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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2015 Cheng Li
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/goldstein.hpp>
#include <ql/math/optimization/method.hpp>
#include <ql/math/optimization/problem.hpp>
#include <ql/math/comparison.hpp>
namespace QuantLib {
Real GoldsteinLineSearch::operator()(Problem& P,
EndCriteria::Type& ecType,
const EndCriteria& endCriteria,
const Real t_ini)
{
Constraint& constraint = P.constraint();
succeed_=true;
bool maxIter = false;
Real t = t_ini;
Size loopNumber = 0;
Real q0 = P.functionValue();
Real qp0 = P.gradientNormValue();
Real tl = 0.0;
Real tr = 0.0;
qt_ = q0;
qpt_ = (gradient_.empty()) ? qp0 : -DotProduct(gradient_,searchDirection_);
// Initialize gradient
gradient_ = Array(P.currentValue().size());
// Compute new point
xtd_ = P.currentValue();
t = update(xtd_, searchDirection_, t, constraint);
// Compute function value at the new point
qt_ = P.value (xtd_);
while ((qt_ - q0) < -beta_*t*qpt_ || (qt_ - q0) > -alpha_*t*qpt_) {
if ((qt_ - q0) > -alpha_*t*qpt_)
tr = t;
else
tl = t;
++loopNumber;
// calculate the new step
if (close_enough(tr, 0.0))
t *= extrapolation_;
else
t = (tl + tr) / 2.0;
// New point value
xtd_ = P.currentValue();
t = update(xtd_, searchDirection_, t, constraint);
// Compute function value at the new point
qt_ = P.value (xtd_);
P.gradient (gradient_, xtd_);
// and it squared norm
maxIter = endCriteria.checkMaxIterations(loopNumber, ecType);
if (maxIter)
break;
}
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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