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de9a1a0ee5
Through a combination of silly mistakes, I missed a pile of race conditions in the OpenMP threading. Switch to C++ threading. Note that this change requires serial generation of trial vectors. Hopefully I can figure out to parallelize the generation of trial vectors to reduce the serial section a la Ahmdahl's law, while simultaneously keeping thread sanitizer happy.
42 lines
1.5 KiB
C++
42 lines
1.5 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 <iostream>
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#include <boost/math/optimization/differential_evolution.hpp>
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using boost::math::optimization::differential_evolution_parameters;
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using boost::math::optimization::differential_evolution;
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double rosenbrock(std::vector<double> const & x) {
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double result = 0;
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for (size_t i = 0; i < x.size() - 1; ++i) {
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double tmp = x[i+1] - x[i]*x[i];
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result += 100*tmp*tmp + (1-x[i])*(1-x[i]);
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}
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return result;
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}
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int main() {
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auto de_params = differential_evolution_parameters<std::vector<double>>();
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constexpr const size_t dimension = 10;
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// Search on [0, 2]^dimension:
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de_params.lower_bounds.resize(dimension, 0);
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de_params.upper_bounds.resize(dimension, 2);
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// This is a challenging function, increase the max generations 10x from default so we don't terminate prematurely:
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de_params.max_generations *= 10;
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std::random_device rd;
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std::mt19937_64 rng(rd());
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// The global minima is exactly zero-but some leeway is required:
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double value_to_reach = 1e-5;
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auto local_minima = differential_evolution(rosenbrock, de_params, rng, value_to_reach);
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std::cout << "Minima: {";
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for (auto l : local_minima) {
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std::cout << l << ", ";
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}
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std::cout << "}\n";
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std::cout << "Value of cost function at minima: " << rosenbrock(local_minima) << "\n";
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}
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