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

All Implemented Interfaces:

DifferentiableMultivariateVectorialOptimizer

Direct Known Subclasses:

GaussNewtonOptimizer, LevenbergMarquardtOptimizer
```
public abstract class AbstractLeastSquaresOptimizer
extends java.lang.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

Version:

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

Field Summary

Fields
Modifier and Type	Field	Description
`protected VectorialConvergenceChecker`	`checker`	Convergence checker.
`protected int`	`cols`	Number of columns of the jacobian matrix.
`protected double`	`cost`	Cost value (square root of the sum of the residuals).
`static int`	`DEFAULT_MAX_ITERATIONS`	Default maximal number of iterations allowed.
`protected double[][]`	`jacobian`	Jacobian matrix.
`protected double[]`	`objective`	Current objective function value.
`protected double[]`	`point`	Current point.
`protected double[]`	`residuals`	Current residuals.
`protected double[]`	`residualsWeights`	Weight for the least squares cost computation.
`protected int`	`rows`	Number of rows of the jacobian matrix.
`protected double[]`	`targetValues`	Target value for the objective functions at optimum.
`protected double[][]`	`wjacobian`	Weighted Jacobian
`protected double[]`	`wresiduals`	Weighted residuals

Constructor Summary

Constructors
Modifier Constructor Description

protected AbstractLeastSquaresOptimizer()
Simple constructor with default settings.

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method	Description
`protected abstract VectorialPointValuePair`	`doOptimize()`	Perform the bulk of optimization algorithm.
`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.
`protected void`	`incrementIterationsCounter()`	Increment the iterations counter by 1.
`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.
`protected void`	`updateJacobian()`	Update the jacobian matrix.
`protected void`	`updateResidualsAndCost()`	Update the residuals array and cost function value.

Methods inherited from class java.lang.Object
clone, equals, finalize, 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
  - checker
```
protected VectorialConvergenceChecker checker
```
    Convergence checker.
  - jacobian
```
protected double[][] jacobian
```
    Jacobian matrix.
    This matrix is in canonical form just after the calls to updateJacobian(), but may be modified by the solver in the derived class (the Levenberg-Marquardt optimizer does this).
  - cols
```
protected int cols
```
    Number of columns of the jacobian matrix.
  - rows
```
protected int rows
```
    Number of rows of the jacobian matrix.
  - targetValues
```
protected double[] targetValues
```
    Target value for the objective functions at optimum.
    
    Since:
    
    2.1
  - residualsWeights
```
protected double[] residualsWeights
```
    Weight for the least squares cost computation.
    
    Since:
    
    2.1
  - point
```
protected double[] point
```
    Current point.
  - objective
```
protected double[] objective
```
    Current objective function value.
  - residuals
```
protected double[] residuals
```
    Current residuals.
  - wjacobian
```
protected double[][] wjacobian
```
    Weighted Jacobian
  - wresiduals
```
protected double[] wresiduals
```
    Weighted residuals
  - cost
```
protected double cost
```
    Cost value (square root of the sum of the residuals).
- Constructor Detail
  - AbstractLeastSquaresOptimizer
```
protected AbstractLeastSquaresOptimizer()
```
    Simple constructor with default settings.
    The convergence check is set to a SimpleVectorialValueChecker and the maximal number of evaluation is set to its default value.
- 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
  - incrementIterationsCounter
```
protected void incrementIterationsCounter()
                                   throws OptimizationException
```
    Increment the iterations counter by 1.
    
    Throws:
    
    OptimizationException - if the maximal number of iterations is exceeded
  - updateJacobian
```
protected void updateJacobian()
                       throws FunctionEvaluationException
```
    Update the jacobian matrix.
    
    Throws:
    
    FunctionEvaluationException - if the function jacobian cannot be evaluated or its dimension doesn't match problem dimension
  - updateResidualsAndCost
```
protected void updateResidualsAndCost()
                               throws FunctionEvaluationException
```
    Update the residuals array and cost function value.
    
    Throws:
    
    FunctionEvaluationException - if the function cannot be evaluated or its dimension doesn't match problem dimension or maximal number of of evaluations is exceeded
  - 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,
                                        java.lang.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
    
    java.lang.IllegalArgumentException - if the start point dimension is wrong
  - doOptimize
```
protected abstract VectorialPointValuePair doOptimize()
                                               throws FunctionEvaluationException,
                                                      OptimizationException,
                                                      java.lang.IllegalArgumentException
```
    Perform the bulk of optimization algorithm.
    
    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
    
    java.lang.IllegalArgumentException - if the start point dimension is wrong

Class AbstractLeastSquaresOptimizer

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_MAX_ITERATIONS

checker

jacobian

cols

rows

targetValues

residualsWeights

point

objective

residuals

wjacobian

wresiduals

cost

Constructor Detail

AbstractLeastSquaresOptimizer

Method Detail

setMaxIterations

getMaxIterations

getIterations

setMaxEvaluations

getMaxEvaluations

getEvaluations

getJacobianEvaluations

setConvergenceChecker

getConvergenceChecker

incrementIterationsCounter

updateJacobian

updateResidualsAndCost

getRMS

getChiSquare

getCovariances

guessParametersErrors

optimize

doOptimize