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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2019 SoftSolutions! S.r.l.
Copyright (C) 2025 Peter Caspers
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
<https://www.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/levenbergmarquardt.hpp>
#include <ql/termstructures/globalbootstrap.hpp>
namespace QuantLib {
MultiCurveBootstrap::MultiCurveBootstrap(Real accuracy) {
optimizer_ = ext::make_shared<LevenbergMarquardt>(accuracy, accuracy, accuracy);
endCriteria_ = ext::make_shared<EndCriteria>(1000, 10, accuracy, accuracy, accuracy);
}
MultiCurveBootstrap::MultiCurveBootstrap(ext::shared_ptr<OptimizationMethod> optimizer,
ext::shared_ptr<EndCriteria> endCriteria)
: optimizer_(std::move(optimizer)), endCriteria_(std::move(endCriteria)) {
constexpr auto accuracy = 1E-10;
if (optimizer_ == nullptr)
optimizer_ = ext::make_shared<LevenbergMarquardt>(accuracy, accuracy, accuracy);
if (endCriteria_ == nullptr)
endCriteria_ = ext::make_shared<EndCriteria>(1000, 10, accuracy, accuracy, accuracy);
}
void MultiCurveBootstrap::add(const MultiCurveBootstrapContributor* c) {
contributors_.push_back(c);
c->setParentBootstrapper(shared_from_this());
}
void MultiCurveBootstrap::addObserver(Observer* o) {
observers_.push_back(o);
}
void MultiCurveBootstrap::runMultiCurveBootstrap() {
std::vector<Size> guessSizes;
std::vector<Real> globalGuess;
for (auto const& c : contributors_) {
Array guess = c->setupCostFunction();
globalGuess.insert(globalGuess.end(), guess.begin(), guess.end());
guessSizes.push_back(guess.size());
}
auto fn = [this, &guessSizes](const Array& x) {
// call the contributors' cost functions' set part
std::size_t offset = 0;
for (std::size_t c = 0; c < contributors_.size(); ++c) {
Array tmp(guessSizes[c]);
std::copy(std::next(x.begin(), offset), std::next(x.begin(), offset + guessSizes[c]),
tmp.begin());
offset += guessSizes[c];
contributors_[c]->setCostFunctionArgument(tmp);
}
// update observers
for(auto *o: observers_)
o->update();
// collect the contributors' result
std::vector<Array> results;
results.reserve(contributors_.size());
for (auto& contributor : contributors_) {
results.push_back(contributor->evaluateCostFunction());
}
// concatenate the contributors' values and return the concatenation as the result
std::size_t resultSize =
std::accumulate(results.begin(), results.end(), (std::size_t)0,
[](std::size_t len, const Array& a) { return len + a.size(); });
Array result(resultSize);
offset = 0;
for (auto const& r : results) {
std::copy(r.begin(), r.end(), std::next(result.begin(), offset));
offset += r.size();
}
return result;
};
SimpleCostFunction<decltype(fn)> costFunction(fn);
NoConstraint noConstraint;
Problem problem(costFunction, noConstraint, Array(globalGuess.begin(), globalGuess.end()));
EndCriteria::Type endType = optimizer_->minimize(problem, *endCriteria_);
QL_REQUIRE(
EndCriteria::succeeded(endType),
"global bootstrap failed to minimize to required accuracy (during multi curve bootstrap): "
<< endType);
// set all contributors to valid
for (auto const& c : contributors_)
c->setToValid();
}
} // namespace QuantLib
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