| 1 | /* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */ | 
| 2 |  | 
| 3 | /* | 
| 4 | Copyright (C) 2001, 2002, 2003 Nicolas Di Césaré | 
| 5 |  | 
| 6 | This file is part of QuantLib, a free-software/open-source library | 
| 7 | for financial quantitative analysts and developers - http://quantlib.org/ | 
| 8 |  | 
| 9 | QuantLib is free software: you can redistribute it and/or modify it | 
| 10 | under the terms of the QuantLib license.  You should have received a | 
| 11 | copy of the license along with this program; if not, please email | 
| 12 | <quantlib-dev@lists.sf.net>. The license is also available online at | 
| 13 | <http://quantlib.org/license.shtml>. | 
| 14 |  | 
| 15 | This program is distributed in the hope that it will be useful, but WITHOUT | 
| 16 | ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS | 
| 17 | FOR A PARTICULAR PURPOSE.  See the license for more details. | 
| 18 | */ | 
| 19 |  | 
| 20 | /*! \file ObjectiveFunction.hpp | 
| 21 | \brief Optimization objective function class | 
| 22 | */ | 
| 23 |  | 
| 24 | #ifndef optimization_objectivefunction_h | 
| 25 | #define optimization_objectivefunction_h | 
| 26 | #include "config.h" | 
| 27 | #include "math/DynamicVector.hpp" | 
| 28 |  | 
| 29 | using namespace OpenMD; | 
| 30 | namespace QuantLib { | 
| 31 |  | 
| 32 | //!  Objective function abstract class for optimization problem | 
| 33 | class ObjectiveFunction { | 
| 34 | public: | 
| 35 | virtual ~ObjectiveFunction() {} | 
| 36 | //! method to overload to compute the objective function value in x | 
| 37 | virtual RealType value(const DynamicVector<RealType>& x)  = 0; | 
| 38 |  | 
| 39 | //! method to overload to compute grad_f, the first derivative of | 
| 40 | //  the objective function with respect to x | 
| 41 | virtual void gradient(DynamicVector<RealType>& grad, const DynamicVector<RealType>& x) { | 
| 42 | RealType eps = finiteDifferenceEpsilon(), fp, fm; | 
| 43 | DynamicVector<RealType> xx(x); | 
| 44 | for (size_t i=0; i<x.size(); i++) { | 
| 45 | xx[i] += eps; | 
| 46 | fp = value(xx); | 
| 47 | xx[i] -= 2.0*eps; | 
| 48 | fm = value(xx); | 
| 49 | grad[i] = 0.5*(fp - fm)/eps; | 
| 50 | xx[i] = x[i]; | 
| 51 | } | 
| 52 | } | 
| 53 |  | 
| 54 | //! method to overload to compute grad_f, the first derivative | 
| 55 | //  of the objective function with respect to x and also the | 
| 56 | //  objective function | 
| 57 | virtual RealType valueAndGradient(DynamicVector<RealType>& grad, | 
| 58 | const DynamicVector<RealType>& x) { | 
| 59 | gradient(grad, x); | 
| 60 | return value(x); | 
| 61 | } | 
| 62 |  | 
| 63 | //! Default epsilon for finite difference method : | 
| 64 | virtual RealType finiteDifferenceEpsilon() const { return 1e-8; } | 
| 65 | }; | 
| 66 |  | 
| 67 | class ParametersTransformation { | 
| 68 | public: | 
| 69 | virtual ~ParametersTransformation() {} | 
| 70 | virtual DynamicVector<RealType> direct(const DynamicVector<RealType>& x) const = 0; | 
| 71 | virtual DynamicVector<RealType> inverse(const DynamicVector<RealType>& x) const = 0; | 
| 72 | }; | 
| 73 | } | 
| 74 |  | 
| 75 | #endif |