacecom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifferenceSuanShujavadoc
PDESolverInterfacecom.numericalmethod.suanshu.analysis.differentialequation.pdeSuanShujavadoc
PDETimeSpaceGrid1DThis grid numerically solves a 1D PDE, e.Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifferenceSuanShujavadoc
PeaksOverThresholdPeaks Over Threshold (POT) method estimates the parameters for generalized Pareto distribution (GPD) using maximum likelihood on the observations that are over a given threshold.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.potSuanShujavadoc
PeaksOverThresholdOnClustersSimilar to POT, but only use the peak observations in clusters for the parametric estimation.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.potSuanShujavadoc
PearsonMinimizerThis is the Pearson method.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShujavadoc
PenaltyFunctionA function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShujavadoc
PenaltyMethodMinimizerThe penalty method is an algorithm for solving a constrained minimization problem with general It replaces a constrained optimization problem by a series of unconstrained problemsClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShujavadoc
PermutationMatrixA permutation matrix is a square matrix that has exactly one entry '1' in each row and each column and 0's elsewhere.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtypeSuanShujavadoc
PerturbationAroundPointThe initial population is generated by adding a variance around a given initial.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgenerationSuanShujavadoc
PhysicalConstantsA collection of fundamental physical constants.Classcom.numericalmethod.suanshu.miscSuanShujavadoc
PointRepresent a n-dimensional point.Classcom.numericalmethod.suanshu.geometrySuanShujavadoc
PoissonDistributionThe Poisson distribution (or Poisson law of small numbers) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of timeClasscom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
PoissonEquation2DPoisson's equation is an elliptic PDE that takes the following general form.Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2SuanShujavadoc
PoissonMixtureDistributionThe HMM states use the Poisson distribution to model the observations.Classcom.numericalmethod.suanshu.stats.hmm.mixture.distributionSuanShujavadoc
PolygonalChainA polygonal chain, polygonal curve, polygonal path, or piecewise linear curve, is a connected series of line segments.Interfacecom.numericalmethod.suanshu.geometry.polylineSuanShujavadoc
PolygonalChainByArrayAn immutable PolygonalChain that is backed by an ArrayList.Classcom.numericalmethod.suanshu.geometry.polylineSuanShujavadoc
PolynomialA polynomial is a UnivariateRealFunction that represents a finite length expression constructed from variables and constants, using the operations of addition, subtraction, multiplication, and constant non-negative whole number exponents.Classcom.numericalmethod.suanshu.analysis.function.polynomialSuanShujavadoc
PolyRootThis is a solver for finding the roots of a polynomial equation.Classcom.numericalmethod.suanshu.analysis.function.polynomial.rootSuanShujavadoc

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PolyRootSolverA root (or a zero) of a polynomial p is a member x in the domain of p such that p(x) vanishes.Interfacecom.numericalmethod.suanshu.analysis.function.polynomial.rootSuanShujavadoc
PositiveDefiniteMatrixByPositiveDiagonalThis class "converts" a matrix into a symmetric, positive definite matrix, if it is not already so, by forcing the diagonal entries in the eigen decomposition to a small non-negative number,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefiniteSuanShujavadoc
PositiveSemiDefiniteMatrixNonNegativeDiagonalThis class "converts" a matrix into a symmetric, positive semi-definite matrix, if it is not already so, by forcing the negative diagonal entries in the eigen decomposition to 0.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefiniteSuanShujavadoc
PowThis is a square matrix A to the power of an integer n, An.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
PowellMinimizerPowell's algorithm, starting from an initial point, performs a series of line searches in one iteration.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirectionSuanShujavadoc
PowerLawSingularityThis transformation is good for an integral which diverges at one of the end points.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
PreconditionerPreconditioning reduces the condition number of the coefficient matrix of a linear system to accelerate the convergenceInterfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShujavadoc
PreconditionerFactoryThis constructs a new instance of Preconditioner for a coefficient matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShujavadoc
PrimalDualInteriorPointMinimizerSolves a Dual Second Order Conic Programming problem using the Primal Dual Interior Point 2014/1/9: This solver is tested up to 6000 variables and 26000 constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpointSuanShujavadoc
PrimalDualPathFollowingMinimizerThe Primal-Dual Path-Following algorithm is an interior point method that solves Semi-Definite Programming problems.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShujavadoc
PrimalDualSolutionThe vector set {x, s, y} is a solution to both the primal and dual SOCP problems.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpointSuanShujavadoc
ProbabilityDistributionA univariate probability distribution completely characterizes a random variable by stipulating the probability of each value of a random variable (when the variable is discrete), or theInterfacecom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
ProbabilityMassFunctionA probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.Interfacecom.numericalmethod.suanshu.stats.distribution.discreteSuanShujavadoc
ProbabilityMassQuantileAs probability mass function is discrete, there are gaps between values in the domain of its cdf, The quantile function is:Classcom.numericalmethod.suanshu.stats.distribution.discreteSuanShujavadoc
ProbabilityMassSamplerA random sampler that is constructed ad-hoc from a list of values and their probabilities.Classcom.numericalmethod.suanshu.stats.distribution.discreteSuanShujavadoc
ProductOfWeightsClasscom.numericalmethod.suanshu.model.corvalan2005.diversificationSuanShujavadoc
ProjectionProject a vector v on another vector w or a set of vectors (basis) {wi}.Classcom.numericalmethod.suanshu.algebra.linear.vector.doubles.operationSuanShujavadoc
ProposalFunctionA proposal function goes from the current state to the next state, where a state is a vector.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunctionSuanShujavadoc
PseudoInverseThe Moore-Penrose pseudo-inverse of an m x n matrix A is A+.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
PureILPProblemThis is a pure integer linear programming problem, in which all variables are integral.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problemSuanShujavadoc
QPDualActiveSetMinimizerThis implementation solves a Quadratic Programming problem using the dual active set algorithm.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activesetSuanShujavadoc
QPExceptionThis is the exception thrown when there is an error solving a quadratic programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qpSuanShujavadoc
QPInfeasibleThis is the exception thrown by a quadratic programming solver when the quadratic programming problem is infeasible, i.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qpSuanShujavadoc
QPPrimalActiveSetMinimizerThis implementation solves a Quadratic Programming problem using the Primal Active Set algorithm.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activesetSuanShujavadoc
QPProblemQuadratic Programming is the problem of optimizing (minimizing) a quadratic function of several variables subject to linear constraints on these variables.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problemSuanShujavadoc

