ObjectiveFunction.hpp

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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
 
/*
  Copyright (C) 2001, 2002, 2003 Nicolas Di C�sar�
 
  This file is part of QuantLib, a free-software/open-source library
  for financial quantitative analysts and developers - http://quantlib.org/
 
  QuantLib is free software: you can redistribute it and/or modify it
  under the terms of the QuantLib license.  You should have received a
  copy of the license along with this program; if not, please email
  <quantlib-dev@lists.sf.net>. The license is also available online at
  <http://quantlib.org/license.shtml>.
 
  This program is distributed in the hope that it will be useful, but WITHOUT
  ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
  FOR A PARTICULAR PURPOSE.  See the license for more details.
*/
 
/*! \file ObjectiveFunction.hpp
  \brief Optimization objective function class
*/
 
#ifndef optimization_objectivefunction_h
#define optimization_objectivefunction_h
 
#include <config.h>
 
#include "math/DynamicVector.hpp"
 
using namespace OpenMD;
namespace QuantLib {
 
  //!  Objective function abstract class for optimization problem
  class ObjectiveFunction {
  public:
    virtual ~ObjectiveFunction() {}
    //! method to overload to compute the objective function value in x
    virtual RealType value(const DynamicVector<RealType>& x) = 0;
 
    //! method to overload to compute grad_f, the first derivative of
    //  the objective function with respect to x
    virtual void gradient(DynamicVector<RealType>& grad,
                          const DynamicVector<RealType>& x) {
      RealType eps = finiteDifferenceEpsilon(), fp, fm;
      DynamicVector<RealType> xx(x);
      for (size_t i = 0; i < x.size(); i++) {
        xx[i] += eps;
        fp = value(xx);
        xx[i] -= 2.0 * eps;
        fm      = value(xx);
        grad[i] = 0.5 * (fp - fm) / eps;
        xx[i]   = x[i];
      }
    }
 
    //! method to overload to compute grad_f, the first derivative
    //  of the objective function with respect to x and also the
    //  objective function
    virtual RealType valueAndGradient(DynamicVector<RealType>& grad,
                                      const DynamicVector<RealType>& x) {
      gradient(grad, x);
      return value(x);
    }
 
    //! Default epsilon for finite difference method :
    virtual RealType finiteDifferenceEpsilon() const { return 1e-8; }
  };
 
  class ParametersTransformation {
  public:
    virtual ~ParametersTransformation() {}
    virtual DynamicVector<RealType> direct(
        const DynamicVector<RealType>& x) const = 0;
    virtual DynamicVector<RealType> inverse(
        const DynamicVector<RealType>& x) const = 0;
  };
}  // namespace QuantLib
 
#endif