Uses of Interface
pal.math.MultivariateFunction
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Packages that use MultivariateFunction Package Description pal.eval Classes for evaluating evolutionary hypothesis (chi-square and likelihood criteria) and estimating model parameters.pal.math Classes for math stuff such as optimisation, numerical derivatives, matrix exponentials, random numbers, special function etc.pal.misc Classes that don't fit elsewhere ;^) -
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Uses of MultivariateFunction in pal.eval
Classes in pal.eval that implement MultivariateFunction Modifier and Type Class Description class
ChiSquareValue
computes chi-square value of a (parameterized) tree for its set of parameters (e.g., branch lengths) and a given distance matrixclass
DemographicValue
estimates demographic parameters by maximising the coalescent prior for a tree with given branch lengths.class
ModelParameters
estimates substitution model parameters from the data -
Uses of MultivariateFunction in pal.math
Subinterfaces of MultivariateFunction in pal.math Modifier and Type Interface Description interface
MFWithGradient
interface for a function of several variables with a gradientClasses in pal.math that implement MultivariateFunction Modifier and Type Class Description class
BoundsCheckedFunction
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
EvaluationCounter
A utiltity class that can be used to track the number of evaluations of a general functionMethods in pal.math with parameters of type MultivariateFunction Modifier and Type Method Description static 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 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 MultivariateFunction Constructor Description BoundsCheckedFunction(MultivariateFunction func)
construct 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 functionEvaluationCounter(MultivariateFunction base)
LineFunction(MultivariateFunction func)
construct univariate function from multivariate functionOrthogonalLineFunction(MultivariateFunction func)
construct 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 MultivariateFunction Modifier and Type Method Description static 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 MultivariateFunction Modifier and Type Method Description static MultivariateFunction
Utils. combineMultivariateFunction(MultivariateFunction base, Parameterized[] additionalParameters)
Creates an interface between a parameterised object to allow it to act as a multivariate minimum.
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