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97 lines
2.6 KiB
C++
97 lines
2.6 KiB
C++
// Copyright Maksym Zhelyenzyakov 2025-2026.
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// Distributed under the Boost Software License, Version 1.0.
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// (See accompanying file LICENSE_1_0.txt or copy at
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// https://www.boost.org/LICENSE_1_0.txt)
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#include "test_autodiff_reverse.hpp"
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#include "test_functions_for_optimization.hpp"
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#include <boost/math/differentiation/autodiff_reverse.hpp>
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#include <boost/math/optimization/lbfgs.hpp>
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#include <boost/math/optimization/minimizer.hpp>
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namespace rdiff = boost::math::differentiation::reverse_mode;
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namespace bopt = boost::math::optimization;
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BOOST_AUTO_TEST_SUITE(basic_lbfgs)
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BOOST_AUTO_TEST_CASE_TEMPLATE(default_lbfgs_test, T, all_float_types)
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{
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constexpr size_t NITER = 10;
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constexpr size_t M = 10;
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const T eps = T{ 1e-8 };
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RandomSample<T> rng{ T(-10), T(10) };
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std::array<rdiff::rvar<T, 1>, 2> x;
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x[0] = rng.next();
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x[1] = rng.next();
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auto opt = bopt::make_lbfgs(&rosenbrock_saddle<rdiff::rvar<T, 1>>, x, M);
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auto result = minimize(opt);
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for (auto& xi : x) {
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BOOST_REQUIRE_CLOSE(xi, T{ 1.0 }, eps);
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}
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}
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// Custom initialization policy that zeros out the parameters
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template<typename RealType>
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struct zero_init_policy
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{
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void operator()(std::vector<RealType>& x) const noexcept
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{
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std::fill(x.begin(), x.end(), RealType{ 0 });
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}
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};
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template<typename RealType>
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struct analytic_objective_eval_pol
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{
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template<typename Objective, typename ArgumentContainer>
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RealType operator()(Objective&& objective, ArgumentContainer& x)
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{
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return objective(x);
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}
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};
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template<typename RealType>
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struct analytic_gradient_eval_pol
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{
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template<class Objective,
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class ArgumentContainer,
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class FunctionEvaluationPolicy>
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void operator()(Objective&& obj_f,
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ArgumentContainer& x,
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FunctionEvaluationPolicy&& f_eval_pol,
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RealType& obj_v,
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std::vector<RealType>& grad_container)
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{
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RealType v = f_eval_pol(obj_f, x);
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obj_v = v;
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grad_container.resize(x.size());
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for (size_t i = 0; i < x.size(); ++i) {
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grad_container[i] = 2 * x[i];
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}
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}
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};
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BOOST_AUTO_TEST_CASE_TEMPLATE(custom_init_lbfgs_test, T, all_float_types)
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{
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constexpr size_t M = 8;
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const T eps = T{ 1e-6 };
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RandomSample<T> rng{ T(-5), T(5) };
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std::array<rdiff::rvar<T, 1>, 2> x;
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x[0] = rng.next();
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x[1] = rng.next();
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auto opt = bopt::make_lbfgs(&rosenbrock_saddle<rdiff::rvar<T, 1>>,
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x,
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M,
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bopt::costant_initializer_rvar<T>(0.0));
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auto result = minimize(opt);
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for (auto& xi : x) {
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BOOST_REQUIRE_CLOSE(xi, T{ 1.0 }, eps);
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}
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}
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BOOST_AUTO_TEST_SUITE_END()
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