A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

F

f - Variable in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference

the function to take the derivative of

f - Variable in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction

the real scalar function to compute the gradient for

f - Variable in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction

the real scalar function to compute the Hessian for

f - Variable in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction

the real vector function to compute the Jacobian for

f - Variable in class com.numericalmethod.suanshu.analysis.differentiation.Ridders

the function to take the derivative of

f - Variable in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference

the univariate function to take the derivative of

f - Variable in class com.numericalmethod.suanshu.analysis.function.rn2r1.Projection

the original Rⁿ → R^m function

f - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential

the original integrand

f - Variable in class com.numericalmethod.suanshu.analysis.uniroot.Uniroot

the univariate function to be solved for 0, f

f - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent

the function to be minimized

f - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.UnconstrainedProblem

the objective function to be minimized

f - Variable in class com.numericalmethod.suanshu.optimization.univariate.BracketSearch

the function to be minimized

F() - Method in enum com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistribution.TrendType

F(int) - Method in class com.numericalmethod.suanshu.stats.dlm.ObservationEquation

Get F(t), the coefficient matrix of x_t.

f - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals

diagnostic measure: F statistics mean of regression / mean squared error = sum((y_i_hat-y_mean)^2) / mean squared error [(TSS-RSS)/n] / [RSS/(m-n)] y_i_hat are the fitted values of the regression.

f - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator

the integrand

F - Class in com.numericalmethod.suanshu.stats.test.variance

The FDistribution-test tests whether two normal populations have the same variance.

F(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.F

Perform the FDistribution test to test for equal variance of two normal populations.

F(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.variance.F

Perform the FDistribution test to test for equal variance of two normal populations.

F - Variable in class com.numericalmethod.suanshu.stats.test.variance.F

the associated FDistribution distribution

F_sum_BtDt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration

a function of this integral /1 I = | (B)(dt) /0

F_sum_BtDt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_BtDt

F_sum_tBtDt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration

a function of this integral /1 | (t - 0.5) * (B) (dt) /0

F_sum_tBtDt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.F_sum_tBtDt

factorial(int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps

Compute the n factorial.

factorial(int) - Static method in class com.numericalmethod.suanshu.number.big.BigIntegerUtils

Compute the n factorial.

Family - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution

Family is a description of the error distribution and link function to be used in the GLM model.

Family(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Family

Construct an instance of Family.

family - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.GlmProblem

the exponential family distribution for the mean

FDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate

FDistribution distribution is the distribution of the ratio of two independent chi-squared variates.

FDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution

Create an FDistribution distribution.

fdx(UnivariateRealFunction) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable

Get the integrand in the "transformed" integral.

Fibonacci - Class in com.numericalmethod.suanshu.analysis.sequence

Construct a Fibonacci sequence.

Fibonacci(int) - Constructor for class com.numericalmethod.suanshu.analysis.sequence.Fibonacci

Construct a Fibonacci sequence.

Fibonacci - Class in com.numericalmethod.suanshu.optimization.univariate

Fibonacci search is a dichotomous search where a bracketing interval is sub-divided by the Fibonacci ratio.

Fibonacci() - Constructor for class com.numericalmethod.suanshu.optimization.univariate.Fibonacci

Field<F> - Interface in com.numericalmethod.suanshu.mathstructure

This interface represents a field.

Field.InverseNonExistent - Exception in com.numericalmethod.suanshu.mathstructure

RuntimeException thrown when the inverse of a field element does not exist.

Field.InverseNonExistent() - Constructor for exception com.numericalmethod.suanshu.mathstructure.Field.InverseNonExistent

Filter - Interface in com.numericalmethod.suanshu.signalprocessing.filter

This represents a filter for signal processing.

filtering(double[]) - Method in interface com.numericalmethod.suanshu.signalprocessing.filter.Filter

Get the filtered signals.

filtering(double[]) - Method in class com.numericalmethod.suanshu.signalprocessing.filter.MovingAverage

filtering(double[]) - Method in class com.numericalmethod.suanshu.signalprocessing.filter.MovingAverageByExtension

filtering(MultiVariateTimeSeries, MultiVariateTimeSeries) - Method in class com.numericalmethod.suanshu.stats.dlm.LinearKalmanFilter

Filter the observations.

filtering(MultiVariateTimeSeries) - Method in class com.numericalmethod.suanshu.stats.dlm.LinearKalmanFilter

Filter the observations without control variable.

Filtration - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration

This class represents the filtration information known at the end of time.

Filtration(TimeSeries<Double>) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration

Construct a Filtration from a Brownian path.

FiltrationFunction - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration

This class represents a function of time and a (fixed) Brownian path.

FiltrationFunction() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction

findPivot(Tableau) - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting.NaiveRule

findPivot(Tableau) - Method in interface com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting.SimplexPivoting

Compute the next swapping indices.

FiniteDifference - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate

This class computes the numerical partial derivative of a function.

FiniteDifference(RealScalarFunction, int[]) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference

Construct a finite difference instance for a multi-variable function.

FiniteDifference - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate

A finite difference (divided by a small increment) is an approximation of the derivative of a function.

FiniteDifference(UnivariateRealFunction, int, FiniteDifference.Type) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference

Construct an approximation of the derivative function for f using finite difference.

FiniteDifference.Type - Enum in com.numericalmethod.suanshu.analysis.differentiation.univariate

the types of finite difference available

FirstOrder - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent

This implements the steepest descent search using the first order expansion of the Taylor's series.

FirstOrder(FirstOrder.Method) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.FirstOrder

Construct an instance of FirstOrder to minimize a function f.