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QPProblemOnlyEqualityConstraintsA quadratic programming problem with only equality constraints can be converted into a equivalent quadratic programming problem without constraints, hence a mere quadratic function.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problemSuanShujavadoc
QPSimpleMinimizerThese are the utility functions to solve simple quadratic programming problems that admit analytical solutions.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qpSuanShujavadoc
QPSolutionThis is a solution to a quadratic programming problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qpSuanShujavadoc
QRQR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qrSuanShujavadoc
QRAlgorithmThe QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a That is, Q'AQ = T where Q is orthogonal, and T is quasi-triangular.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShujavadoc
QRDecompositionQR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qrSuanShujavadoc
QuadraticFunctionA quadratic function takes this form: (f(x) = frac{1}{2} imes x'Hx + x'p + c).Classcom.numericalmethod.suanshu.analysis.function.rn2r1SuanShujavadoc
QuadraticMonomialA quadratic monomial has this form: x2 + ux + v.Classcom.numericalmethod.suanshu.analysis.function.polynomialSuanShujavadoc
QuadraticRootThis is a solver for finding the roots of a quadratic equation, (ax^2 + bx + c = 0).Classcom.numericalmethod.suanshu.analysis.function.polynomial.rootSuanShujavadoc
QuadraticSyntheticDivisionDivide a polynomial P(x) by a quadratic monomial (x2 + ux + v) to give the quotient Q(x) and the remainder (b * (x + u) + a).Classcom.numericalmethod.suanshu.analysis.function.polynomialSuanShujavadoc
QuantileQuantiles are points taken at regular intervals from the cumulative distribution function (CDF) of a random variable.Classcom.numericalmethod.suanshu.stats.descriptive.rankSuanShujavadoc
QuarticRootThis is a quartic equation solver that solves (ax^4 + bx^3 + cx^2 + dx + e = 0).Classcom.numericalmethod.suanshu.analysis.function.polynomial.rootSuanShujavadoc
QuarticRootFerrariThis is a quartic equation solver that solves (ax^4 + bx^3 + cx^2 + dx + e = 0) using the Ferrari method.Classcom.numericalmethod.suanshu.analysis.function.polynomial.rootSuanShujavadoc
QuarticRootFormulaThis is a quartic equation solver that solves (ax^4 + bx^3 + cx^2 + dx + e = 0) using a root-finding formula.Classcom.numericalmethod.suanshu.analysis.function.polynomial.rootSuanShujavadoc
QuasiBinomialThis is the quasi Binomial distribution in GLM.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.quasi.familySuanShujavadoc
QuasiDistributionThis interface represents the quasi-distribution used in GLM.Interfacecom.numericalmethod.suanshu.stats.regression.linear.glm.quasi.familySuanShujavadoc
QuasiFamilyThis interface represents the quasi-family used in GLM.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.quasi.familySuanShujavadoc
QuasiGammaThis is the quasi Gamma distribution in GLM.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.quasi.familySuanShujavadoc
QuasiGaussianThis is the quasi Gaussian distribution in GLM.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.quasi.familySuanShujavadoc
QuasiGLMBetaClasscom.numericalmethod.suanshu.stats.regression.linear.glm.quasiSuanShujavadoc
QuasiGLMNewtonRaphsonClasscom.numericalmethod.suanshu.stats.regression.linear.glm.quasiSuanShujavadoc
QuasiGLMProblemThis class represents a quasi generalized linear regression problem.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.quasiSuanShujavadoc
QuasiGLMResidualsResidual analysis of the results of a quasi Generalized Linear Model regression.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.quasiSuanShujavadoc
QuasiInverseGaussianThis is the quasi Inverse-Gaussian distribution in GLM.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.quasi.familySuanShujavadoc
QuasiMinimalResidualSolverThe Quasi-Minimal Residual method (QMR) is useful for solving a non-symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc

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QuasiNewtonMinimizerThe Quasi-Newton methods in optimization are for finding local maxima and minima of functions.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShujavadoc
QuasiPoissonThis is the quasi Poisson distribution in GLM.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.quasi.familySuanShujavadoc
R1ProjectionProjection creates a real-valued function RealScalarFunction from a vector-valued function RealVectorFunction by taking only one of its coordinate components in the vector output.Classcom.numericalmethod.suanshu.analysis.function.rn2r1SuanShujavadoc
R1toConstantMatrixA constant matrix function maps a real number to a constant matrix: (R^n ightarrow A).Classcom.numericalmethod.suanshu.analysis.function.matrixSuanShujavadoc
R1toMatrixThis is a function that maps from R1 to a Matrix space.Classcom.numericalmethod.suanshu.analysis.function.matrixSuanShujavadoc
R2toMatrixThis is a function that maps from R2 to a Matrix space.Classcom.numericalmethod.suanshu.analysis.function.matrixSuanShujavadoc
RamerDouglasPeuckerThe Ramer-Douglas-Peucker algorithm simplifies a PolygonalChain by removing vertices which do not affect the shape of the curve to a given tolerance.Classcom.numericalmethod.suanshu.geometry.polylineSuanShujavadoc
Rand1BinThe Rand-1-Bin rule is defined by: mutation by adding a scaled, randomly sampled vector difference to a third vectorClasscom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShujavadoc
RandomBetaGeneratorThis is a random number generator that generates random deviates according to the Beta distribution.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariate.betaSuanShujavadoc
RandomExpGeneratorThis is a random number generator that generates random deviates according to the exponential distribution.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariate.expSuanShujavadoc
RandomGammaGeneratorThis is a random number generator that generates random deviates according to the Gamma distribution.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShujavadoc
RandomizedFunctionA variant of Function, which permits subclasses to offer randomized functions.Classcom.numericalmethod.suanshu.grid.function.randomSuanShujavadoc
RandomLongGeneratorA (pseudo) random number generator that generates a sequence of longs that lack any pattern and are uniformly distributed.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc
RandomNumberGeneratorA (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc
RandomProcessThis interface represents a univariate random process a.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.randomSuanShujavadoc
RandomRealizationGeneratorThis interface defines a generator to construct random realizations from a univariate stochastic process.Interfacecom.numericalmethod.suanshu.stats.stochasticprocess.univariate.randomSuanShujavadoc
RandomRealizationOfRandomProcessThis class generates random realizations from a random/stochastic process.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.randomSuanShujavadoc
RandomStandardNormalGeneratorInterfacecom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShujavadoc
RandomVectorGeneratorA (pseudo) multivariate random number generator samples a random vector from a multivariate distribution.Interfacecom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShujavadoc
RandomWalkThis is the Random Walk construction of a stochastic process per SDE specification.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.randomSuanShujavadoc
RankRank is a relationship between a set of items such that, for any two items, the first is either "ranked higher than", "ranked lower than" or "ranked equal to" the second.Classcom.numericalmethod.suanshu.stats.descriptive.rankSuanShujavadoc
RankOneMinimizerThe Rank One method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShujavadoc
RastriginThe Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms.Classcom.numericalmethod.suanshu.analysis.function.specialSuanShujavadoc
RayleighDistributionThe L2 norm of (x1, x2), where xi's are normal, uncorrelated, equal variance and have the Rayleigh distributions.Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
RayleighRNGThis random number generator samples from the Rayleigh distribution using the inverse transform sampling method.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc

