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221 lines
8.3 KiB
C++
221 lines
8.3 KiB
C++
/*
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* Copyright Nick Thompson, 2023
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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/differential_evolution.hpp>
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#include <random>
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using boost::math::optimization::differential_evolution;
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using boost::math::optimization::differential_evolution_parameters;
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using boost::math::optimization::validate_differential_evolution_parameters;
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using boost::math::optimization::detail::best_indices;
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using boost::math::optimization::detail::random_initial_population;
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using boost::math::optimization::detail::validate_initial_guess;
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void test_random_initial_population() {
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std::array<double, 2> lower_bounds = {-5, -5};
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std::array<double, 2> upper_bounds = {5, 5};
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size_t n = 500;
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std::mt19937_64 gen(12345);
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auto population = random_initial_population(lower_bounds, upper_bounds, n, gen);
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CHECK_EQUAL(population.size(), n);
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for (auto const & individual : population) {
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validate_initial_guess(individual, lower_bounds, upper_bounds);
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}
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// Reproducibility:
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gen.seed(12345);
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auto population2 = random_initial_population(lower_bounds, upper_bounds, n, gen);
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for (size_t i = 0; i < n; ++i) {
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for (size_t j = 0; j < 2; ++j) {
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CHECK_EQUAL(population[i][j], population2[i][j]);
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}
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}
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}
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void test_nan_sorting() {
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auto nan = std::numeric_limits<double>::quiet_NaN();
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std::vector<double> v{-1.2, nan, -3.5, 2.3, nan, 8.7, -4.2};
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auto indices = best_indices(v);
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CHECK_EQUAL(indices[0], size_t(6));
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CHECK_EQUAL(indices[1], size_t(2));
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CHECK_EQUAL(indices[2], size_t(0));
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CHECK_EQUAL(indices[3], size_t(3));
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CHECK_EQUAL(indices[4], size_t(5));
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CHECK_NAN(v[indices[5]]);
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CHECK_NAN(v[indices[6]]);
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}
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void test_parameter_checks() {
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using ArgType = std::array<double, 2>;
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auto de_params = differential_evolution_parameters<ArgType>();
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de_params.threads = 0;
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bool caught = false;
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try {
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validate_differential_evolution_parameters(de_params);
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} catch(std::exception const &) {
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caught = true;
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}
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CHECK_TRUE(caught);
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caught = false;
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de_params = differential_evolution_parameters<ArgType>();
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de_params.NP = 1;
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try {
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validate_differential_evolution_parameters(de_params);
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} catch(std::exception const &) {
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caught = true;
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}
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CHECK_TRUE(caught);
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}
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template <class Real> void test_ackley() {
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std::cout << "Testing differential evolution on the Ackley function . . .\n";
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using ArgType = std::array<Real, 2>;
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auto de_params = differential_evolution_parameters<ArgType>();
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de_params.lower_bounds = {-5, -5};
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de_params.upper_bounds = {5, 5};
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std::mt19937_64 gen(12345);
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auto local_minima = differential_evolution(ackley<Real>, de_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 = differential_evolution(ack, de_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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de_params.initial_guess = &initial_guess;
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local_minima = differential_evolution(ack, de_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 differential evolution on the Rosenbrock saddle . . .\n";
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using ArgType = std::array<Real, 2>;
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auto de_params = differential_evolution_parameters<ArgType>();
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de_params.lower_bounds = {0.5, 0.5};
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de_params.upper_bounds = {2.048, 2.048};
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std::mt19937_64 gen(234568);
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auto local_minima = differential_evolution(rosenbrock_saddle<Real>, de_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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differential_evolution(rosenbrock_saddle<Real>, de_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 differential evolution on the Rastrigin function . . .\n";
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using ArgType = std::vector<Real>;
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auto de_params = differential_evolution_parameters<ArgType>();
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de_params.lower_bounds.resize(8, static_cast<Real>(-5.12));
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de_params.upper_bounds.resize(8, static_cast<Real>(5.12));
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std::mt19937_64 gen(34567);
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auto local_minima = differential_evolution(rastrigin<Real>, de_params, gen);
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for (auto x : local_minima) {
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CHECK_ABSOLUTE_ERROR(x, Real(0), Real(2e-4));
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}
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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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auto target_value = static_cast<Real>(1e-3);
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local_minima = differential_evolution(rastrigin<Real>, de_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 differential evolution on the sphere function . . .\n";
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using ArgType = std::vector<float>;
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auto de_params = differential_evolution_parameters<ArgType>();
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de_params.lower_bounds.resize(3, -1);
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de_params.upper_bounds.resize(3, 1);
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de_params.NP *= 10;
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de_params.max_generations *= 10;
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de_params.crossover_probability = 0.9;
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double target_value = 1e-8;
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de_params.threads = 1;
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std::mt19937_64 gen(56789);
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auto local_minima = differential_evolution(sphere, de_params, gen, target_value);
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CHECK_LE(sphere(local_minima), target_value);
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// Check computational reproducibility:
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gen.seed(56789);
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auto local_minima_2 = differential_evolution(sphere, de_params, gen, target_value);
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for (size_t i = 0; i < local_minima.size(); ++i) {
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CHECK_EQUAL(local_minima[i], local_minima_2[i]);
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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 differential evolution on the three hump camel . . .\n";
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using ArgType = std::array<Real, 2>;
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auto de_params = differential_evolution_parameters<ArgType>();
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de_params.lower_bounds[0] = -5.0;
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de_params.lower_bounds[1] = -5.0;
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de_params.upper_bounds[0] = 5.0;
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de_params.upper_bounds[1] = 5.0;
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std::mt19937_64 gen(56789);
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auto local_minima = differential_evolution(three_hump_camel<Real>, de_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 differential evolution on the Beale function . . .\n";
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using ArgType = std::array<Real, 2>;
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auto de_params = differential_evolution_parameters<ArgType>();
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de_params.lower_bounds[0] = -5.0;
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de_params.lower_bounds[1] = -5.0;
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de_params.upper_bounds[0]= 5.0;
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de_params.upper_bounds[1]= 5.0;
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std::mt19937_64 gen(56789);
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auto local_minima = differential_evolution(beale<Real>, de_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 differential evolution on dimensioned sphere . . .\n";
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using ArgType = std::vector<quantity<length>>;
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auto params = differential_evolution_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 = differential_evolution(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<float>();
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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_parameter_checks();
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return boost::math::test::report_errors();
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}
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