org.apache.commons.math.optimization
Interface DifferentiableMultivariateVectorialOptimizer

All Known Implementing Classes:

AbstractLeastSquaresOptimizer, GaussNewtonOptimizer, LevenbergMarquardtOptimizer, MultiStartDifferentiableMultivariateVectorialOptimizer

public interface DifferentiableMultivariateVectorialOptimizer

This interface represents an optimization algorithm for vectorial differentiable objective functions.

Optimization algorithms find the input point set that either maximize or minimize an objective function.

Since:

2.0

Version:

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

See Also:

MultivariateRealOptimizer, DifferentiableMultivariateRealOptimizer

Method Summary

VectorialConvergenceChecker	getConvergenceChecker() Get the convergence checker.
int	getEvaluations() Get the number of evaluations of the objective function.
int	getIterations() Get the number of iterations realized by the algorithm.
int	getJacobianEvaluations() Get the number of evaluations of the objective function jacobian .
int	getMaxEvaluations() Get the maximal number of functions evaluations.
int	getMaxIterations() Get the maximal number of iterations of the algorithm.
VectorialPointValuePair	optimize(DifferentiableMultivariateVectorialFunction f, double[] target, double[] weights, double[] startPoint) Optimizes an objective function.
void	setConvergenceChecker(VectorialConvergenceChecker 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.

Method Detail

setMaxIterations

void setMaxIterations(int maxIterations)

Set the maximal number of iterations of the algorithm.

Parameters:

maxIterations - maximal number of function calls .

getMaxIterations

int getMaxIterations()

Get the maximal number of iterations of the algorithm.

Returns:

maximal number of iterations

getIterations

int getIterations()

Get the number of iterations realized by the algorithm.

Returns:

number of iterations

setMaxEvaluations

void setMaxEvaluations(int maxEvaluations)

Set the maximal number of functions evaluations.

Parameters:

maxEvaluations - maximal number of function evaluations

getMaxEvaluations

int getMaxEvaluations()

Get the maximal number of functions evaluations.

Returns:

maximal number of functions evaluations

getEvaluations

int getEvaluations()

Get the number of evaluations of the objective function.

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

Returns:

number of evaluations of the objective function

getJacobianEvaluations

int getJacobianEvaluations()

Get the number of evaluations of the objective function jacobian .

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

Returns:

number of evaluations of the objective function jacobian

setConvergenceChecker

void setConvergenceChecker(VectorialConvergenceChecker checker)

Set the convergence checker.

Parameters:

checker - object to use to check for convergence

getConvergenceChecker

VectorialConvergenceChecker getConvergenceChecker()

Get the convergence checker.

Returns:

object used to check for convergence

optimize

VectorialPointValuePair optimize(DifferentiableMultivariateVectorialFunction f,
                                 double[] target,
                                 double[] weights,
                                 double[] startPoint)
                                 throws FunctionEvaluationException,
                                        OptimizationException,
                                        IllegalArgumentException

Optimizes an objective function.

Optimization is considered to be a weighted least-squares minimization. The cost function to be minimized is ∑weight_i(objective_i-target_i)²

Parameters:

f - objective function

target - target value for the objective functions at optimum

weights - weight for the least squares cost computation

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

IllegalArgumentException - if the start point dimension is wrong