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RealA real number is an arbitrary precision number.Classcom.numericalmethod.suanshu.numberSuanShujavadoc
RealIntervalThis is an interval on the real line.Classcom.numericalmethod.suanshu.intervalSuanShujavadoc
RealizationThis is a univariate time series indexed real numbers.Interfacecom.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtimeSuanShujavadoc
RealMatrixThis is a Real matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtypeSuanShujavadoc
RealScalarFunctionA real valued function a (R^n ightarrow R) function, (y = f(x_1, .Interfacecom.numericalmethod.suanshu.analysis.function.rn2r1SuanShujavadoc
RealScalarFunctionChromosomeThis chromosome encodes a real valued function.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegridSuanShujavadoc
RealScalarSubFunctionThis constructs a RealScalarFunction from another RealScalarFunction by restricting/fixing the values of a subset ofClasscom.numericalmethod.suanshu.analysis.function.rn2r1SuanShujavadoc
RealVectorFunctionA vector-valued function a (R^n ightarrow R^m) function, ([y_1,.Interfacecom.numericalmethod.suanshu.analysis.function.rn2rmSuanShujavadoc
RealVectorSpaceA vector space is a set of vectors that are closed under some operations.Classcom.numericalmethod.suanshu.algebra.linear.vector.doubles.operationSuanShujavadoc
RealVectorSubFunctionThis constructs a RealVectorFunction from another RealVectorFunction by restricting/fixing the values of a subset of variables.Classcom.numericalmethod.suanshu.analysis.function.rn2rmSuanShujavadoc
RecursiveGridInterpolationThis algorithm works by recursively calling lower order interpolation (hence the cost is exponential), until the given univariate algorithm can be used when the remaining dimensionClasscom.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariateSuanShujavadoc
ReferenceClasscom.numericalmethod.suanshu.misc.parallelSuanShujavadoc
RelativeToleranceThe stopping criteria is that the norm of the residual r relative to the input base is equal to or smaller than the specifiedClasscom.numericalmethod.suanshu.misc.algorithm.iterative.toleranceSuanShujavadoc
RemoteConfigJava class for remoteConfig complex type.Classcom.numericalmethod.suanshu.grid.config.xml.schemaSuanShujavadoc
RemoteConfigurationInterfacecom.numericalmethod.suanshu.grid.config.remoteSuanShujavadoc
RemoteGridExecutorInterface for classes that execute their tasks remotely.Interfacecom.numericalmethod.suanshu.grid.executor.remoteSuanShujavadoc
RemoteGridExecutorTestHelperWhen using this class directly, you should remember to call shutdown() after you're done This class is designed to help build unit tests for distributed computations using the gridClasscom.numericalmethod.suanshu.grid.testSuanShujavadoc
ResamplerThis is the interface of a re-sampler method.Interfacecom.numericalmethod.suanshu.stats.random.sampler.resamplerSuanShujavadoc
ResamplerModelInterfacecom.numericalmethod.suanshu.model.lai2010.fitSuanShujavadoc
ResultSimple immutable message class to communicate results.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.messageSuanShujavadoc
ReturnLevelGiven a GEV distribution of a random variable (X), the return level (eta) is the value that is expected to be exceeded on average once every interval of time (T), with a probability ofClasscom.numericalmethod.suanshu.stats.evt.functionSuanShujavadoc
ReturnPeriodThe return period (R) of a level (eta) for a random variable (X) is the mean number of trials that must be done for (X) to exceed (eta).Classcom.numericalmethod.suanshu.stats.evt.functionSuanShujavadoc
ReturnsMomentsContains the estimated moments of asset returns.Classcom.numericalmethod.suanshu.model.returns.momentsSuanShujavadoc
ReturnsMomentsEstimatorInterfacecom.numericalmethod.suanshu.model.lai2010.fitSuanShujavadoc
ReturnsResamplerFactoryThis is a factory interface to construct new instances of multivariate resamplers.Interfacecom.numericalmethod.suanshu.model.lai2010.ceta.npeb.resamplerSuanShujavadoc

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ReversedWeibullDistributionThe Reversed Weibull distribution is a special case (Type III) of the generalized extreme value distribution, with (xi<0).Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShujavadoc
RiddersRidders' method computes the numerical derivative of a function.Classcom.numericalmethod.suanshu.analysis.differentiationSuanShujavadoc
RiemannThis is a wrapper class that integrates a function by using an appropriate integrator together with Romberg's method.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemannSuanShujavadoc
RingInterfacecom.numericalmethod.suanshu.algebra.structureSuanShujavadoc
RLGFunctionRandomized function that requires a single RLG for evaluation.Classcom.numericalmethod.suanshu.grid.function.randomSuanShujavadoc
RngConfigJava class for rngConfig complex type.Classcom.numericalmethod.suanshu.grid.config.xml.schemaSuanShujavadoc
RNGsInterfacecom.numericalmethod.suanshu.grid.function.randomSuanShujavadoc
RNGUtilsProvides static methods that wraps random number generators to produce synchronized generators.Classcom.numericalmethod.suanshu.stats.random.rngSuanShujavadoc
RntoMatrixThis interface is a function that maps from Rn to a Matrix space.Interfacecom.numericalmethod.suanshu.analysis.function.matrixSuanShujavadoc
RobustAdaptiveMetropolisA variation of Metropolis, that uses the estimated covariance of the target distribution in the proposal distribution, based on a paper by Vihola (2011).Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShujavadoc
RombergRomberg's method computes an integral by generating a sequence of estimations of the integral value and then doing an extrapolation.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotesSuanShujavadoc
RootedTreeA rooted tree is a directed graph, and has a root to measure distance from theSee Also:Wikipedia: Simple graphInterfacecom.numericalmethod.suanshu.graphSuanShujavadoc
RungeKuttaThe Runge-Kutta methods are an important family of implicit and explicit iterative methods for the approximation of solutions of ordinary differential equations.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKutta1This is the first-order Runge-Kutta formula, which is the same as the Euler method.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKutta10This is the tenth-order Runge-Kutta formula.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKutta2This is the second-order Runge-Kutta formula, which can be implemented efficiently with a three-step algorithm.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKutta3This is the third-order Runge-Kutta formula.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKutta4This is the fourth-order Runge-Kutta formula.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKutta5This is the fifth-order Runge-Kutta formula.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKutta6This is the sixth-order Runge-Kutta formula.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKutta7This is the seventh-order Runge-Kutta formula.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKutta8This is the eighth-order Runge-Kutta formula.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKuttaFehlbergThe Runge-Kutta-Fehlberg method is a version of the classic Runge-Kutta method, which additionally uses step-size control and hence allows specification of a local truncation errorClasscom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKuttaIntegratorThis integrator works with a single-step stepper which estimates the solution for the next step given the solution of the current step.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc
RungeKuttaStepperInterfacecom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekuttaSuanShujavadoc

