
/*
 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 http://quantlib.org/html/license.html

 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
    class OptimizationMethod {
      public:
        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
        int& iterationNumber() const { return iterationNumber_; }

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

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

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

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

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

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

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

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

    /*! \deprecated use OptimizationMethod */
    typedef OptimizationMethod Method;


    // inline definitions

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

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

}


#endif
