Uses of Interface
jebl.math.MultivariateFunction

Packages that use MultivariateFunction

jebl.evolution.coalescent
jebl.math

Uses of MultivariateFunction in jebl.evolution.coalescent

Classes in jebl.evolution.coalescent that implement MultivariateFunction

class

Coalescent A likelihood function for the coalescent.

Uses of MultivariateFunction in jebl.math

Methods in jebl.math with parameters of type MultivariateFunction

static double[]	NumericalDerivative.diagonalHessian(MultivariateFunction f, double[] x) determine diagonal of Hessian
double	MultivariateMinimum.findMinimum(MultivariateFunction f, double[] xvec) Find minimum close to vector x
double	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[]	NumericalDerivative.gradient(MultivariateFunction f, double[] x) determine gradient
static void	NumericalDerivative.gradient(MultivariateFunction f, double[] x, double[] grad) determine gradient
void	MinimiserMonitor.newMinimum(double value, double[] parameterValues, MultivariateFunction beingOptimized) Inform monitor of a new minimum, along with the current arguments.
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	OrthogonalSearch.optimize(MultivariateFunction f, double[] xvec, double tolfx, double tolx)
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, MinimiserMonitor monitor)

Constructors in jebl.math with parameters of type MultivariateFunction

OrthogonalLineFunction(MultivariateFunction func)
construct univariate function from multivariate function

OrthogonalLineFunction(MultivariateFunction func, int selectedDimension, double[] initialArguments)
construct univariate function from multivariate function