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SampleAutoCorrelationThis is the sample Auto-Correlation Function (ACF) for a univariate data set.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.sampleSuanShujavadoc
SampleAutoCovarianceThis is the sample Auto-Covariance Function (ACVF) for a univariate data set.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.sampleSuanShujavadoc
SampleCovarianceThis class computes the Covariance matrix of a matrix, where the (i, j) entry is the covariance of the i-th column and j-th column of the matrix.Classcom.numericalmethod.suanshu.stats.descriptive.covarianceSuanShujavadoc
SamplePartialAutoCorrelationThis is the sample partial Auto-Correlation Function (PACF) for a univariate data set.Classcom.numericalmethod.suanshu.stats.timeseries.linear.univariate.sampleSuanShujavadoc
ScaledPolynomialThis constructs a scaled polynomial that has neither too big or too small coefficients, hence avoiding overflow or underflow.Classcom.numericalmethod.suanshu.analysis.function.polynomialSuanShujavadoc
ScientificNotationClasscom.numericalmethod.suanshu.numberSuanShujavadoc
SDEThis class represents a univariate, continuous-time Stochastic Differential Equation (SDE) of the following form.Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sdeSuanShujavadoc
SDPDualProblemA dual SDP problem, as in equation 14.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problemSuanShujavadoc
SDPPrimalProblemA Primal SDP problem, as in equation 14.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problemSuanShujavadoc
SDPT3v4This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3 version 4.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpointSuanShujavadoc
SeedableA seed-able experiment allow the same experiment to be repeated in exactly the same way.Interfacecom.numericalmethod.suanshu.stats.randomSuanShujavadoc
SelectionByAICIn each step, a factor is added if the resulting model has the highest AIC, until no factor addition can result in a model with AIC higher than the current AIC.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShujavadoc
SelectionByZValueIn each step, the most significant factor is added, until all remaining factors are insignificant.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShujavadoc
SemiImplicitExtrapolationSemi-Implicit Extrapolation is a method of solving ordinary differential equations, that is similar to Burlisch-Stoer extrapolation.Classcom.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolationSuanShujavadoc
SequenceA sequence is an ordered list of (real) numbers.Interfacecom.numericalmethod.suanshu.analysis.sequenceSuanShujavadoc
ShapiroWilkThe Shapiro-Wilk test tests the null hypothesis that a sample comes from a normally distributed population.Classcom.numericalmethod.suanshu.stats.test.distribution.normalitySuanShujavadoc
ShapiroWilkDistributionShapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics, which tests the null hypothesis that a sample comes from a normally distributed population.Classcom.numericalmethod.suanshu.stats.test.distribution.normalitySuanShujavadoc
ShortestPathIn graph theory, a shortest path algorithm finds a path between two vertices in a graph such that the sum of the weights of its constituent edges is minimized.Interfacecom.numericalmethod.suanshu.graph.algorithm.shortestpathSuanShujavadoc
SHR0SHR0 is a simple uniform random number generator.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniformSuanShujavadoc
SHR3SHR3 is a 3-shift-register generator with period 2^32-1.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniformSuanShujavadoc
SiegelTukeyThe Siegel-Tukey test tests for differences in scale (variability) between two groups.Classcom.numericalmethod.suanshu.stats.test.rankSuanShujavadoc
SimilarMatrixGiven a matrix A and an invertible matrix P, we construct the similar matrixSee Also:Wikipedia: Similar matrixClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
SimpleAnnealingFunctionThis annealing function takes a random step in a uniform direction, where the step size depends only on the temperature.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShujavadoc
SimpleAR1FitThis class does a quick AR(1) fitting to the time series, essentially treating the returns as independent.Classcom.numericalmethod.suanshu.model.lai2010.fitSuanShujavadoc
SimpleAR1MomentsClasscom.numericalmethod.suanshu.model.lai2010.fitSuanShujavadoc

