# Uses of Interface
jebl.math.MultivariateFunction

Packages that use MultivariateFunction
Package
Description
jebl.evolution.coalescent
jebl.math

## Uses of MultivariateFunction in jebl.evolution.coalescent

Classes in jebl.evolution.coalescent that implement MultivariateFunction
Modifier and Type
Class
Description
`class`
`Coalescent`
A likelihood function for the coalescent.

## Uses of MultivariateFunction in jebl.math

Methods in jebl.math with parameters of type MultivariateFunction
Modifier and Type
Method
Description
`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)
`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`
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 jebl.math with parameters of type MultivariateFunction
Modifier
Constructor
Description
``
`OrthogonalLineFunction(MultivariateFunction func)`
construct univariate function from multivariate function
``
`OrthogonalLineFunction(MultivariateFunction func, int selectedDimension, double[] initialArguments)`
construct univariate function from multivariate function