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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2001, 2002, 2003 Nicolas Di Csar
Copyright (C) 2007 Ferdinando Ametrano
Copyright (C) 2007 Marco Bianchetti
Copyright (C) 2007 Franois du Vignaud
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/conjugategradient.hpp>
#include <ql/math/optimization/problem.hpp>
#include <ql/math/optimization/linesearch.hpp>
namespace QuantLib {
EndCriteria::Type ConjugateGradient::minimize(Problem &P,
const EndCriteria& endCriteria) {
// Initializations
Real ftol = endCriteria.functionEpsilon();
Size maxStationaryStateIterations_
= endCriteria.maxStationaryStateIterations();
EndCriteria::Type ecType = EndCriteria::None; // reset end criteria
P.reset(); // reset problem
Array x_ = P.currentValue(); // store the starting point
Size iterationNumber_=0 /*, stationaryStateIterationNumber_=0 */;
lineSearch_->searchDirection() = Array(x_.size()); // dimension line search
bool done = false;
// function and squared norm of gradient values;
Real fnew, fold, gold2;
Real c;
Real fdiff, normdiff;
// classical initial value for line-search step
Real t = 1.0;
// Set gradient g at the size of the optimization problem search direction
Size sz = lineSearch_->searchDirection().size();
Array g(sz), d(sz), sddiff(sz);
// Initialize cost function, gradient g and search direction
P.setFunctionValue(P.valueAndGradient(g, x_));
P.setGradientNormValue(DotProduct(g, g));
lineSearch_->searchDirection() = -g;
// Loop over iterations
do {
// Linesearch
t = (*lineSearch_)(P, ecType, endCriteria, t);
// don't throw: it can fail just because maxIterations exceeded
//QL_REQUIRE(lineSearch_->succeed(), "line-search failed!");
if (lineSearch_->succeed())
{
// Updates
d = lineSearch_->searchDirection();
// New point
x_ = lineSearch_->lastX();
// New function value
fold = P.functionValue();
P.setFunctionValue(lineSearch_->lastFunctionValue());
// New gradient and search direction vectors
g = lineSearch_->lastGradient();
// orthogonalization coef
gold2 = P.gradientNormValue();
P.setGradientNormValue(lineSearch_->lastGradientNorm2());
c = P.gradientNormValue() / gold2;
// conjugate gradient search direction
sddiff = (-g + c * d) - lineSearch_->searchDirection();
normdiff = std::sqrt(DotProduct(sddiff, sddiff));
lineSearch_->searchDirection() = -g + c * d;
// Now compute accuracy and check end criteria
// Numerical Recipes exit strategy on fx (see NR in C++, p.423)
fnew = P.functionValue();
fdiff = 2.0*std::fabs(fnew-fold) /
(std::fabs(fnew) + std::fabs(fold) + QL_EPSILON);
if (fdiff < ftol ||
endCriteria.checkMaxIterations(iterationNumber_, ecType)) {
endCriteria.checkStationaryFunctionValue(0.0, 0.0,
maxStationaryStateIterations_, ecType);
endCriteria.checkMaxIterations(iterationNumber_, ecType);
return ecType;
}
//done = endCriteria(iterationNumber_,
// stationaryStateIterationNumber_,
// true, //FIXME: it should be in the problem
// fold,
// std::sqrt(gold2),
// P.functionValue(),
// std::sqrt(P.gradientNormValue()),
// ecType);
P.setCurrentValue(x_); // update problem current value
++iterationNumber_; // Increase iteration number
} else {
done=true;
}
} while (!done);
P.setCurrentValue(x_);
return ecType;
}
}
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