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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) */ #ifndef BOOST_MATH_OPTIMIZATION_RANDOM_SEARCH_HPP #define BOOST_MATH_OPTIMIZATION_RANDOM_SEARCH_HPP #include #include #include #include #include #include #include #include #include #include #include namespace boost::math::optimization { template struct random_search_parameters { using Real = typename ArgumentContainer::value_type; ArgumentContainer lower_bounds; ArgumentContainer upper_bounds; size_t max_function_calls = 10000*std::thread::hardware_concurrency(); ArgumentContainer const *initial_guess = nullptr; unsigned threads = std::thread::hardware_concurrency(); }; template void validate_random_search_parameters(random_search_parameters const ¶ms) { using std::isfinite; using std::isnan; std::ostringstream oss; detail::validate_bounds(params.lower_bounds, params.upper_bounds); if (params.initial_guess) { detail::validate_initial_guess(*params.initial_guess, params.lower_bounds, params.upper_bounds); } if (params.threads == 0) { oss << __FILE__ << ":" << __LINE__ << ":" << __func__; oss << ": There must be at least one thread."; throw std::invalid_argument(oss.str()); } } template ArgumentContainer random_search( const Func cost_function, random_search_parameters const ¶ms, URBG &gen, std::invoke_result_t target_value = std::numeric_limits>::quiet_NaN(), std::atomic *cancellation = nullptr, std::atomic> *current_minimum_cost = nullptr, std::vector>> *queries = nullptr) { using Real = typename ArgumentContainer::value_type; using DimensionlessReal = decltype(Real()/Real()); using ResultType = std::invoke_result_t; using std::isnan; using std::uniform_real_distribution; validate_random_search_parameters(params); const size_t dimension = params.lower_bounds.size(); std::atomic target_attained = false; // Unfortunately, the "minimum_cost" variable can either be passed // (for observability) or not (if the user doesn't care). // That makes this a bit awkward . . . std::atomic lowest_cost = std::numeric_limits::infinity(); ArgumentContainer best_vector; if constexpr (detail::has_resize_v) { best_vector.resize(dimension, std::numeric_limits::quiet_NaN()); } if (params.initial_guess) { auto initial_cost = cost_function(*params.initial_guess); if (!isnan(initial_cost)) { lowest_cost = initial_cost; best_vector = *params.initial_guess; if (current_minimum_cost) { *current_minimum_cost = initial_cost; } } } std::mutex mt; std::vector thread_pool; std::atomic function_calls = 0; for (unsigned j = 0; j < params.threads; ++j) { auto seed = gen(); thread_pool.emplace_back([&, seed]() { URBG g(seed); ArgumentContainer trial_vector; // This vector is empty unless the user requests the queries be stored: std::vector>> local_queries; if constexpr (detail::has_resize_v) { trial_vector.resize(dimension, std::numeric_limits::quiet_NaN()); } while (function_calls < params.max_function_calls) { if (cancellation && *cancellation) { break; } if (target_attained) { break; } // Fill trial vector: uniform_real_distribution unif01(DimensionlessReal(0), DimensionlessReal(1)); for (size_t i = 0; i < dimension; ++i) { trial_vector[i] = params.lower_bounds[i] + (params.upper_bounds[i] - params.lower_bounds[i])*unif01(g); } ResultType trial_cost = cost_function(trial_vector); ++function_calls; if (isnan(trial_cost)) { continue; } if (trial_cost < lowest_cost) { lowest_cost = trial_cost; if (current_minimum_cost) { *current_minimum_cost = trial_cost; } // We expect to need to acquire this lock with decreasing frequency // as the computation proceeds: std::scoped_lock lock(mt); best_vector = trial_vector; } if (queries) { local_queries.push_back(std::make_pair(trial_vector, trial_cost)); } if (!isnan(target_value) && trial_cost <= target_value) { target_attained = true; } } if (queries) { std::scoped_lock lock(mt); queries->insert(queries->begin(), local_queries.begin(), local_queries.end()); } }); } for (auto &thread : thread_pool) { thread.join(); } return best_vector; } } // namespace boost::math::optimization #endif