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LevenbergMarquardtMinimizer.h

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// LevenbergMarquardtMinimizer.h: interface for the CLevenbergMarquardtMinimizer class.
//

#if !defined(AFX_LEVENBERGMARQUARDTMINIMIZER_H__B872BB8D_BEEB_412D_BDC2_43D6F067FD89__INCLUDED_)
#define AFX_LEVENBERGMARQUARDTMINIMIZER_H__B872BB8D_BEEB_412D_BDC2_43D6F067FD89__INCLUDED_

#if _MSC_VER > 1000
#pragma once
#endif // _MSC_VER > 1000

#ifdef _WIN32
#include <mkl.h>
#else
#include "lapack.h"
#endif

#include <iostream>
#include <fstream>
#include <cmath>
#include <cfloat>
#include <assert.h>
#include "LeastSquaresMinimizer.h"
#include "NLLSMinimizable.h"
#include "ParameterFilter.h"
using namespace std;

class CLevenbergMarquardtMinimizer : public CLeastSquaresMinimizer  
{
public:
        CLevenbergMarquardtMinimizer();
        virtual ~CLevenbergMarquardtMinimizer();
        // Adds the marquardt parameter to the diagonal
        void RescaleDiagonal(double scaleParameter);
        // Restores the diagonal elements of the Hessian to their saved values
        void RestoreDiagonal();
        // Enforce positive semi-definiteness on the parameters
        void CheckBounds(double *parameters);
        // Obtain the trial parameters from the current parameters and the change
        void ObtainTrialParameters(double *currentP, double *deltaP, double *trialP);
        // Accepts trial parameters as the new parameters
        void AcceptParameters(double *parameters, double *trialP, double newCost);
        // The next three functions return true if the values are below their respective
        // tolerances
        // Computes the fractional cost tolerance
        bool ComputeChiSqTol(double oldCost, double newCost);
        // Computes the gradient tolerance
        bool ComputeGradTol();
        // Computes the parameter tolerance
        bool ComputeParTol(double *oldP, double *deltaP);
//private:
        bool m_bCheckChi;                               // Set to true if you want to use fractional
                                                                        //    change in chi^2 as a convergence crit.
        bool m_bCheckGrad;                              // Set to true if you want to use the 2-norm
                                                                        //    of the gradient as a convergence crit.
        bool m_bCheckPar;                               // Set to true if you want to use parameter 
                                                                        //    norms as a convergence crit.
        bool m_bPositiveSemiDef;                // Set to true if all parameters >= 0.  If you
                                                                        //    do so, you must use the identity filter
        double m_dLambda;                               // Marquardt parameter
        double m_dNu;                                   // Divisor of MarquardtParameter
        int m_iNIterations;                             // Maximum number of LM iterations
        double m_dChiSqTol;                             // Fractional stopping criterion for Chi squared
        double m_dGradTol;                              // Stopping criterion for the norm of the gradient
        double m_dParTol;                               // Stopping criterion based on deltaP
        double m_dTau;                                  // Small number
};

#endif // !defined(AFX_LEVENBERGMARQUARDTMINIMIZER_H__B872BB8D_BEEB_412D_BDC2_43D6F067FD89__INCLUDED_)

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