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SimpleArcA simple arc has two vertices: head and tail.Classcom.numericalmethod.suanshu.graph.typeSuanShujavadoc
SimpleCellFactoryA SimpleCellFactory produces SimpleCellFactory.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegridSuanShujavadoc
SimpleDoubleArrayOperationThis is a simple, single-threaded implementation of the array math operations.Classcom.numericalmethod.suanshu.number.doublearraySuanShujavadoc
SimpleEdgeA simple edge has two vertices.Classcom.numericalmethod.suanshu.graph.typeSuanShujavadoc
SimpleGARCHFitThis class does a quick GARCH(1,1) fitting to the time series, essentially treating the returns as independent.Classcom.numericalmethod.suanshu.model.lai2010.fitSuanShujavadoc
SimpleGARCHMomentsEstimates the moments by GARCH model.Classcom.numericalmethod.suanshu.model.lai2010.fitSuanShujavadoc
SimpleGridMinimizerThis minimizer is a simple global optimization method.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegridSuanShujavadoc
SimpleMatrixMathOperationThis is a generic, single-threaded implementation of matrix math operations.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperationSuanShujavadoc
SimpleMCThis is a time-homogeneous Markov chain with a finite state space.Classcom.numericalmethod.suanshu.stats.markovchainSuanShujavadoc
SimpleTemperatureFunctionAbstract class for the common case where (T^V_t = T^A_t).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShujavadoc
SimpleTestRemoteConfigurationDoesn't perform failure detection.Classcom.numericalmethod.suanshu.grid.test.configSuanShujavadoc
SimpleTimeSeriesThis simple univariate time series simply wraps a double[] to form a time series.Classcom.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttimeSuanShujavadoc
SimplexCuttingPlaneMinimizerThe use of cutting planes to solve Mixed Integer Linear Programming (MILP) problems was introduced by Ralph E Gomory.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShujavadoc
SimplexPivotingA simplex pivoting finds a row and column to exchange to reduce the cost function.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivotingSuanShujavadoc
SimplexTableThis is a simplex table used to solve a linear programming problem using a simplex method.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShujavadoc
SimpsonSimpson's rule can be thought of as a special case of Romberg's method.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotesSuanShujavadoc
SimulatedAnnealingMinimizerSimulated Annealing is a global optimization meta-heuristic that is inspired by annealing in metallurgy.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealingSuanShujavadoc
SingleRLGRNGs that contains a single RLG instance.Classcom.numericalmethod.suanshu.grid.function.randomSuanShujavadoc
SingularValueByDQDSComputes all the singular values of a bidiagonal matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqdsSuanShujavadoc
SkewnessSkewness is a measure of the asymmetry of the probability distribution.Classcom.numericalmethod.suanshu.stats.descriptive.momentSuanShujavadoc
SlaveSlave that is meant to run on a remote machine and that creates the Worker instances.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.actorSuanShujavadoc
SlaveConfigJava class for slaveConfig complex type.Classcom.numericalmethod.suanshu.grid.config.xml.schemaSuanShujavadoc
SlavesConfigJava class for slavesConfig complex type.Classcom.numericalmethod.suanshu.grid.config.xml.schemaSuanShujavadoc
SmallestSubscriptRuleBland's smallest-subscript rule is for anti-cycling in choosing a pivot.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivotingSuanShujavadoc
SOCPDualProblemThis is the Dual Second Order Conic Programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problemSuanShujavadoc

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SOCPGeneralConstraintThis represents the SOCP general constraint of this form.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problemSuanShujavadoc
SOCPGeneralConstraintsThis represents a set of SOCP general constraints of this form.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problemSuanShujavadoc
SOCPGeneralProblemMany convex programming problems can be represented in the following form.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problemSuanShujavadoc
SOCPPortfolioConstraintAn SOCP constraint for portfolio optimization, e.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShujavadoc
SOCPPortfolioObjectiveFunctionConstructs the objective function for portfolio optimization.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShujavadoc
SOCPPortfolioProblemConstructs an SOCP problem for portfolio optimization.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShujavadoc
SOCPRiskConstraintClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShujavadoc
SORSweepThis is a building block for to perform the forward or backward sweep.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShujavadoc
SortableArrayThese arrays can be sorted according to the dictionary order.Classcom.numericalmethod.suanshu.misc.datastructureSuanShujavadoc
SortedOrderedPairsThe ordered pairs are first sorted by abscissa, then by ordinate.Classcom.numericalmethod.suanshu.analysis.function.tupleSuanShujavadoc
SparseDAGraphThis class implements the sparse directed acyclic graph representation.Classcom.numericalmethod.suanshu.graph.typeSuanShujavadoc
SparseDiGraphThis class implements the sparse directed graph representation.Classcom.numericalmethod.suanshu.graph.typeSuanShujavadoc
SparseGraphThis class implements the sparse graph representation.Classcom.numericalmethod.suanshu.graph.typeSuanShujavadoc
SparseMatrixA sparse matrix stores only non-zero values.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShujavadoc
SparseMatrixUtilsThese are the utility functions for SparseMatrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShujavadoc
SparseStructureThis interface defines common operations on sparse structures such as sparse vector or sparse matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShujavadoc
SparseTreeThis class implements the sparse tree representation.Classcom.numericalmethod.suanshu.graph.typeSuanShujavadoc
SparseUnDiGraphThis class implements the sparse undirected graph representation.Classcom.numericalmethod.suanshu.graph.typeSuanShujavadoc
SparseVectorA sparse vector stores only non-zero values.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShujavadoc
SpearmanRankCorrelationSpearman's rank correlation coefficient or Spearman's rho is a non-parametric measure of statistical dependence between two variables.Classcom.numericalmethod.suanshu.stats.descriptive.correlationSuanShujavadoc
SpectrumA spectrum is the set of eigenvalues of a matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShujavadoc
SQPActiveSetMinimizerSequential quadratic programming (SQP) is an iterative method for nonlinear optimization.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activesetSuanShujavadoc
SQPActiveSetOnlyEqualityConstraint1MinimizerThis implementation is a modified version of Algorithm 15.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShujavadoc
SQPActiveSetOnlyEqualityConstraint2MinimizerThis particular implementation of SQPActiveSetOnlyEqualityConstraint1Minimizer uses SQPASEVariation2.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShujavadoc
SQPActiveSetOnlyInequalityConstraintMinimizerThis implementation is a modified version of Algorithm 15.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activesetSuanShujavadoc

