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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 armijo.cpp
\brief Armijo line-search class
\fullpath
ql/Optimization/%armijo.cpp
*/
#include "ql/Optimization/armijo.hpp"
namespace QuantLib {
namespace Optimization {
double ArmijoLineSearch::operator()(
OptimizationProblem &P, // Optimization problem
double t_ini) // initial value of line-search step
{
OptimizationMethod& method = P.optimisationMethod();
Constraint& constraint = P.constraint();
bool maxIter = false;
double q0 = method.functionValue();
double qp0 = method.gradientNormValue();
qt_ = q0;
qpt_ = qp0;
double qtold, t = t_ini;
int loopNumber = 0;
Array& x = method.x();
Array& d = method.searchDirection();
// Initialize gradient
gradient_ = Array(x.size());
// Compute new point
xtd_ = x;
t = update(xtd_, d, t, constraint);
// Compute function value at the new point
qt_ = P.value (xtd_);
// Enter in the loop if the criterion is not satisfied
if ((qt_-q0) > -alpha_*t*qpt_) {
do {
std::cout << "Line iteration for t " << t << std::endl;
loopNumber++;
// Decrease step
t *= beta_;
// Store old value of the function
qtold = qt_;
// New point value
xtd_ = x;
t = update(xtd_, d, t, constraint);
// Compute function value at the new point
qt_ = P.value (xtd_);
P.gradient (gradient_, xtd_);
// and it squared norm
maxIter = P.optimisationMethod().endCriteria().
checkIterationNumber(loopNumber);
} while (
(((qt_ - q0) > (-alpha_ * t * qpt_)) ||
((qtold - q0) <= (-alpha_ * t * qpt_ / beta_))) &&
(!maxIter));
}
if (maxIter)
succeed_ = false;
// Compute new gradient
P.gradient(gradient_, xtd_);
// and it squared norm
qpt_ = DotProduct(gradient_, gradient_);
// Return new step value
return t;
}
}
}
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