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 TypeClassDescriptionclass
computes chi-square value of a (parameterized) tree for its set of parameters (e.g., branch lengths) and a given distance matrixclass
estimates demographic parameters by maximising the coalescent prior for a tree with given branch lengths.class
estimates substitution model parameters from the data -
Uses of MultivariateFunction in pal.math
Subinterfaces of MultivariateFunction in pal.mathModifier and TypeInterfaceDescriptioninterface
interface for a function of several variables with a gradientClasses in pal.math that implement MultivariateFunctionModifier and TypeClassDescriptionclass
returns 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)class
A 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 Hessiandouble
MultivariateMinimum.findMinimum
(MultivariateFunction f, double[] xvec) Find minimum close to vector xdouble
MultivariateMinimum.findMinimum
(MultivariateFunction f, double[] xvec, int fxFracDigits, int xFracDigits) Find minimum close to vector x (desired fractional digits for each parameter is specified)double
MultivariateMinimum.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.RoundOptimiser
OrthogonalSearch.generateOrthogonalRoundOptimiser
(MultivariateFunction mf) static final double[]
MathUtils.getRandomArguments
(MultivariateFunction mf) static double[]
NumericalDerivative.gradient
(MultivariateFunction f, double[] x) determine gradientstatic void
NumericalDerivative.gradient
(MultivariateFunction f, double[] x, double[] grad) determine gradientvoid
MinimiserMonitor.newMinimum
(double value, double[] parameterValues, MultivariateFunction beingOptimized) Inform monitor of a new minimum, along with the current arguments.void
ConjugateDirectionSearch.optimize
(MultivariateFunction f, double[] xvector, double tolfx, double tolx) void
ConjugateDirectionSearch.optimize
(MultivariateFunction f, double[] xvector, double tolfx, double tolx, MinimiserMonitor monitor) void
ConjugateGradientSearch.optimize
(MultivariateFunction f, double[] x, double tolfx, double tolx) void
ConjugateGradientSearch.optimize
(MultivariateFunction f, double[] x, double tolfx, double tolx, MinimiserMonitor monitor) void
DifferentialEvolution.optimize
(MultivariateFunction func, double[] xvec, double tolfx, double tolx) void
DifferentialEvolution.optimize
(MultivariateFunction func, double[] xvec, double tolfx, double tolx, MinimiserMonitor monitor) void
GeneralizedDEOptimizer.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.void
GeneralizedDEOptimizer.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 void
MultivariateMinimum.optimize
(MultivariateFunction f, double[] xvec, double tolfx, double tolx) The actual optimization routine (needs to be implemented in a subclass of MultivariateMinimum).void
MultivariateMinimum.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.void
OrthogonalSearch.optimize
(MultivariateFunction f, double[] xvec, double tolfx, double tolx) void
OrthogonalSearch.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 MultivariateFunction
Utils.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 MultivariateFunction
Utils.combineMultivariateFunction
(MultivariateFunction base, Parameterized[] additionalParameters) Creates an interface between a parameterised object to allow it to act as a multivariate minimum.