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SQPASEVariationThis interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem with only equality constraints using Sequential Quadratic Programming.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShujavadoc
SQPASEVariation1This implementation is a modified version of the algorithm in the reference to solve a general constrained minimization problem using Sequential Quadratic Programming.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShujavadoc
SQPASEVariation2This implementation tries to find an exact positive definite Hessian whenever possible.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShujavadoc
SQPASVariationThis interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem using Sequential Quadratic Programming.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activesetSuanShujavadoc
SQPASVariation1This implementation is a modified version of Algorithm 15.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activesetSuanShujavadoc
SSORPreconditionerSSOR preconditioner is derived from a symmetric coefficient matrix A which is decomposed asClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShujavadoc
StandardCumulativeNormalThe cumulative Normal distribution function describes the probability of a Normal random variable falling in the interval ((-infty, x]).Interfacecom.numericalmethod.suanshu.analysis.function.special.gaussianSuanShujavadoc
StandardIntervalThis transformation is for mapping integral region from [a, b] to [-1, 1].Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
StandardNormalRNGAn alias for Zignor2005 to provide a default implementation for sampling from the standard Normal distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShujavadoc
StateEquationThis is the state equation in a controlled dynamic linear model.Classcom.numericalmethod.suanshu.stats.dlm.univariateSuanShujavadoc
StatisticA statistic (singular) is a single measure of some attribute of a sample (e.Interfacecom.numericalmethod.suanshu.stats.descriptiveSuanShujavadoc
StatisticFactoryA factory to construct a new Statistic.Interfacecom.numericalmethod.suanshu.stats.descriptiveSuanShujavadoc
SteepestDescentMinimizerA steepest descent algorithm finds the minimum by moving along the negative of the steepest gradient direction.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShujavadoc
SteepestDescentSolverThe Steepest Descent method (SDM) solves a symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShujavadoc
StepFunctionA step function (or staircase function) is a finite linear combination of indicator functions of Informally speaking, a step function is a piecewise constant function having only finitely manyClasscom.numericalmethod.suanshu.analysis.function.rn2r1.univariateSuanShujavadoc
StopConditionDefines when an algorithm stops (the iterations).Interfacecom.numericalmethod.suanshu.misc.algorithm.stopconditionSuanShujavadoc
StringUtilsUtility methods for string manipulation.Classcom.numericalmethod.suanshu.miscSuanShujavadoc
SturmCountClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
SubFunctionA sub-function, g, is defined over a subset of the domain of another (original) function,Classcom.numericalmethod.suanshu.analysis.functionSuanShujavadoc
SubMatrixBlockSub-matrix block representation for block algorithm.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShujavadoc
SubMatrixRefThis is a 'reference' to a sub-matrix of a larger matrix without copying it.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
SubProblemMinimizerThis minimizer solves a constrained optimization sub-problem where the values for some variables are held fixed for the original optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShujavadoc
SubstitutionRuleA substitution rule specifies (x(t)) and (frac{mathrm{d} x}{mathrm{d} t}).Interfacecom.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitutionSuanShujavadoc
SubVectorRefRepresents a sub-vector backed by the referenced vector, without data copying.Classcom.numericalmethod.suanshu.algebra.linear.vector.doublesSuanShujavadoc
SuccessiveOverrelaxationSolverThe Successive Overrelaxation method (SOR), is devised by applying extrapolation to the Gauss-Seidel method.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShujavadoc

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SummationSummation is the operation of adding a sequence of numbers; the result is their sum or total.Classcom.numericalmethod.suanshu.analysis.sequenceSuanShujavadoc
SumOfPenaltiesThis penalty function sums up the costs from a set of constituent penalty functions.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShujavadoc
SumOfPoweredWeightsDefines portfolio diversification as D(w) = sum_i w_i^PClasscom.numericalmethod.suanshu.model.corvalan2005.diversificationSuanShujavadoc
SumOfSquaredWeightsDefines portfolio diversification as D(w) = sum_i w_i^2Classcom.numericalmethod.suanshu.model.corvalan2005.diversificationSuanShujavadoc
SumOfWLogWDefines portfolio diversification as D(w) = sum_i w_i ln(w_i)Classcom.numericalmethod.suanshu.model.corvalan2005.diversificationSuanShujavadoc
SVDSVD decomposition decomposes a matrix A of dimension m x n, where m >= n, U' * A * V = D, or U * D * V' = A.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svdSuanShujavadoc
SVDbyMR3Given a matrix A, computes its singular value decomposition (SVD), using "Algorithm of Multiple Relatively Robust Representations" (MRRR).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3SuanShujavadoc
SVDDecompositionSVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that U' * A * V = D, or U * D * V' = A.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svdSuanShujavadoc
SVECSVEC converts a symmetric matrix K = {Kij} into a vector of dimension n(n+1)/2.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
SymmetricEigenByMR3Computes eigen decomposition for a symmetric matrix using "Algorithm of Multiple Relatively Robust Representations" (MRRR).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
SymmetricEigenFor2x2MatrixClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShujavadoc
SymmetricKroneckerCompute the symmetric Kronecker product of two matrices.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
SymmetricMatrixA symmetric matrix is a square matrix such that its transpose equals to itself, i.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangleSuanShujavadoc
SymmetricQRAlgorithmThe symmetric QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a square, symmetric matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShujavadoc
SymmetricSuccessiveOverrelaxationSolverThe Symmetric Successive Overrelaxation method (SSOR) is like SOR, but it performs in eachClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShujavadoc
SymmetricSVDThis algorithm calculates the Singular Value Decomposition (SVD) of a square, symmetric matrix A using QR algorithm.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svdSuanShujavadoc
SynchronizedStatisticThis is a thread-safe wrapper of Statistic by synchronizing all public methods so that only one thread at a time can access the instance.Classcom.numericalmethod.suanshu.stats.descriptiveSuanShujavadoc
TStudent's t-test tests for the equality of means, for the one-sample case, against a hypothetical mean,Classcom.numericalmethod.suanshu.stats.test.meanSuanShujavadoc
TableA table is a means of arranging data in rows and columns.Interfacecom.numericalmethod.suanshu.misc.datastructureSuanShujavadoc
TDistributionThe Student t distribution is the probability distribution of t, where t = frac{ar{x} - mu}{s / sqrt N}Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
TemperatureFunctionA temperature function defines a temperature schedule used in simulated annealing.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShujavadoc
TemperedAcceptanceProbabilityFunctionA tempered acceptance probability function computes the probability that the next state transition will be accepted.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunctionSuanShujavadoc
TestRemoteConfigurationFactoryUsed to allow the test to customize its configuration, after the slaves have been configured by a RemoteGridExecutorTestHelper.Interfacecom.numericalmethod.suanshu.grid.testSuanShujavadoc
ThinRNGThinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc
ThinRVGThinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShujavadoc

