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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */

 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 method.hpp
    \brief Abstract optimization method class

#ifndef quantlib_optimization_method_h
#define quantlib_optimization_method_h

#include <ql/Optimization/constraint.hpp>
#include <ql/Optimization/costfunction.hpp>
#include <ql/Optimization/criteria.hpp>

namespace QuantLib {

    class Problem;

    //! Abstract class for constrained optimization method
00036     class OptimizationMethod {
        : iterationNumber_(0), functionEvaluation_(0),
          gradientEvaluation_(0), functionValue_(1), squaredNorm_(1) {}
        virtual ~OptimizationMethod() {}

        //! Set initial value
        void setInitialValue(const Array& initialValue);

        //! Set optimization end criteria
        void setEndCriteria(const EndCriteria& endCriteria);

        //! current iteration number
00050         Integer& iterationNumber() const { return iterationNumber_; }

        //! optimization end criteria
00053         EndCriteria& endCriteria() const { return endCriteria_; }

        //! number of evaluation of cost function
00056         Integer& functionEvaluation() const { return functionEvaluation_; }

        //! number of evaluation of cost function gradient
00059         Integer& gradientEvaluation() const { return gradientEvaluation_; }

        //! value of cost function
00062         Real& functionValue() const { return functionValue_; }

        //! value of cost function gradient norm
00065         Real& gradientNormValue() const { return squaredNorm_; }

        //! current value of the local minimum
00068         Array& x() const { return x_; }

        //! current value of the search direction
00071         Array& searchDirection() const { return searchDirection_; }

        //! minimize the optimization problem P
        virtual void minimize(const Problem& P) const = 0;
        //! initial value of unknowns
00077         Array initialValue_;
        //! current iteration step in the Optimization process
00079         mutable Integer iterationNumber_;
        //! optimization end criteria
00081         mutable EndCriteria endCriteria_;
        //! number of evaluation of cost function and its gradient
00083         mutable Integer functionEvaluation_, gradientEvaluation_;
        //! function and gradient norm values of the last step
00085         mutable Real functionValue_, squaredNorm_;
        //! current values of the local minimum and the search direction
00087         mutable Array x_, searchDirection_;

    // inline definitions

00092     inline void OptimizationMethod::setEndCriteria(
                                        const EndCriteria& endCriteria) {
        endCriteria_ = endCriteria;

00097     inline void OptimizationMethod::setInitialValue(
                                        const Array& initialValue) {
        iterationNumber_ = 0;
        initialValue_ = initialValue;
        x_ = initialValue;
        searchDirection_ = Array(x_.size ());



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