org.apache.commons.math.optimization.general
Class AbstractLeastSquaresOptimizer

java.lang.Object
  org.apache.commons.math.optimization.general.AbstractLeastSquaresOptimizer

All Implemented Interfaces:

DifferentiableMultivariateVectorialOptimizer

Direct Known Subclasses:

GaussNewtonOptimizer, LevenbergMarquardtOptimizer

public abstract class AbstractLeastSquaresOptimizer
extends Object
implements DifferentiableMultivariateVectorialOptimizer

Base class for implementing least squares optimizers.

This base class handles the boilerplate methods associated to thresholds settings, jacobian and error estimation.

Since:

1.2

Field Summary

static int

DEFAULT_MAX_ITERATIONS Default maximal number of iterations allowed.

Method Summary

double	getChiSquare() Get a Chi-Square-like value assuming the N residuals follow N distinct normal distributions centered on 0 and whose variances are the reciprocal of the weights.
VectorialConvergenceChecker	getConvergenceChecker() Get the convergence checker.
double[][]	getCovariances() Get the covariance matrix of optimized parameters.
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.
double	getRMS() Get the Root Mean Square value.
double[]	guessParametersErrors() Guess the errors in optimized parameters.
VectorialPointValuePair	optimize(DifferentiableMultivariateVectorialFunction f, double[] target, double[] weights, double[] startPoint) Optimizes an objective function.
void	setConvergenceChecker(VectorialConvergenceChecker convergenceChecker) 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

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

Field Detail

DEFAULT_MAX_ITERATIONS

public static final int DEFAULT_MAX_ITERATIONS

Default maximal number of iterations allowed.

See Also:

Constant Field Values

Method Detail

setMaxIterations

public void setMaxIterations(int maxIterations)

Set the maximal number of iterations of the algorithm.

Specified by:

setMaxIterations in interface DifferentiableMultivariateVectorialOptimizer

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 DifferentiableMultivariateVectorialOptimizer

Returns:

maximal number of iterations

getIterations

public int getIterations()

Get the number of iterations realized by the algorithm.

Specified by:

getIterations in interface DifferentiableMultivariateVectorialOptimizer

Returns:

number of iterations

setMaxEvaluations

public void setMaxEvaluations(int maxEvaluations)

Set the maximal number of functions evaluations.

Specified by:

setMaxEvaluations in interface DifferentiableMultivariateVectorialOptimizer

Parameters:

maxEvaluations - maximal number of function evaluations

getMaxEvaluations

public int getMaxEvaluations()

Get the maximal number of functions evaluations.

Specified by:

getMaxEvaluations in interface DifferentiableMultivariateVectorialOptimizer

Returns:

maximal number of functions evaluations

getEvaluations

public 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.

Specified by:

getEvaluations in interface DifferentiableMultivariateVectorialOptimizer

Returns:

number of evaluations of the objective function

getJacobianEvaluations

public 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.

Specified by:

getJacobianEvaluations in interface DifferentiableMultivariateVectorialOptimizer

Returns:

number of evaluations of the objective function jacobian

setConvergenceChecker

public void setConvergenceChecker(VectorialConvergenceChecker convergenceChecker)

Set the convergence checker.

Specified by:

setConvergenceChecker in interface DifferentiableMultivariateVectorialOptimizer

Parameters:

convergenceChecker - object to use to check for convergence

getConvergenceChecker

public VectorialConvergenceChecker getConvergenceChecker()

Get the convergence checker.

Specified by:

getConvergenceChecker in interface DifferentiableMultivariateVectorialOptimizer

Returns:

object used to check for convergence

getRMS

public double getRMS()

Get the Root Mean Square value. Get the Root Mean Square value, i.e. the root of the arithmetic mean of the square of all weighted residuals. This is related to the criterion that is minimized by the optimizer as follows: if c if the criterion, and n is the number of measurements, then the RMS is sqrt (c/n).

Returns:

RMS value

getChiSquare

public double getChiSquare()

Get a Chi-Square-like value assuming the N residuals follow N distinct normal distributions centered on 0 and whose variances are the reciprocal of the weights.

Returns:

chi-square value

getCovariances

public double[][] getCovariances()
                          throws FunctionEvaluationException,
                                 OptimizationException

Get the covariance matrix of optimized parameters.

Returns:

covariance matrix

Throws:

FunctionEvaluationException - if the function jacobian cannot be evaluated

OptimizationException - if the covariance matrix cannot be computed (singular problem)

guessParametersErrors

public double[] guessParametersErrors()
                               throws FunctionEvaluationException,
                                      OptimizationException

Guess the errors in optimized parameters.

Guessing is covariance-based, it only gives rough order of magnitude.

Returns:

errors in optimized parameters

Throws:

FunctionEvaluationException - if the function jacobian cannot b evaluated

OptimizationException - if the covariances matrix cannot be computed or the number of degrees of freedom is not positive (number of measurements lesser or equal to number of parameters)

optimize

public 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

Specified by:

optimize in interface DifferentiableMultivariateVectorialOptimizer

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