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ThomasAlgorithmThomas algorithm is an efficient algorithm to solve a linear tridiagonal matrix equation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShujavadoc
ThreadIDRLGClasscom.numericalmethod.suanshu.stats.random.rng.concurrent.contextSuanShujavadoc
ThreadIDRNGClasscom.numericalmethod.suanshu.stats.random.rng.concurrent.contextSuanShujavadoc
ThreadLocalRngGridExecutorA simple adaptor, which allows for execution of RandomizedFunctions, using a random number generator for each thread (with the same thread name).Classcom.numericalmethod.suanshu.grid.executor.localSuanShujavadoc
TiesCount the number of occurrences of each distinctive value.Classcom.numericalmethod.suanshu.combinatoricsSuanShujavadoc
TimeGridSpecify the time points in a grid or axis.Interfacecom.numericalmethod.suanshu.stats.stochasticprocess.timegridSuanShujavadoc
TimeIntervalThis is a time interval.Classcom.numericalmethod.suanshu.misc.datastructure.timeSuanShujavadoc
TimeIntervalsThis is a collection of time intervals TimeInterval.Classcom.numericalmethod.suanshu.misc.datastructure.timeSuanShujavadoc
TimeSeriesA time series is a serially indexed collection of items.Interfacecom.numericalmethod.suanshu.stats.timeseries.datastructureSuanShujavadoc
ToleranceThe tolerance criteria for an iterative algorithm to stop.Interfacecom.numericalmethod.suanshu.misc.algorithm.iterative.toleranceSuanShujavadoc
TrapezoidalThe Trapezoidal rule is a closed type Newton-Cotes formula, where the integral interval is evenly divided into N sub-intervals.Classcom.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotesSuanShujavadoc
TraversalFromRootsA graph traversal is the problem of visiting all the nodes in a graph in a particular manner.Classcom.numericalmethod.suanshu.graph.algorithm.traversalSuanShujavadoc
TreeA tree is an undirected graph in which any two vertices are connected by exactly one simple path.Classcom.numericalmethod.suanshu.graphSuanShujavadoc
TrendTypeThese are the three versions of the Augmented Dickey-Fuller (ADF) test.Classcom.numericalmethod.suanshu.stats.test.timeseries.adfSuanShujavadoc
TriangularDistributionClasscom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
TridiagonalDeflationSearchThis class locates deflation in a tridiagonal matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShujavadoc
TriDiagonalizationA tri-diagonal matrix A is a matrix such that it has non-zero elements only in the main diagonal, the first diagonal below, and the firstClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalizationSuanShujavadoc
TridiagonalMatrixA tri-diagonal matrix has non-zero entries only on the super, main and sub diagonals.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonalSuanShujavadoc
TrigammaThe trigamma function is defined as the logarithmic derivative of the digamma function.Classcom.numericalmethod.suanshu.analysis.function.special.gammaSuanShujavadoc
TrigMathA collection of trigonometric functions complementary to those in Java's Math class.Classcom.numericalmethod.suanshu.geometrySuanShujavadoc
TripleA triple is a tuple of length three.Classcom.numericalmethod.suanshu.analysis.function.tupleSuanShujavadoc
TrivariateRealFunctionA trivariate real function takes three real arguments and outputs one real value.Interfacecom.numericalmethod.suanshu.analysis.function.rn2r1SuanShujavadoc
TruncatedNormalDistributionThe truncated Normal distribution is the probability distribution of a normally distributed random variable whose value is either bounded below or above (or both).Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
TwiddleGenerates all combinations of M elements drawn without replacement from a set of N elements.Classcom.numericalmethod.suanshu.combinatoricsSuanShujavadoc
UnconstrainedLASSObyCoordinateDescentThis class solves the unconstrained form of LASSO, that is, min_w left { left | Xw - y ight |_2^2 + lambda * left | wClasscom.numericalmethod.suanshu.stats.regression.linear.lassoSuanShujavadoc

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UnconstrainedLASSObyQPThis class solves the unconstrained form of LASSO (i.Classcom.numericalmethod.suanshu.stats.regression.linear.lassoSuanShujavadoc
UnconstrainedLASSOProblemA LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization.Classcom.numericalmethod.suanshu.stats.regression.linear.lassoSuanShujavadoc
UnDiGraphAn undirected graph is a graph, or set of nodes connected by edges, where an edge does not differentiate between (a, b) or (b, a).Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
UndirectedEdgeA tagging interface for implementations of an undirected graph that accept only undirected edges.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
UniformDistributionOverBoxThis random vector generator uniformly samples points over a box region.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShujavadoc
UniformDistributionOverBox1This algorithm, by sampling uniformly in each dimension, generates a set of initials uniformly distributed over a box region,Classcom.numericalmethod.suanshu.optimization.multivariate.initializationSuanShujavadoc
UniformDistributionOverBox2This algorithm, by perturbing each grid point by a small random scale, generates a set of initials uniformly distributed over a box region,Classcom.numericalmethod.suanshu.optimization.multivariate.initializationSuanShujavadoc
UniformMeshOverRegionThe initial population is generated by putting a uniform mesh/grid/net over the entire region.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgenerationSuanShujavadoc
UniformRNGA pseudo uniform random number generator samples numbers from the unit interval, [0, 1], in such a way that there are equal probabilities of them falling in any same length sub-interval.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniformSuanShujavadoc
UnirootA root-finding algorithm is a numerical algorithm for finding a value x such that f(x) = 0, for a given function f.Interfacecom.numericalmethod.suanshu.analysis.root.univariateSuanShujavadoc
UnitGridThis is the sequence of time points [0, 1, .Classcom.numericalmethod.suanshu.stats.stochasticprocess.timegridSuanShujavadoc
UnivariateEVDDistribution of extreme values (e.Interfacecom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShujavadoc
UnivariateMinimizerA univariate minimizer minimizes a univariate function.Interfacecom.numericalmethod.suanshu.optimization.univariateSuanShujavadoc
UnivariateRealFunctionA univariate real function takes one real argument and outputs one real value.Interfacecom.numericalmethod.suanshu.analysis.function.rn2r1.univariateSuanShujavadoc
UnivariateTimeSeriesThis is a univariate time series indexed by some notion of time.Interfacecom.numericalmethod.suanshu.stats.timeseries.datastructure.univariateSuanShujavadoc
UnivariateTimeSeriesUtilsThese are the utility functions to manipulate a univariate time series.Classcom.numericalmethod.suanshu.stats.timeseries.datastructure.univariateSuanShujavadoc
UnsatisfiableErrorCriterionExceptionAn exception that is thrown when the error criterion cannot be met.Classcom.numericalmethod.suanshu.analysis.differentialequationSuanShujavadoc
UpperBoundConstraintsClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShujavadoc
UpperTriangularMatrixAn upper triangular matrix has 0 entries where row index is greater than column index.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangleSuanShujavadoc
VanDerWaerdenThe Van der Waerden test tests for the equality of all population distribution functions.Classcom.numericalmethod.suanshu.stats.test.rankSuanShujavadoc
VanDerWaerden1969Classcom.numericalmethod.suanshu.stats.random.rng.univariate.betaSuanShujavadoc
VARFitThis class construct a VAR model by estimating the coefficients using OLS regression.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VarianceThe variance of a sample is the average squared deviations from the sample mean.Classcom.numericalmethod.suanshu.stats.descriptive.momentSuanShujavadoc
VariancebtXClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShujavadoc
VARIMAModelAn ARIMA(p, d, q) process, Yt, is such that X_t = (1 - L)^d Y_tClasscom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arimaSuanShujavadoc

