Package org.apache.commons.math.optimization

Class MultiStartDifferentiableMultivariateRealOptimizer

java.lang.Object

org.apache.commons.math.optimization.MultiStartDifferentiableMultivariateRealOptimizer

All Implemented Interfaces:

DifferentiableMultivariateRealOptimizer

public class MultiStartDifferentiableMultivariateRealOptimizer
extends java.lang.Object
implements DifferentiableMultivariateRealOptimizer

Special implementation of the DifferentiableMultivariateRealOptimizer interface adding multi-start features to an existing optimizer.

This class wraps a classical optimizer to use it several times in turn with different starting points in order to avoid being trapped into a local extremum when looking for a global one.

Since:

2.0

Version:

$Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $

Constructor Summary

Constructors

Constructor	Description
MultiStartDifferentiableMultivariateRealOptimizer(DifferentiableMultivariateRealOptimizer optimizer, int starts, RandomVectorGenerator generator)	Create a multi-start optimizer from a single-start optimizer

Method Summary

Modifier and Type	Method	Description
RealConvergenceChecker	getConvergenceChecker()	Get the convergence checker.
int	getEvaluations()	Get the number of evaluations of the objective function.
int	getGradientEvaluations()	Get the number of evaluations of the objective function gradient.
int	getIterations()	Get the number of iterations realized by the algorithm.
int	getMaxEvaluations()	Get the maximal number of functions evaluations.
int	getMaxIterations()	Get the maximal number of iterations of the algorithm.
RealPointValuePair[]	getOptima()	Get all the optima found during the last call to optimize.
RealPointValuePair	optimize(DifferentiableMultivariateRealFunction f, GoalType goalType, double[] startPoint)	Optimizes an objective function.
void	setConvergenceChecker(RealConvergenceChecker checker)	Set the convergence checker.
void	setMaxEvaluations(int maxEvaluations)	Set the maximal number of functions evaluations.
void	setMaxIterations(int maxIterations)	Set the maximal number of iterations of the algorithm.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

MultiStartDifferentiableMultivariateRealOptimizer

public MultiStartDifferentiableMultivariateRealOptimizer(DifferentiableMultivariateRealOptimizer optimizer,
                                                         int starts,
                                                         RandomVectorGenerator generator)

Create a multi-start optimizer from a single-start optimizer

Parameters:

optimizer - single-start optimizer to wrap

starts - number of starts to perform (including the first one), multi-start is disabled if value is less than or equal to 1

generator - random vector generator to use for restarts

Method Detail

getOptima

public RealPointValuePair[] getOptima()
                               throws java.lang.IllegalStateException

Get all the optima found during the last call to optimize.

The optimizer stores all the optima found during a set of restarts. The optimize method returns the best point only. This method returns all the points found at the end of each starts, including the best one already returned by the optimize method.

The returned array as one element for each start as specified in the constructor. It is ordered with the results from the runs that did converge first, sorted from best to worst objective value (i.e in ascending order if minimizing and in descending order if maximizing), followed by and null elements corresponding to the runs that did not converge. This means all elements will be null if the optimize method did throw a ConvergenceException). This also means that if the first element is non null, it is the best point found across all starts.

Returns:

array containing the optima

Throws:

java.lang.IllegalStateException - if optimize has not been called

setMaxIterations

public void setMaxIterations(int maxIterations)

Set the maximal number of iterations of the algorithm.

Specified by:

setMaxIterations in interface DifferentiableMultivariateRealOptimizer

Parameters:

maxIterations - maximal number of function calls

getMaxIterations

public int getMaxIterations()

Get the maximal number of iterations of the algorithm.

Specified by:

getMaxIterations in interface DifferentiableMultivariateRealOptimizer

Returns:

maximal number of iterations

getIterations

public int getIterations()

Get the number of iterations realized by the algorithm.

The number of evaluations corresponds to the last call to the optimize method. It is 0 if the method has not been called yet.

Specified by:

getIterations in interface DifferentiableMultivariateRealOptimizer

Returns:

number of iterations

setMaxEvaluations

public void setMaxEvaluations(int maxEvaluations)

Set the maximal number of functions evaluations.

Specified by:

setMaxEvaluations in interface DifferentiableMultivariateRealOptimizer

Parameters:

maxEvaluations - maximal number of function evaluations

getMaxEvaluations

public int getMaxEvaluations()

Get the maximal number of functions evaluations.

Specified by:

getMaxEvaluations in interface DifferentiableMultivariateRealOptimizer

Returns:

maximal number of functions evaluations

getEvaluations

public int getEvaluations()

Get the number of evaluations of the objective function.

The number of evaluations corresponds to the last call to the optimize method. It is 0 if the method has not been called yet.

Specified by:

getEvaluations in interface DifferentiableMultivariateRealOptimizer

Returns:

number of evaluations of the objective function

getGradientEvaluations

public int getGradientEvaluations()

Get the number of evaluations of the objective function gradient.

The number of evaluations corresponds to the last call to the optimize method. It is 0 if the method has not been called yet.

Specified by:

getGradientEvaluations in interface DifferentiableMultivariateRealOptimizer

Returns:

number of evaluations of the objective function gradient

setConvergenceChecker

public void setConvergenceChecker(RealConvergenceChecker checker)

Set the convergence checker.

Specified by:

setConvergenceChecker in interface DifferentiableMultivariateRealOptimizer

Parameters:

checker - object to use to check for convergence

getConvergenceChecker

public RealConvergenceChecker getConvergenceChecker()

Get the convergence checker.

Specified by:

getConvergenceChecker in interface DifferentiableMultivariateRealOptimizer

Returns:

object used to check for convergence

optimize

public RealPointValuePair optimize(DifferentiableMultivariateRealFunction f,
                                   GoalType goalType,
                                   double[] startPoint)
                            throws FunctionEvaluationException,
                                   OptimizationException,
                                   FunctionEvaluationException

Optimizes an objective function.

Specified by:

optimize in interface DifferentiableMultivariateRealOptimizer

Parameters:

f - objective function

goalType - type of optimization goal: either GoalType.MAXIMIZE or GoalType.MINIMIZE

startPoint - the start point for optimization

Returns:

the point/value pair giving the optimal value for objective function

Throws:

FunctionEvaluationException - if the objective function throws one during the search

OptimizationException - if the algorithm failed to converge