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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */ |
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|
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/* |
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Copyright (C) 2007 Ferdinando Ametrano |
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Copyright (C) 2007 François du Vignaud |
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Copyright (C) 2001, 2002, 2003 Nicolas Di Césaré |
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|
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This file is part of QuantLib, a free-software/open-source library |
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for financial quantitative analysts and developers - http://quantlib.org/ |
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|
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QuantLib is free software: you can redistribute it and/or modify it |
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under the terms of the QuantLib license. You should have received a |
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copy of the license along with this program; if not, please email |
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<quantlib-dev@lists.sf.net>. The license is also available online at |
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<http://quantlib.org/license.shtml>. |
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|
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This program is distributed in the hope that it will be useful, but WITHOUT |
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ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS |
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FOR A PARTICULAR PURPOSE. See the license for more details. |
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*/ |
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|
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/*! \file problem.hpp |
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\brief Abstract optimization problem class |
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*/ |
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|
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#ifndef quantlib_optimization_problem_h |
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#define quantlib_optimization_problem_h |
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|
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#include "optimization/Method.hpp" |
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#include "optimization/ObjectiveFunction.hpp" |
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#include "optimization/StatusFunction.hpp" |
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|
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namespace QuantLib { |
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class Constraint; |
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//! Constrained optimization problem |
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class Problem { |
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public: |
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//! default constructor |
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Problem(ObjectiveFunction& objectiveFunction, |
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Constraint& constraint, |
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OpenMD::StatusFunction& statFunc, |
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const DynamicVector<RealType>& initialValue = DynamicVector<RealType>()) |
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: objectiveFunction_(objectiveFunction), constraint_(constraint), |
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currentValue_(initialValue), statusFunction_(statFunc) {} |
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|
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/*! \warning it does not reset the current minumum to any initial value |
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*/ |
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void reset(); |
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|
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//! call objective function computation and increment evaluation counter |
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RealType value(const DynamicVector<RealType>& x); |
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|
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//! call objective function gradient computation and increment |
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// evaluation counter |
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void gradient(DynamicVector<RealType>& grad_f, |
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const DynamicVector<RealType>& x); |
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|
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//! call objective function computation and it gradient |
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RealType valueAndGradient(DynamicVector<RealType>& grad_f, |
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const DynamicVector<RealType>& x); |
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|
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//! Constraint |
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Constraint& constraint() const { return constraint_; } |
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|
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//! Objective function |
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ObjectiveFunction& objectiveFunction() const { return objectiveFunction_; } |
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|
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void setCurrentValue(const DynamicVector<RealType>& currentValue) { |
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currentValue_=currentValue; |
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statusFunction_.writeStatus(); |
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} |
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|
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//! current value of the local minimum |
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const DynamicVector<RealType>& currentValue() const { return currentValue_; } |
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|
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void setFunctionValue(RealType functionValue) { |
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functionValue_=functionValue; |
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} |
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//! value of objective function |
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RealType functionValue() const { return functionValue_; } |
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|
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void setGradientNormValue(RealType squaredNorm) { |
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squaredNorm_=squaredNorm; |
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} |
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//! value of objective function gradient norm |
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RealType gradientNormValue() const { return squaredNorm_; } |
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|
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//! number of evaluation of objective function |
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int functionEvaluation() const { return functionEvaluation_; } |
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|
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//! number of evaluation of objective function gradient |
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int gradientEvaluation() const { return gradientEvaluation_; } |
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|
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RealType DotProduct(DynamicVector<RealType>& v1, DynamicVector<RealType>& v2); |
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RealType computeGradientNormValue(DynamicVector<RealType>& grad_f); |
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protected: |
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//! Unconstrained objective function |
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ObjectiveFunction& objectiveFunction_; |
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//! Constraint |
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Constraint& constraint_; |
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//! current value of the local minimum |
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DynamicVector<RealType> currentValue_; |
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//! function and gradient norm values at the curentValue_ (i.e. the last step) |
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RealType functionValue_, squaredNorm_; |
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//! number of evaluation of objective function and its gradient |
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int functionEvaluation_, gradientEvaluation_; |
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//! status function |
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StatusFunction& statusFunction_; |
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|
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}; |
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|
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// inline definitions |
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inline RealType Problem::value(const DynamicVector<RealType>& x) { |
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++functionEvaluation_; |
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return objectiveFunction_.value(x); |
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} |
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|
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inline void Problem::gradient(DynamicVector<RealType>& grad_f, |
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const DynamicVector<RealType>& x) { |
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++gradientEvaluation_; |
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objectiveFunction_.gradient(grad_f, x); |
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} |
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|
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inline RealType Problem::valueAndGradient(DynamicVector<RealType>& grad_f, |
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const DynamicVector<RealType>& x) { |
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++functionEvaluation_; |
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++gradientEvaluation_; |
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return objectiveFunction_.valueAndGradient(grad_f, x); |
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} |
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inline void Problem::reset() { |
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functionEvaluation_ = gradientEvaluation_ = 0; |
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functionValue_ = squaredNorm_ = NULL; |
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} |
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|
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} |
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|
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#endif |
