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175 lines
6.2 KiB
C++
175 lines
6.2 KiB
C++
/*
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* Copyright Nick Thompson, 2024
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* Use, modification and distribution are subject to the
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* Boost Software License, Version 1.0. (See accompanying file
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* LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
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*/
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#include "math_unit_test.hpp"
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#include "test_functions_for_optimization.hpp"
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#include <boost/math/optimization/jso.hpp>
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#include <random>
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#include <limits>
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using boost::math::optimization::jso;
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using boost::math::optimization::jso_parameters;
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using boost::math::optimization::detail::weighted_lehmer_mean;
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void test_weighted_lehmer_mean() {
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size_t n = 50;
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std::vector<double> weights(n, 1.0);
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std::vector<double> values(n, 2.5);
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// Technically, this is not a fully general weighted Lehmer mean,
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// but just a weighted contraharmonic mean.
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// So we have a few more invariants available to us:
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CHECK_ULP_CLOSE(2.5, weighted_lehmer_mean(values, weights), n);
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std::mt19937_64 gen(12345);
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std::uniform_real_distribution<double> unif(std::numeric_limits<double>::epsilon(),1);
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for (size_t i = 0; i < n; ++i) {
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weights[i] = unif(gen);
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values[i] = unif(gen);
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}
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auto mean = weighted_lehmer_mean(values, weights);
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CHECK_LE(mean, 1.0);
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CHECK_LE(std::numeric_limits<double>::epsilon(), mean);
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}
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template <class Real> void test_ackley() {
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std::cout << "Testing jSO on Ackley function . . .\n";
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using ArgType = std::array<Real, 2>;
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auto jso_params = jso_parameters<ArgType>();
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jso_params.lower_bounds = {-5, -5};
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jso_params.upper_bounds = {5, 5};
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std::mt19937_64 gen(12345);
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auto local_minima = jso(ackley<Real>, jso_params, gen);
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CHECK_LE(std::abs(local_minima[0]), 10 * std::numeric_limits<Real>::epsilon());
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CHECK_LE(std::abs(local_minima[1]), 10 * std::numeric_limits<Real>::epsilon());
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// Does it work with a lambda?
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auto ack = [](std::array<Real, 2> const &x) { return ackley<Real>(x); };
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local_minima = jso(ack, jso_params, gen);
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CHECK_LE(std::abs(local_minima[0]), 10 * std::numeric_limits<Real>::epsilon());
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CHECK_LE(std::abs(local_minima[1]), 10 * std::numeric_limits<Real>::epsilon());
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// Test that if an intial guess is the exact solution, the returned solution is the exact solution:
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std::array<Real, 2> initial_guess{0, 0};
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jso_params.initial_guess = &initial_guess;
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local_minima = jso(ack, jso_params, gen);
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CHECK_EQUAL(local_minima[0], Real(0));
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CHECK_EQUAL(local_minima[1], Real(0));
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}
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template <class Real> void test_rosenbrock_saddle() {
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std::cout << "Testing jSO on Rosenbrock saddle . . .\n";
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using ArgType = std::array<Real, 2>;
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auto jso_params = jso_parameters<ArgType>();
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jso_params.lower_bounds = {0.5, 0.5};
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jso_params.upper_bounds = {2.048, 2.048};
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std::mt19937_64 gen(234568);
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auto local_minima = jso(rosenbrock_saddle<Real>, jso_params, gen);
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CHECK_ABSOLUTE_ERROR(Real(1), local_minima[0], 10 * std::numeric_limits<Real>::epsilon());
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CHECK_ABSOLUTE_ERROR(Real(1), local_minima[1], 10 * std::numeric_limits<Real>::epsilon());
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// Does cancellation work?
