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

 Copyright (C) 2006 Klaus Spanderen

 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.

#include <ql/math/optimization/lmdif.hpp>
#include <ql/math/optimization/levenbergmarquardt.hpp>

namespace QuantLib {

    LevenbergMarquardt::LevenbergMarquardt(Real epsfcn,
                                           Real xtol,
                                           Real gtol)
    : info_(0), epsfcn_(epsfcn), xtol_(xtol), gtol_(gtol) {}

    Integer LevenbergMarquardt::getInfo() const {
        return info_;

00034     EndCriteria::Type LevenbergMarquardt::minimize(Problem& P,
                                                   const EndCriteria& endCriteria) {
        EndCriteria::Type ecType = EndCriteria::None;
        Array x_ = P.currentValue();
        ProblemData::instance().problem() = &P;
        ProblemData::instance().initCostValues() = P.costFunction().values(x_);

        int m = ProblemData::instance().initCostValues().size();
        int n = x_.size();
        boost::scoped_array<double> xx(new double[n]);
        std::copy(x_.begin(), x_.end(), xx.get());
        boost::scoped_array<double> fvec(new double[m]);
        boost::scoped_array<double> diag(new double[n]);
        int mode = 1;
        double factor = 1;
        int nprint = 0;
        int info = 0;
        int nfev =0;
        boost::scoped_array<double> fjac(new double[m*n]);
        int ldfjac = m;
        boost::scoped_array<int> ipvt(new int[n]);
        boost::scoped_array<double> qtf(new double[n]);
        boost::scoped_array<double> wa1(new double[n]);
        boost::scoped_array<double> wa2(new double[n]);
        boost::scoped_array<double> wa3(new double[n]);
        boost::scoped_array<double> wa4(new double[m]);
        // call lmdif to minimize the sum of the squares of m functions
        // in n variables by the Levenberg-Marquardt algorithm.
        QuantLib::MINPACK::lmdif(m, n, xx.get(), fvec.get(),
                                 diag.get(), mode, factor,
                                 nprint, &info, &nfev, fjac.get(),
                                 ldfjac, ipvt.get(), qtf.get(),
                                 wa1.get(), wa2.get(), wa3.get(), wa4.get());
        info_ = info;
        // check requirements & endCriteria evaluation
        QL_REQUIRE(info != 0, "MINPACK: improper input parameters");
        //QL_REQUIRE(info != 6, "MINPACK: ftol is too small. no further "
        //                               "reduction in the sum of squares "
        //                               "is possible.");
        if (info != 6) ecType = QuantLib::EndCriteria::StationaryFunctionValue;
        //QL_REQUIRE(info != 5, "MINPACK: number of calls to fcn has "
        //                               "reached or exceeded maxfev.");
        endCriteria.checkMaxIterations(nfev, ecType);
        QL_REQUIRE(info != 7, "MINPACK: xtol is too small. no further "
                                       "improvement in the approximate "
                                       "solution x is possible.");
        QL_REQUIRE(info != 8, "MINPACK: gtol is too small. fvec is "
                                       "orthogonal to the columns of the "
                                       "jacobian to machine precision.");
        // set problem
        std::copy(xx.get(), xx.get()+n, x_.begin());

        return ecType;

    void LevenbergMarquardt::fcn(int, int n, double* x, double* fvec, int*) {
        Array xt(n);
        std::copy(x, x+n, xt.begin());
        // constraint handling needs some improvement in the future:
        // starting point should not be close to a constraint violation
        if (ProblemData::instance().problem()->constraint().test(xt)) {
            const Array& tmp = ProblemData::instance().problem()->values(xt);
            std::copy(tmp.begin(), tmp.end(), fvec);
        } else {
                      ProblemData::instance().initCostValues().end(), fvec);


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