org.apache.commons.math.estimation
Class LevenbergMarquardtEstimator

java.lang.Object
  extended by org.apache.commons.math.estimation.AbstractEstimator
      extended by org.apache.commons.math.estimation.LevenbergMarquardtEstimator
All Implemented Interfaces:
Serializable, Estimator

Deprecated. as of 2.0, everything in package org.apache.commons.math.estimation has been deprecated and replaced by package org.apache.commons.math.optimization.general

@Deprecated
public class LevenbergMarquardtEstimator
extends AbstractEstimator
implements Serializable

This class solves a least squares problem.

This implementation should work even for over-determined systems (i.e. systems having more variables than equations). Over-determined systems are solved by ignoring the variables which have the smallest impact according to their jacobian column norm. Only the rank of the matrix and some loop bounds are changed to implement this.

The resolution engine is a simple translation of the MINPACK lmder routine with minor changes. The changes include the over-determined resolution and the Q.R. decomposition which has been rewritten following the algorithm described in the P. Lascaux and R. Theodor book Analyse numérique matricielle appliquée à l'art de l'ingénieur, Masson 1986.

The authors of the original fortran version are:

The redistribution policy for MINPACK is available here, for convenience, it is reproduced below.

Minpack Copyright Notice (1999) University of Chicago. All rights reserved
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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  4. WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4) DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL BE CORRECTED.
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    Since:
    1.2
    Version:
    $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $
    See Also:
    Serialized Form

    Field Summary
     
    Fields inherited from class org.apache.commons.math.estimation.AbstractEstimator
    cols, cost, DEFAULT_MAX_COST_EVALUATIONS, jacobian, measurements, parameters, residuals, rows
     
    Constructor Summary
    LevenbergMarquardtEstimator()
              Deprecated. Build an estimator for least squares problems.
     
    Method Summary
     void estimate(EstimationProblem problem)
              Deprecated. Solve an estimation problem using the Levenberg-Marquardt algorithm.
     void setCostRelativeTolerance(double costRelativeTolerance)
              Deprecated. Set the desired relative error in the sum of squares.
     void setInitialStepBoundFactor(double initialStepBoundFactor)
              Deprecated. Set the positive input variable used in determining the initial step bound.
     void setOrthoTolerance(double orthoTolerance)
              Deprecated. Set the desired max cosine on the orthogonality.
     void setParRelativeTolerance(double parRelativeTolerance)
              Deprecated. Set the desired relative error in the approximate solution parameters.
     
    Methods inherited from class org.apache.commons.math.estimation.AbstractEstimator
    getChiSquare, getCostEvaluations, getCovariances, getJacobianEvaluations, getRMS, guessParametersErrors, incrementJacobianEvaluationsCounter, initializeEstimate, setMaxCostEval, updateJacobian, updateResidualsAndCost
     
    Methods inherited from class java.lang.Object
    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
     

    Constructor Detail

    LevenbergMarquardtEstimator

    public LevenbergMarquardtEstimator()
    Deprecated. 
    Build an estimator for least squares problems.

    The default values for the algorithm settings are:

    Method Detail

    setInitialStepBoundFactor

    public void setInitialStepBoundFactor(double initialStepBoundFactor)
    Deprecated. 
    Set the positive input variable used in determining the initial step bound. This bound is set to the product of initialStepBoundFactor and the euclidean norm of diag*x if nonzero, or else to initialStepBoundFactor itself. In most cases factor should lie in the interval (0.1, 100.0). 100.0 is a generally recommended value

    Parameters:
    initialStepBoundFactor - initial step bound factor
    See Also:
    estimate(org.apache.commons.math.estimation.EstimationProblem)

    setCostRelativeTolerance

    public void setCostRelativeTolerance(double costRelativeTolerance)
    Deprecated. 
    Set the desired relative error in the sum of squares.

    Parameters:
    costRelativeTolerance - desired relative error in the sum of squares
    See Also:
    estimate(org.apache.commons.math.estimation.EstimationProblem)

    setParRelativeTolerance

    public void setParRelativeTolerance(double parRelativeTolerance)
    Deprecated. 
    Set the desired relative error in the approximate solution parameters.

    Parameters:
    parRelativeTolerance - desired relative error in the approximate solution parameters
    See Also:
    estimate(org.apache.commons.math.estimation.EstimationProblem)

    setOrthoTolerance

    public void setOrthoTolerance(double orthoTolerance)
    Deprecated. 
    Set the desired max cosine on the orthogonality.

    Parameters:
    orthoTolerance - desired max cosine on the orthogonality between the function vector and the columns of the jacobian
    See Also:
    estimate(org.apache.commons.math.estimation.EstimationProblem)

    estimate

    public void estimate(EstimationProblem problem)
                  throws EstimationException
    Deprecated. 
    Solve an estimation problem using the Levenberg-Marquardt algorithm.

    The algorithm used is a modified Levenberg-Marquardt one, based on the MINPACK lmder routine. The algorithm settings must have been set up before this method is called with the setInitialStepBoundFactor(double), AbstractEstimator.setMaxCostEval(int), setCostRelativeTolerance(double), setParRelativeTolerance(double) and setOrthoTolerance(double) methods. If these methods have not been called, the default values set up by the constructor will be used.

    The authors of the original fortran function are:

    Luc Maisonobe did the Java translation.

    Specified by:
    estimate in interface Estimator
    Specified by:
    estimate in class AbstractEstimator
    Parameters:
    problem - estimation problem to solve
    Throws:
    EstimationException - if convergence cannot be reached with the specified algorithm settings or if there are more variables than equations
    See Also:
    setInitialStepBoundFactor(double), setCostRelativeTolerance(double), setParRelativeTolerance(double), setOrthoTolerance(double)


    Copyright © 2003-2011 Apache Software Foundation. All Rights Reserved.