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VARIMASimThis class simulates a multivariate ARIMA process.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arimaSuanShujavadoc
VARIMAXModelThe ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arimaSuanShujavadoc
VARLinearRepresentationThe linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VARMAAutoCorrelationCompute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VARMAAutoCovarianceCompute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VARMAForecastOneStepThis is an implementation, adapted for an ARMA process, of the innovation algorithm, which is an efficient way of obtaining a one step least square linear predictor.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VARMAModelA multivariate ARMA model, Xt, takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VARMAXModelThe ARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VARModelThis class represents a VAR model.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VARXModelA VARX (Vector AutoRegressive model with eXogeneous inputs) model, Xt, takes Y_t = mu + Sigma phi_i * Y_{t-i} + Psi * D_t + epsilon_tClasscom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VECMA Vector Error Correction Model (VECM(p)) has one of the following specifications: Delta Y_t = mu + Pi Y_{t-1} + sum left ( Gamma_i Y_{t-1} ight ) + Psi D_t + epsilon_t, i = 1, 2, .Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VECMLongrunThe long-run Vector Error Correction Model (VECM(p)) takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VECMTransitoryA transitory Vector Error Correction Model (VECM(p)) takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VectorAn Euclidean vector is a geometric object that has both a magnitude/length and a direction.Interfacecom.numericalmethod.suanshu.algebra.linear.vector.doublesSuanShujavadoc
VectorAccessExceptionThis is the exception thrown when any invalid access to a Vector instance is detected, e.Classcom.numericalmethod.suanshu.algebra.linear.vectorSuanShujavadoc
VectorFactoryThese are the utility functions that create new instances of vectors from existing ones.Classcom.numericalmethod.suanshu.algebra.linear.vector.doubles.operationSuanShujavadoc
VectorMonitorThis IterationMonitor stores all vectors generated during iterations.Classcom.numericalmethod.suanshu.misc.algorithm.iterative.monitorSuanShujavadoc
VectorSizeMismatchThis is the exception thrown when an operation is performed on two vectors with differentSee Also:Serialized FormClasscom.numericalmethod.suanshu.algebra.linear.vectorSuanShujavadoc
VectorSpaceA vector space is a set V together with two binary operations that combine two entities to yield a third, called vector addition and scalar multiplication.Interfacecom.numericalmethod.suanshu.algebra.structureSuanShujavadoc
VertexTreeA VertexTree is both a tree and a vertex/node.Classcom.numericalmethod.suanshu.graph.typeSuanShujavadoc
ViterbiThe Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states - called the Viterbi path - that results inClasscom.numericalmethod.suanshu.stats.hmmSuanShujavadoc
VMAInvertibilityThe inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
VMAModelThis class represents a multivariate MA model.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShujavadoc
WaveEquation1DA one-dimensional wave equation is a hyperbolic PDE that takes the following form.Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1SuanShujavadoc
WaveEquation2DA two-dimensional wave equation is a hyperbolic PDE that takes the following form.Classcom.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2SuanShujavadoc

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WeibullDistributionThe Weibull distribution interpolates between the exponential distribution k = 1 and the Rayleigh distribution (k = 2),Classcom.numericalmethod.suanshu.stats.distribution.univariateSuanShujavadoc
WeibullRNGThis random number generator samples from the Weibull distribution using the inverse transform sampling method.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShujavadoc
WeightedArcA weighted arc is an arc that has a weight or a cost associated with it.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
WeightedEdgeA weighted edge has a weight or a cost associated with it.Interfacecom.numericalmethod.suanshu.graphSuanShujavadoc
WeightedMeanThe weighted mean is defined as ar{x} = frac{ sum_{i=1}^N w_i x_i}{sum_{i=1}^N w_i}Classcom.numericalmethod.suanshu.stats.descriptive.moment.weightedSuanShujavadoc
WeightedRSSWeighted sum of squared residuals (RSS) for a given function (f(.Classcom.numericalmethod.suanshu.stats.regressionSuanShujavadoc
WeightedVarianceThe weighted sample variance is defined as follows.Classcom.numericalmethod.suanshu.stats.descriptive.moment.weightedSuanShujavadoc
WhiteThe White test tests for conditional heteroskedasticity.Classcom.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticitySuanShujavadoc
WilcoxonRankSumThe Wilcoxon rank sum test tests for the equality of means of two populations, or whether the means differ by an offset.Classcom.numericalmethod.suanshu.stats.test.rank.wilcoxonSuanShujavadoc
WilcoxonRankSumDistributionCompute the exact distribution of the Wilcoxon rank sum test statistic.Classcom.numericalmethod.suanshu.stats.test.rank.wilcoxonSuanShujavadoc
WilcoxonSignedRankThe Wilcoxon signed rank test tests, for the one-sample case, the median of the distribution against a hypothetical median, andClasscom.numericalmethod.suanshu.stats.test.rank.wilcoxonSuanShujavadoc
WilcoxonSignedRankDistributionCompute the exact distribution of the Wilcoxon signed rank test statistic.Classcom.numericalmethod.suanshu.stats.test.rank.wilcoxonSuanShujavadoc
WorkRepresents a unit of work as done by an actor (worker).Classcom.numericalmethod.suanshu.grid.executor.remote.akka.messageSuanShujavadoc
WorkAssignmentUtility class that contains the method that performs assignment of a task with a given index to a slave/worker with a given index (given the numbers of slaves and workers).Classcom.numericalmethod.suanshu.grid.executor.remote.akka.actorSuanShujavadoc
WorkerThe actor who does the real work.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.actorSuanShujavadoc
WorkerCountCollectorCollects the number of workers managed by each of the given slaves.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.actorSuanShujavadoc
WorkerCountReplyReply for WorkerCountRequest.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.messageSuanShujavadoc
WorkerCountRequestRequest for the number of workers managed by a slave.Classcom.numericalmethod.suanshu.grid.executor.remote.akka.messageSuanShujavadoc
XiTanLiu2010aXi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShujavadoc
XiTanLiu2010bXi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShujavadoc
XtAdaptedFunctionThis represents an Ft-adapted function that depends only on X(t).Classcom.numericalmethod.suanshu.stats.stochasticprocess.univariate.sdeSuanShujavadoc
ZangwillMinimizerZangwill's algorithm is an improved version of Powell's algorithm.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirectionSuanShujavadoc
ZeroDriftVectorThis class represents a 0 drift function.Classcom.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficientsSuanShujavadoc
ZeroPenaltyThis is a dummy zero cost (no cost) penalty function.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShujavadoc
Ziggurat2000The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShujavadoc

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Ziggurat2000ExpThis implements the ziggurat algorithm to sample from the exponential distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.expSuanShujavadoc
Zignor2005This is an improved version of the Ziggurat algorithm as proposed in the reference.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShujavadoc

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