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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2009 Frédéric Degraeve
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/license.shtml>.
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/bfgs.hpp>
#include <ql/math/optimization/problem.hpp>
#include <ql/math/optimization/linesearch.hpp>
namespace QuantLib {
Array BFGS::getUpdatedDirection(const Problem& P,
Real,
const Array& oldGradient) {
if (inverseHessian_.rows() == 0)
{
// first time in this update, we create needed structures
inverseHessian_ = Matrix(P.currentValue().size(),
P.currentValue().size(), 0.);
for (Size i = 0; i < P.currentValue().size(); ++i)
inverseHessian_[i][i] = 1.;
}
Array diffGradient;
Array diffGradientWithHessianApplied(P.currentValue().size(), 0.);
diffGradient = lineSearch_->lastGradient() - oldGradient;
for (Size i = 0; i < P.currentValue().size(); ++i)
for (Size j = 0; j < P.currentValue().size(); ++j)
diffGradientWithHessianApplied[i] += inverseHessian_[i][j] * diffGradient[j];
Real fac, fae, fad;
Real sumdg, sumxi;
fac = fae = sumdg = sumxi = 0.;
for (Size i = 0; i < P.currentValue().size(); ++i)
{
fac += diffGradient[i] * lineSearch_->searchDirection()[i];
fae += diffGradient[i] * diffGradientWithHessianApplied[i];
sumdg += std::pow(diffGradient[i], 2.);
sumxi += std::pow(lineSearch_->searchDirection()[i], 2.);
}
if (fac > std::sqrt(1e-8 * sumdg * sumxi)) // skip update if fac not sufficiently positive
{
fac = 1.0 / fac;
fad = 1.0 / fae;
for (Size i = 0; i < P.currentValue().size(); ++i)
diffGradient[i] = fac * lineSearch_->searchDirection()[i] - fad * diffGradientWithHessianApplied[i];
for (Size i = 0; i < P.currentValue().size(); ++i)
for (Size j = 0; j < P.currentValue().size(); ++j)
{
inverseHessian_[i][j] += fac * lineSearch_->searchDirection()[i] * lineSearch_->searchDirection()[j];
inverseHessian_[i][j] -= fad * diffGradientWithHessianApplied[i] * diffGradientWithHessianApplied[j];
inverseHessian_[i][j] += fae * diffGradient[i] * diffGradient[j];
}
}
//else
// throw "BFGS: FAC not sufficiently positive";
Array direction(P.currentValue().size());
for (Size i = 0; i < P.currentValue().size(); ++i)
{
direction[i] = 0.0;
for (Size j = 0; j < P.currentValue().size(); ++j)
direction[i] -= inverseHessian_[i][j] * lineSearch_->lastGradient()[j];
}
return direction;
}
}
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