Uses of Interface
pal.math.MultivariateFunction
Packages that use MultivariateFunction
Package
Description
Classes for evaluating evolutionary hypothesis (chi-square and likelihood
criteria) and estimating model parameters.
Classes for math stuff such as optimisation, numerical derivatives, matrix exponentials,
random numbers, special function etc.
Classes that don't fit elsewhere ;^)
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Uses of MultivariateFunction in pal.eval
Classes in pal.eval that implement MultivariateFunctionModifier and TypeClassDescriptionclasscomputes chi-square value of a (parameterized) tree for its set of parameters (e.g., branch lengths) and a given distance matrixclassestimates demographic parameters by maximising the coalescent prior for a tree with given branch lengths.classestimates substitution model parameters from the data -
Uses of MultivariateFunction in pal.math
Subinterfaces of MultivariateFunction in pal.mathModifier and TypeInterfaceDescriptioninterfaceinterface for a function of several variables with a gradientClasses in pal.math that implement MultivariateFunctionModifier and TypeClassDescriptionclassreturns a very large number instead of the function value if arguments are out of bound (useful for minimization with minimizers that don't check argument boundaries)classA utiltity class that can be used to track the number of evaluations of a general functionMethods in pal.math with parameters of type MultivariateFunctionModifier and TypeMethodDescriptionstatic double[]NumericalDerivative.diagonalHessian(MultivariateFunction f, double[] x) determine diagonal of HessiandoubleMultivariateMinimum.findMinimum(MultivariateFunction f, double[] xvec) Find minimum close to vector xdoubleMultivariateMinimum.findMinimum(MultivariateFunction f, double[] xvec, int fxFracDigits, int xFracDigits) Find minimum close to vector x (desired fractional digits for each parameter is specified)doubleMultivariateMinimum.findMinimum(MultivariateFunction f, double[] xvec, int fxFracDigits, int xFracDigits, MinimiserMonitor monitor) Find minimum close to vector x (desired fractional digits for each parameter is specified)protected OrthogonalSearch.RoundOptimiserOrthogonalSearch.generateOrthogonalRoundOptimiser(MultivariateFunction mf) static final double[]MathUtils.getRandomArguments(MultivariateFunction mf) static double[]NumericalDerivative.gradient(MultivariateFunction f, double[] x) determine gradientstatic voidNumericalDerivative.gradient(MultivariateFunction f, double[] x, double[] grad) determine gradientvoidMinimiserMonitor.newMinimum(double value, double[] parameterValues, MultivariateFunction beingOptimized) Inform monitor of a new minimum, along with the current arguments.voidConjugateDirectionSearch.optimize(MultivariateFunction f, double[] xvector, double tolfx, double tolx) voidConjugateDirectionSearch.optimize(MultivariateFunction f, double[] xvector, double tolfx, double tolx, MinimiserMonitor monitor) voidConjugateGradientSearch.optimize(MultivariateFunction f, double[] x, double tolfx, double tolx) voidConjugateGradientSearch.optimize(MultivariateFunction f, double[] x, double tolfx, double tolx, MinimiserMonitor monitor) voidDifferentialEvolution.optimize(MultivariateFunction func, double[] xvec, double tolfx, double tolx) voidDifferentialEvolution.optimize(MultivariateFunction func, double[] xvec, double tolfx, double tolx, MinimiserMonitor monitor) voidGeneralizedDEOptimizer.optimize(MultivariateFunction f, double[] xvec, double tolfx, double tolx) The actual optimization routine It finds a minimum close to vector x when the absolute tolerance for each parameter is specified.voidGeneralizedDEOptimizer.optimize(MultivariateFunction f, double[] xvec, double tolfx, double tolx, MinimiserMonitor monitor) The actual optimization routine It finds a minimum close to vector x when the absolute tolerance for each parameter is specified.abstract voidMultivariateMinimum.optimize(MultivariateFunction f, double[] xvec, double tolfx, double tolx) The actual optimization routine (needs to be implemented in a subclass of MultivariateMinimum).voidMultivariateMinimum.optimize(MultivariateFunction f, double[] xvec, double tolfx, double tolx, MinimiserMonitor monitor) The actual optimization routine It finds a minimum close to vector x when the absolute tolerance for each parameter is specified.voidOrthogonalSearch.optimize(MultivariateFunction f, double[] xvec, double tolfx, double tolx) voidOrthogonalSearch.optimize(MultivariateFunction f, double[] xvec, double tolfx, double tolx, MinimiserMonitor monitor) Constructors in pal.math with parameters of type MultivariateFunctionModifierConstructorDescriptionconstruct bound-checked multivariate function (a large number will be returned on function evaluation if argument is out of bounds; default is 1000000)BoundsCheckedFunction(MultivariateFunction func, double largeNumber) construct constrained multivariate functionconstruct univariate function from multivariate functionconstruct univariate function from multivariate functionOrthogonalLineFunction(MultivariateFunction func, int selectedDimension, double[] initialArguments) construct univariate function from multivariate function -
Uses of MultivariateFunction in pal.misc
Methods in pal.misc that return MultivariateFunctionModifier and TypeMethodDescriptionstatic final MultivariateFunctionUtils.combineMultivariateFunction(MultivariateFunction base, Parameterized[] additionalParameters) Creates an interface between a parameterised object to allow it to act as a multivariate minimum.Methods in pal.misc with parameters of type MultivariateFunctionModifier and TypeMethodDescriptionstatic final MultivariateFunctionUtils.combineMultivariateFunction(MultivariateFunction base, Parameterized[] additionalParameters) Creates an interface between a parameterised object to allow it to act as a multivariate minimum.