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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2006, 2007 Ferdinando Ametrano
Copyright (C) 2007 Marco Bianchetti
Copyright (C) 2001, 2002, 2003 Nicolas Di Césaré
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/endcriteria.hpp>
#include <ql/errors.hpp>
#include <algorithm>
namespace QuantLib {
EndCriteria::EndCriteria(Size maxIterations,
Size maxStationaryStateIterations,
Real rootEpsilon,
Real functionEpsilon,
Real gradientNormEpsilon)
: maxIterations_(maxIterations),
maxStationaryStateIterations_(maxStationaryStateIterations),
rootEpsilon_(rootEpsilon),
functionEpsilon_(functionEpsilon),
gradientNormEpsilon_(gradientNormEpsilon) {
if (maxStationaryStateIterations_ == Null<Size>())
maxStationaryStateIterations_ = std::min(static_cast<Size>(maxIterations/2),
static_cast<Size>(100));
QL_REQUIRE(maxStationaryStateIterations_>1,
"maxStationaryStateIterations_ (" <<
maxStationaryStateIterations_ <<
") must be greater than one");
QL_REQUIRE(maxStationaryStateIterations_<maxIterations_,
"maxStationaryStateIterations_ (" <<
maxStationaryStateIterations_ <<
") must be less than maxIterations_ (" <<
maxIterations_ << ")");
if (gradientNormEpsilon_ == Null<Real>())
gradientNormEpsilon_ = functionEpsilon_;
}
bool EndCriteria::checkMaxIterations(const Size iteration,
EndCriteria::Type& ecType) const{
if (iteration < maxIterations_)
return false;
ecType = MaxIterations;
return true;
}
bool EndCriteria::checkStationaryPoint(const Real xOld,
const Real xNew,
Size& statStateIterations,
EndCriteria::Type& ecType) const {
if (std::fabs(xNew-xOld) >= rootEpsilon_) {
statStateIterations = 0;
return false;
}
++statStateIterations;
if (statStateIterations <= maxStationaryStateIterations_)
return false;
ecType = StationaryPoint;
return true;
}
bool EndCriteria::checkStationaryFunctionValue(
const Real fxOld,
const Real fxNew,
Size& statStateIterations,
EndCriteria::Type& ecType) const {
if (std::fabs(fxNew-fxOld) >= functionEpsilon_) {
statStateIterations = 0;
return false;
}
++statStateIterations;
if (statStateIterations <= maxStationaryStateIterations_)
return false;
ecType = StationaryFunctionValue;
return true;
}
bool EndCriteria::checkStationaryFunctionAccuracy(
const Real f,
const bool positiveOptimization,
EndCriteria::Type& ecType) const {
if (!positiveOptimization)
return false;
if (f >= functionEpsilon_)
return false;
ecType = StationaryFunctionAccuracy;
return true;
}
//bool EndCriteria::checkZerGradientNormValue(
// const Real gNormOld,
// const Real gNormNew,
// EndCriteria::Type& ecType) const {
// if (std::fabs(gNormNew-gNormOld) >= gradientNormEpsilon_)
// return false;
// ecType = StationaryGradient;
// return true;
//}
bool EndCriteria::checkZeroGradientNorm(const Real gradientNorm,
EndCriteria::Type& ecType) const {
if (gradientNorm >= gradientNormEpsilon_)
return false;
ecType = ZeroGradientNorm;
return true;
}
bool EndCriteria::operator()(const Size iteration,
Size& statStateIterations,
const bool positiveOptimization,
const Real fold,
const Real, //normgold,
const Real fnew,
const Real normgnew,
EndCriteria::Type& ecType) const {
return
checkMaxIterations(iteration, ecType) ||
checkStationaryFunctionValue(fold, fnew, statStateIterations, ecType) ||
checkStationaryFunctionAccuracy(fnew, positiveOptimization, ecType) ||
checkZeroGradientNorm(normgnew, ecType);
}
// Inspectors
Size EndCriteria::maxIterations() const {
return maxIterations_;
}
Size EndCriteria::maxStationaryStateIterations() const {
return maxStationaryStateIterations_;
}
Real EndCriteria::rootEpsilon() const {
return rootEpsilon_;
}
Real EndCriteria::functionEpsilon() const {
return functionEpsilon_;
}
Real EndCriteria::gradientNormEpsilon() const {
return gradientNormEpsilon_;
}
std::ostream& operator<<(std::ostream& out, EndCriteria::Type ec) {
switch (ec) {
case QuantLib::EndCriteria::None:
return out << "None";
case QuantLib::EndCriteria::MaxIterations:
return out << "MaxIterations";
case QuantLib::EndCriteria::StationaryPoint:
return out << "StationaryPoint";
case QuantLib::EndCriteria::StationaryFunctionValue:
return out << "StationaryFunctionValue";
case QuantLib::EndCriteria::StationaryFunctionAccuracy:
return out << "StationaryFunctionAccuracy";
case QuantLib::EndCriteria::ZeroGradientNorm:
return out << "ZeroGradientNorm";
case QuantLib::EndCriteria::Unknown:
return out << "Unknown";
default:
QL_FAIL("unknown EndCriteria::Type (" << Integer(ec) << ")");
}
}
}
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