Uses of Interface
pal.math.MultivariateFunction

Packages that use MultivariateFunction
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 ;^) 
 

Uses of MultivariateFunction in pal.eval
 

Classes in pal.eval that implement MultivariateFunction
 class ChiSquareValue
          computes chi-square value of a (parameterized) tree for its set of parameters (e.g., branch lengths) and a given distance matrix
 class 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
 interface MFWithGradient
          interface for a function of several variables with a gradient
 

Classes in pal.math that implement MultivariateFunction
 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 function
 

Methods in pal.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[] MathUtils.getRandomArguments(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.
 void ConjugateGradientSearch.optimize(MultivariateFunction f, double[] x, double tolfx, double tolx)
           
 void OrthogonalSearch.optimize(MultivariateFunction f, double[] xvec, double tolfx, double tolx)
           
 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 ConjugateDirectionSearch.optimize(MultivariateFunction f, double[] xvector, double tolfx, double tolx)
           
 void DifferentialEvolution.optimize(MultivariateFunction func, double[] xvec, double tolfx, double tolx)
           
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 ConjugateGradientSearch.optimize(MultivariateFunction f, double[] x, double tolfx, double tolx, MinimiserMonitor monitor)
           
 void OrthogonalSearch.optimize(MultivariateFunction f, double[] xvec, double tolfx, double tolx, MinimiserMonitor monitor)
           
 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.
 void ConjugateDirectionSearch.optimize(MultivariateFunction f, double[] xvector, double tolfx, double tolx, MinimiserMonitor monitor)
           
 void DifferentialEvolution.optimize(MultivariateFunction func, double[] xvec, double tolfx, double tolx, MinimiserMonitor monitor)
           
 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.
 

Constructors in pal.math with parameters of type MultivariateFunction
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 function
EvaluationCounter(MultivariateFunction base)
           
LineFunction(MultivariateFunction func)
          construct univariate function from multivariate function
OrthogonalLineFunction(MultivariateFunction func)
          construct univariate function from multivariate function
OrthogonalLineFunction(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
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
static MultivariateFunction Utils.combineMultivariateFunction(MultivariateFunction base, Parameterized[] additionalParameters)
          Creates an interface between a parameterised object to allow it to act as a multivariate minimum.