Logo Search packages:      
Sourcecode: quantlib version File versions  Download package


Go to the documentation of this file.
/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */

 Copyright (C) 2007 Ferdinando Ametrano
 Copyright (C) 2007 François du Vignaud
 Copyright (C) 2001, 2002, 2003 Nicolas Di Césaré

 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

 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 problem.hpp
    \brief Abstract optimization problem class

#ifndef quantlib_optimization_problem_h
#define quantlib_optimization_problem_h

#include <ql/math/optimization/method.hpp>
#include <ql/math/optimization/costfunction.hpp>

namespace QuantLib {

    class Constraint;
    //! Constrained optimization problem
00036     class Problem {
        //! default constructor
00039         Problem(CostFunction& costFunction,
                Constraint& constraint,
                const Array& initialValue = Array())
        : costFunction_(costFunction), constraint_(constraint),
          currentValue_(initialValue) {}

        /*! \warning it does not reset the current minumum to any initial value
        void reset();

        //! call cost function computation and increment evaluation counter
        Real value(const Array& x);

        //! call cost values computation and increment evaluation counter
        Disposable<Array> values(const Array& x);

        //! call cost function gradient computation and increment
        //  evaluation counter
        void gradient(Array& grad_f,
                      const Array& x);

        //! call cost function computation and it gradient
        Real valueAndGradient(Array& grad_f,
                              const Array& x);

        //! Constraint
00065         Constraint& constraint() const { return constraint_; }

        //! Cost function
00068         CostFunction& costFunction() const { return costFunction_; }

        void setCurrentValue(const Array& currentValue) {

        //! current value of the local minimum
00075         const Array& currentValue() const { return currentValue_; }

        void setFunctionValue(Real functionValue) {

        //! value of cost function
00082         Real functionValue() const { return functionValue_; }

        void setGradientNormValue(Real squaredNorm) {
        //! value of cost function gradient norm
00088         Real gradientNormValue() const { return squaredNorm_; }

        //! number of evaluation of cost function
00091         Integer functionEvaluation() const { return functionEvaluation_; }

        //! number of evaluation of cost function gradient
00094         Integer gradientEvaluation() const { return gradientEvaluation_; }

        //! Unconstrained cost function
00098         CostFunction& costFunction_;
        //! Constraint
00100         Constraint& constraint_;
        //! current value of the local minimum
00102         Array currentValue_;
        //! function and gradient norm values at the curentValue_ (i.e. the last step)
00104         Real functionValue_, squaredNorm_;
        //! number of evaluation of cost function and its gradient
00106         Integer functionEvaluation_, gradientEvaluation_;

    // inline definitions
00110     inline Real Problem::value(const Array& x) {
        return costFunction_.value(x);

00115     inline Disposable<Array> Problem::values(const Array& x) {
        return costFunction_.values(x);

00120     inline void Problem::gradient(Array& grad_f,
                                  const Array& x) {
        costFunction_.gradient(grad_f, x);

00126     inline Real Problem::valueAndGradient(Array& grad_f,
                                          const Array& x) {
        return costFunction_.valueAndGradient(grad_f, x);

00133     inline void Problem::reset() {
        functionEvaluation_ = gradientEvaluation_ = 0;
        functionValue_ = squaredNorm_ = Null<Real>();



Generated by  Doxygen 1.6.0   Back to index