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/*
Copyright (C) 2001, 2002 Nicolas Di Csar
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 ferdinando@ametrano.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 conjugategradient.cpp
\brief Conjugate gradient optimization method
\fullpath
ql/Optimization/%conjugategradient.cpp
*/
#include "ql/Optimization/conjugategradient.hpp"
namespace QuantLib {
namespace Optimization {
void ConjugateGradient::minimize(OptimizationProblem &P) {
bool EndCriteria;
// function and squared norm of gradient values;
double fold, gold2;
double c;
double normdiff;
// classical initial value for line-search step
double t = 1.0;
// reference X as the optimization problem variable
Array& X = x();
Array& SearchDirection = searchDirection();
// Set g at the size of the optimization problem search direction
int sz = searchDirection().size();
Array g(sz), d(sz), sddiff(sz);
functionValue() = P.valueAndGradient(g, X);
SearchDirection = -g;
gradientNormValue() = DotProduct(g, g);
do {
// Linesearch
std::cout << "Doing line search on direction " << SearchDirection << std::endl;
t = (*lineSearch_)(P, t);
if (!lineSearch_->succeed())
throw Error("Conjugate gradient: line-search failed!");
// Updates
d = SearchDirection;
// New point
X = lineSearch_->lastX();
// New function value
fold = functionValue();
functionValue() = lineSearch_->lastFunctionValue();
// New gradient and search direction vectors
g = lineSearch_->lastGradient();
// orthogonalization coef
gold2 = gradientNormValue();
gradientNormValue() = lineSearch_->lastGradientNorm2();
c = gradientNormValue() / gold2;
// conjugate gradient search direction
sddiff = (-g + c * d) - SearchDirection;
normdiff = QL_SQRT(DotProduct(sddiff, sddiff));
SearchDirection = -g + c * d;
// End criteria
EndCriteria = endCriteria()(iterationNumber_,
fold, QL_SQRT(gold2), functionValue(),
QL_SQRT(gradientNormValue()), normdiff);
// Increase interation number
iterationNumber()++;
} while (EndCriteria == false);
}
}
}
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