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std::atomic<bool> cancel = true;
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gen.seed(12345);
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local_minima =
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jso(rosenbrock_saddle<Real>, jso_params, gen, std::numeric_limits<Real>::quiet_NaN(), &cancel);
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CHECK_GE(std::abs(local_minima[0] - Real(1)), std::sqrt(std::numeric_limits<Real>::epsilon()));
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}
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template <class Real> void test_rastrigin() {
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std::cout << "Testing jSO on Rastrigin function (global minimum = (0,0,...,0))\n";
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using ArgType = std::vector<Real>;
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auto jso_params = jso_parameters<ArgType>();
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jso_params.lower_bounds.resize(3, static_cast<Real>(-5.12));
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jso_params.upper_bounds.resize(3, static_cast<Real>(5.12));
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jso_params.initial_population_size = 5000;
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jso_params.max_function_evaluations = 1000000;
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std::mt19937_64 gen(34567);
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// By definition, the value of the function which a target value is provided must be <= target_value.
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Real target_value = 1e-3;
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auto local_minima = jso(rastrigin<Real>, jso_params, gen, target_value);
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CHECK_LE(rastrigin(local_minima), target_value);
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}
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// Tests NaN return types and return type != input type:
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void test_sphere() {
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std::cout << "Testing jSO on sphere . . .\n";
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using ArgType = std::vector<float>;
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auto jso_params = jso_parameters<ArgType>();
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jso_params.lower_bounds.resize(8, -1);
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jso_params.upper_bounds.resize(8, 1);
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std::mt19937_64 gen(56789);
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auto local_minima = jso(sphere, jso_params, gen);
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for (auto x : local_minima) {
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CHECK_ABSOLUTE_ERROR(0.0f, x, 2e-4f);
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}
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}
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template<typename Real>
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void test_three_hump_camel() {
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std::cout << "Testing jSO on three hump camel . . .\n";
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using ArgType = std::array<Real, 2>;
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auto jso_params = jso_parameters<ArgType>();
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jso_params.lower_bounds[0] = -5.0;
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jso_params.lower_bounds[1] = -5.0;
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jso_params.upper_bounds[0] = 5.0;
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jso_params.upper_bounds[1] = 5.0;
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std::mt19937_64 gen(56789);
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auto local_minima = jso(three_hump_camel<Real>, jso_params, gen);
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for (auto x : local_minima) {
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CHECK_ABSOLUTE_ERROR(0.0f, x, 2e-4f);
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}
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}
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template<typename Real>
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void test_beale() {
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std::cout << "Testing jSO on the Beale function . . .\n";
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using ArgType = std::array<Real, 2>;
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auto jso_params = jso_parameters<ArgType>();
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jso_params.lower_bounds[0] = -5.0;
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jso_params.lower_bounds[1] = -5.0;
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jso_params.upper_bounds[0]= 5.0;
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jso_params.upper_bounds[1]= 5.0;
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std::mt19937_64 gen(56789);
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auto local_minima = jso(beale<Real>, jso_params, gen);
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CHECK_ABSOLUTE_ERROR(Real(3), local_minima[0], Real(2e-4));
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CHECK_ABSOLUTE_ERROR(Real(1)/Real(2), local_minima[1], Real(2e-4));
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}
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#if BOOST_MATH_TEST_UNITS_COMPATIBILITY
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void test_dimensioned_sphere() {
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std::cout << "Testing jso on dimensioned sphere . . .\n";
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using ArgType = std::vector<quantity<length>>;
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auto params = jso_parameters<ArgType>();
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params.lower_bounds.resize(4, -1.0*meter);
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params.upper_bounds.resize(4, 1*meter);
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params.threads = 2;
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std::mt19937_64 gen(56789);
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auto local_minima = jso(dimensioned_sphere, params, gen);
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}
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#endif
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int main() {
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#if defined(__clang__) || defined(_MSC_VER)
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test_ackley<float>();
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test_ackley<double>();
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test_rosenbrock_saddle<double>();
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test_rastrigin<double>();
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test_three_hump_camel<float>();
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test_beale<double>();
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#endif
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#if BOOST_MATH_TEST_UNITS_COMPATIBILITY
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test_dimensioned_sphere();
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#endif
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test_sphere();
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test_weighted_lehmer_mean();
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return boost::math::test::report_errors();
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}
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