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gezelter |
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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */ |
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/* |
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Copyright (C) 2009 Frédéric Degraeve |
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This file is part of QuantLib, a free-software/open-source library |
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for financial quantitative analysts and developers - http://quantlib.org/ |
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QuantLib is free software: you can redistribute it and/or modify it |
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under the terms of the QuantLib license. You should have received a |
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copy of the license along with this program; if not, please email |
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<quantlib-dev@lists.sf.net>. The license is also available online at |
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<http://quantlib.org/license.shtml>. |
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This program is distributed in the hope that it will be useful, but WITHOUT |
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ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS |
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FOR A PARTICULAR PURPOSE. See the license for more details. |
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*/ |
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#include "optimization/BFGS.hpp" |
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#include "optimization/Problem.hpp" |
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#include "optimization/LineSearch.hpp" |
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namespace QuantLib { |
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DynamicVector<RealType> BFGS::getUpdatedDirection(const Problem& P, |
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RealType, |
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const DynamicVector<RealType>& oldGradient) { |
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if (inverseHessian_.getNRow() == 0) |
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{ |
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// first time in this update, we create needed structures |
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inverseHessian_ = DynamicRectMatrix<RealType>(P.currentValue().size(), |
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P.currentValue().size(), 0.); |
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for (size_t i = 0; i < P.currentValue().size(); ++i) |
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inverseHessian_(i,i) = 1.; |
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} |
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DynamicVector<RealType> diffGradient; |
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DynamicVector<RealType> diffGradientWithHessianApplied(P.currentValue().size(), 0.); |
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diffGradient = lineSearch_->lastGradient() - oldGradient; |
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for (size_t i = 0; i < P.currentValue().size(); ++i) |
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for (size_t j = 0; j < P.currentValue().size(); ++j) |
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diffGradientWithHessianApplied[i] += inverseHessian_(i,j) * diffGradient[j]; |
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double fac, fae, fad; |
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double sumdg, sumxi; |
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fac = fae = sumdg = sumxi = 0.; |
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for (size_t i = 0; i < P.currentValue().size(); ++i) |
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{ |
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fac += diffGradient[i] * lineSearch_->searchDirection()[i]; |
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fae += diffGradient[i] * diffGradientWithHessianApplied[i]; |
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sumdg += std::pow(diffGradient[i], 2.); |
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sumxi += std::pow(lineSearch_->searchDirection()[i], 2.); |
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} |
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if (fac > std::sqrt(1e-8 * sumdg * sumxi)) // skip update if fac not sufficiently positive |
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{ |
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fac = 1.0 / fac; |
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fad = 1.0 / fae; |
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for (size_t i = 0; i < P.currentValue().size(); ++i) |
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diffGradient[i] = fac * lineSearch_->searchDirection()[i] - fad * diffGradientWithHessianApplied[i]; |
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for (size_t i = 0; i < P.currentValue().size(); ++i) |
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for (size_t j = 0; j < P.currentValue().size(); ++j) |
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{ |
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inverseHessian_(i,j) += fac * lineSearch_->searchDirection()[i] * lineSearch_->searchDirection()[j]; |
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inverseHessian_(i,j) -= fad * diffGradientWithHessianApplied[i] * diffGradientWithHessianApplied[j]; |
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inverseHessian_(i,j) += fae * diffGradient[i] * diffGradient[j]; |
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} |
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} |
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//else |
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// throw "BFGS: FAC not sufficiently positive"; |
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DynamicVector<RealType> direction(P.currentValue().size()); |
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for (size_t i = 0; i < P.currentValue().size(); ++i) |
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{ |
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direction[i] = 0.0; |
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for (size_t j = 0; j < P.currentValue().size(); ++j) |
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direction[i] -= inverseHessian_(i,j) * lineSearch_->lastGradient()[j]; |
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} |
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return direction; |
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} |
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} |