FirstOrder() - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.FirstOrder

Construct an instance of FirstOrder to minimize a function f, using Fletcher's inexact line search method.

FirstOrder.Method - Enum in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent

the methods available to do line search

FisherExactDistribution - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson

Fisher's exact test is a statistical significance test used in the analysis of contingency tables where sample sizes are small.

FisherExactDistribution(int[], int[], int, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.FisherExactDistribution

Construct the distribution for the Fisher's exact test.

FisherExactDistribution(int[], int[], int) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.FisherExactDistribution

Construct the distribution for the Fisher's exact test.

fit(GlmProblem, Vector) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.Fitting

Fit a Generalized Linear Model.

fit(GlmProblem, Vector) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS

fitted - Variable in class com.numericalmethod.suanshu.stats.regression.linear.Residuals

the fitted values, y^

Fitting - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm

This interface represents a fitting method for estimating β in a Generalized Linear Model (GLM).

Fletcher - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod

This penalty function sums up the squared costs penalties.

Fletcher(InequalityConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Fletcher

Construct a Fletcher penalty function from a set of inequality constraints.

Fletcher(InequalityConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Fletcher

Construct a Fletcher penalty function from a set of inequality constraints.

Fletcher(InequalityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Fletcher

Construct a Fletcher penalty function from a set of inequality constraints.

Fletcher - Class in com.numericalmethod.suanshu.optimization.unconstrained.linesearch

This is the Fletcher's inexact line search method.

Fletcher(double, double, double, double, RealScalarFunction, RealVectorFunction, double, int) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.linesearch.Fletcher

Construct a Fletcher instance to minimize a function f.

Fletcher(RealScalarFunction, RealVectorFunction) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.linesearch.Fletcher

Construct a Fletcher instance to minimize a function f with the default control parameters.

FletcherReeves - Class in com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection

The Fletcher-Reeves method is a variant of the Conjugate-Gradient method.

FletcherReeves() - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.FletcherReeves

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

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

floatValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation

foreach(double[], UnivariateRealFunction) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils

Get a double array in which each element is the result of applying the function f to the corresponding element in doubles.

forEach(Iterable<T>, IterationBody<T>) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor

Runs a "foreach" loop in parallel.

foreach(Vector, UnivariateRealFunction) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector

Produce a new vector in which each entry is the result of applying the function to the corresponding entry of this vector.

forLoop(int, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor

Runs a for-loop in parallel.

forLoop(int, int, LoopBody) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor

Calls forLoop with increment of 1.

forward(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SorSweep

Perform a forward sweep.

Forward - Class in com.numericalmethod.suanshu.stats.regression.linear.modelselection

To construct a GLM model for a set of observations using the forward selection method, we iteratively add a significant factor to the model, one at a time.

Forward(GlmProblem, double) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.modelselection.Forward

Construct automatically a GLM model using the forward selection method.

ForwardSubstitution - Class in com.numericalmethod.suanshu.matrix.doubles.linearsystem

Forward substitution solves a matrix equation in the form Lx = b by an iterative process for a lower triangular matrix L.

ForwardSubstitution(LowerTriangularMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.ForwardSubstitution

Construct a ForwardSubstitution instance to solve for different Vector b's.

Frobenius(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.Measure

Compute the Frobenius norm, i.e., the sqrt of the sum of squares of all elements of a matrix.

fromDegree(int) - Static method in enum com.numericalmethod.suanshu.analysis.function.polynomial.root.Solver.Type

fromPolar(double, double) - Static method in class com.numericalmethod.suanshu.number.complex.Complex

Factory method to construct a complex number from the polar form: (r, θ).

fromVarx(VarxModel) - Static method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.VecmTransitory

Construct a transitory VECM(p) from a VARX(p).

Ft - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde

This represents the concept 'Filtration', the information available at time t.

Ft() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft

Construct an empty filtration (no information).

Ft(Ft) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft

Copy constructor.

FT - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction

the filtration, containing all histories

ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction

Compute all values at all time points.

ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDB

ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDt

ft() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator

Get an array of function values.

Ft - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde

This represents the concept 'Filtration', the information available at time t.

Ft() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft

Construct an empty filtration (no information).

Ft(Ft) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft

Copy constructor.

FtAdaptedFunction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde

This represents a F_t-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.

FtAdaptedRealFunction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde

This represents a real-valued F_t-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.

FtAdaptedVectorFunction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde

This represents a vector-valued F_t-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.

FtWt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde

This is a filtration implementation that includes the path-dependent information, e.g., W_t.

FtWt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt

Construct an empty filtration (no information).

FtWt(FtWt) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt

Copy constructor.

FtWt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde

This is a filtration implementation that includes the path-dependent information, e.g., W_t.

FtWt() - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt

Construct an empty filtration (no information).

FtWt(FtWt) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt

Copy constructor.

Function - Interface in com.numericalmethod.suanshu.analysis.function

The mathematical concept of a function expresses the idea that one quantity (the argument of the function, also known as the input) completely determines another quantity (the value, or output).

Function.EvaluationException - Exception in com.numericalmethod.suanshu.analysis.function

RuntimeException thrown when it fails to evaluate an expression.

Function.EvaluationException(String) - Constructor for exception com.numericalmethod.suanshu.analysis.function.Function.EvaluationException

FunctionOps - Class in com.numericalmethod.suanshu.analysis.function

This class collects some commonly used mathematical functions.

FunctionOps() - Constructor for class com.numericalmethod.suanshu.analysis.function.FunctionOps

fx - Variable in exception com.numericalmethod.suanshu.analysis.uniroot.Uniroot.NoRootFoundException

f(x)