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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 steepestdescent.cpp
\brief Steepest descent optimization method
\fullpath
ql/Optimization/%steepestdescent.cpp
*/
#include "ql/Optimization/steepestdescent.hpp"
namespace QuantLib {
namespace Optimization {
void SteepestDescent::minimize(OptimizationProblem& P) {
bool EndCriteria;
// function and squared norm of gradient values;
double normdiff;
// classical initial value for line-search step
double t = 1.0;
// reference X as the optimization problem variable
Array& X = x();
// Set gold at the size of the optimization problem search direction
Array gold(searchDirection().size());
Array gdiff(searchDirection().size());
functionValue() = P.valueAndGradient(gold, X);
searchDirection() = -gold;
gradientNormValue() = DotProduct(gold, gold);
normdiff = QL_SQRT(gradientNormValue());
do {
// Linesearch
t = (*lineSearch_)(P, t);
if (!lineSearch_->succeed())
throw Error("SteepestDescent: line-search failed!");
// End criteria
EndCriteria = endCriteria()(
iterationNumber_, functionValue(),
QL_SQRT(gradientNormValue()),
lineSearch_->lastFunctionValue(),
QL_SQRT(lineSearch_->lastGradientNorm2()), normdiff);
// Updates
// New point
X = lineSearch_->lastX();
// New function value
functionValue() = lineSearch_->lastFunctionValue();
// New gradient and search direction vectors
gdiff = gold - lineSearch_->lastGradient();
normdiff = QL_SQRT(DotProduct (gdiff, gdiff));
gold = lineSearch_->lastGradient();
searchDirection() = -gold;
// New gradient squared norm
gradientNormValue() = lineSearch_->lastGradientNorm2();
// Increase interation number
iterationNumber()++;
} while (EndCriteria == false);
}
}
}
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