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/*
* Copyright Nick Thompson, 2024
* Use, modification and distribution are subject to the
* Boost Software License, Version 1.0. (See accompanying file
* LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
*/
#include "math_unit_test.hpp"
#include "test_functions_for_optimization.hpp"
#include <boost/math/optimization/random_search.hpp>
#include <random>
#include <limits>
using boost::math::optimization::random_search;
using boost::math::optimization::random_search_parameters;
template <class Real> void test_ackley() {
std::cout << "Testing random search on Ackley function . . .\n";
using ArgType = std::array<Real, 2>;
auto rs_params = random_search_parameters<ArgType>();
rs_params.lower_bounds = {-5, -5};
rs_params.upper_bounds = {5, 5};
// This makes the CI a bit more robust;
// the computation is only deterministic with a deterministic number of threads:
rs_params.threads = 2;
rs_params.max_function_calls *= 10;
std::mt19937_64 gen(12345);
auto local_minima = random_search(ackley<Real>, rs_params, gen);
CHECK_LE(std::abs(local_minima[0]), Real(0.2));
CHECK_LE(std::abs(local_minima[1]), Real(0.2));
// Does it work with a lambda?
auto ack = [](std::array<Real, 2> const &x) { return ackley<Real>(x); };
local_minima = random_search(ack, rs_params, gen);
CHECK_LE(std::abs(local_minima[0]), Real(0.2));
CHECK_LE(std::abs(local_minima[1]), Real(0.2));
// Test that if an intial guess is the exact solution, the returned solution is the exact solution:
std::array<Real, 2> initial_guess{0, 0};
rs_params.initial_guess = &initial_guess;
local_minima = random_search(ack, rs_params, gen);
CHECK_EQUAL(local_minima[0], Real(0));
CHECK_EQUAL(local_minima[1], Real(0));
std::atomic<bool> cancel = false;
Real target_value = 0.0;
std::atomic<Real> current_minimum_cost = std::numeric_limits<Real>::quiet_NaN();
// Test query storage:
std::vector<std::pair<ArgType, Real>> queries;
local_minima = random_search(ack, rs_params, gen, target_value, &cancel, ¤t_minimum_cost, &queries);
CHECK_EQUAL(local_minima[0], Real(0));
CHECK_EQUAL(local_minima[1], Real(0));
CHECK_LE(size_t(1), queries.size());
for (auto const & q : queries) {
auto expected = ackley<Real>(q.first);
CHECK_EQUAL(expected, q.second);
}
}
template <class Real> void test_rosenbrock_saddle() {
std::cout << "Testing random search on Rosenbrock saddle . . .\n";
using ArgType = std::array<Real, 2>;
auto rs_params = random_search_parameters<ArgType>();
rs_params.lower_bounds = {0.5, 0.5};
rs_params.upper_bounds = {2.048, 2.048};
rs_params.max_function_calls = 20000;
rs_params.threads = 2;
std::mt19937_64 gen(234568);
auto local_minima = random_search(rosenbrock_saddle<Real>, rs_params, gen);
CHECK_ABSOLUTE_ERROR(Real(1), local_minima[0], Real(0.05));
CHECK_ABSOLUTE_ERROR(Real(1), local_minima[1], Real(0.05));
// Does cancellation work?
std::atomic<bool> cancel = true;
gen.seed(12345);
local_minima =
random_search(rosenbrock_saddle<Real>, rs_params, gen, std::numeric_limits<Real>::quiet_NaN(), &cancel);
CHECK_GE(std::abs(local_minima[0] - Real(1)), std::sqrt(std::numeric_limits<Real>::epsilon()));
}
template <class Real> void test_rastrigin() {
std::cout << "Testing random search on Rastrigin function (global minimum = (0,0,...,0))\n";
using ArgType = std::vector<Real>;
auto rs_params = random_search_parameters<ArgType>();
rs_params.lower_bounds.resize(3, static_cast<Real>(-5.12));
rs_params.upper_bounds.resize(3, static_cast<Real>(5.12));
rs_params.max_function_calls = 1000000;
rs_params.threads = 2;
std::mt19937_64 gen(34567);
// By definition, the value of the function which a target value is provided must be <= target_value.
Real target_value = 2.0;
auto local_minima = random_search(rastrigin<Real>, rs_params, gen, target_value);
CHECK_LE(rastrigin(local_minima), target_value);
}
// Tests NaN return types and return type != input type:
void test_sphere() {
std::cout << "Testing random search on sphere . . .\n";
using ArgType = std::vector<float>;
auto rs_params = random_search_parameters<ArgType>();
rs_params.lower_bounds.resize(4, -1);
rs_params.upper_bounds.resize(4, 1);
rs_params.max_function_calls = 100000;
rs_params.threads = 2;
std::mt19937_64 gen(56789);
auto local_minima = random_search(sphere, rs_params, gen);
for (auto x : local_minima) {
CHECK_ABSOLUTE_ERROR(0.0f, x, 0.5f);
}
}
template<typename Real>
void test_three_hump_camel() {
std::cout << "Testing random search on three hump camel . . .\n";
using ArgType = std::array<Real, 2>;
auto rs_params = random_search_parameters<ArgType>();
rs_params.lower_bounds[0] = -5.0;
rs_params.lower_bounds[1] = -5.0;
rs_params.upper_bounds[0] = 5.0;
rs_params.upper_bounds[1] = 5.0;
rs_params.threads = 2;
rs_params.max_function_calls *= 10;
std::mt19937_64 gen(56789);
auto local_minima = random_search(three_hump_camel<Real>, rs_params, gen);
for (auto x : local_minima) {
CHECK_ABSOLUTE_ERROR(0.0f, x, 0.2f);
}
}
template<typename Real>
void test_beale() {
std::cout << "Testing random search on the Beale function . . .\n";
using ArgType = std::array<Real, 2>;
auto rs_params = random_search_parameters<ArgType>();
rs_params.lower_bounds[0] = -5.0;
rs_params.lower_bounds[1] = -5.0;
rs_params.upper_bounds[0]= 5.0;
rs_params.upper_bounds[1]= 5.0;
rs_params.threads = 2;
rs_params.max_function_calls *= 10;
std::mt19937_64 gen(56789);
auto local_minima = random_search(beale<Real>, rs_params, gen);
CHECK_ABSOLUTE_ERROR(Real(3), local_minima[0], Real(0.1));
CHECK_ABSOLUTE_ERROR(Real(1)/Real(2), local_minima[1], Real(0.1));
}
int main() {
#if defined(__clang__) || defined(_MSC_VER)
test_ackley<float>();
test_ackley<double>();
test_rosenbrock_saddle<double>();
test_rastrigin<double>();
test_three_hump_camel<float>();
test_beale<double>();
#endif
test_sphere();
return boost::math::test::report_errors();
}
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