tmp
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@ -27,8 +27,8 @@
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#include "../lib/optimization.h"
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#include "../lib/optimization.h"
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#define M 100
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#define M 90
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#define N 90
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#define N 100
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// get random floating points
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// get random floating points
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double random_double(double l, double t)
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double random_double(double l, double t)
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@ -134,28 +134,30 @@ int main(int argc, char const *argv[])
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// 生成一组正演解 包含一些大值和一些小值
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// 生成一组正演解 包含一些大值和一些小值
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gctl::array<double> fm(N);
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gctl::array<double> fm(N);
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int N2 = (int) N/2;
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int N2 = (int) N/2;
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srand(time(0));
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for (int i = 0; i < N2; i++)
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for (int i = 0; i < N2; i++)
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{
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{
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//fm[i] = random_double(5, 10);
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fm[i] = random_double(5, 10);
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fm[i] = 10.0;
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//fm[i] = 10.0;
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}
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}
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for (int i = N2; i < N; i++)
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for (int i = N2; i < N; i++)
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{
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{
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//fm[i] = random_double(1, 2);
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fm[i] = random_double(1, 2);
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fm[i] = 1.0;
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//fm[i] = 1.0;
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}
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}
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for (int i = 0; i < N2; i++)
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for (int i = 0; i < N2; i++)
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{
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{
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low[i] = 9.0; // 对解的范围进行约束
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low[i] = 4.0; // 对解的范围进行约束
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hig[i] = 11.0;
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hig[i] = 11.0;
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}
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}
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for (int i = N2; i < N; i++)
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for (int i = N2; i < N; i++)
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{
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{
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low[i] = 0.0;
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low[i] = 0.0;
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hig[i] = 2.0;
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hig[i] = 3.0;
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}
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}
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ex2 e;
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ex2 e;
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@ -164,6 +166,8 @@ int main(int argc, char const *argv[])
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gctl::lgd_para my_para = e.default_lgd_para();
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gctl::lgd_para my_para = e.default_lgd_para();
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my_para.flight_times = 20000;
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my_para.flight_times = 20000;
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my_para.batch = 100;
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my_para.batch = 100;
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my_para.fmt = 0.5;
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my_para.smt = 0.05;
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e.set_lgd_para(my_para);
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e.set_lgd_para(my_para);
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e.LGD_Minimize(m, mean_m, stddev_m, low, hig);
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e.LGD_Minimize(m, mean_m, stddev_m, low, hig);
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@ -30,7 +30,7 @@
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/**
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/**
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* Default parameter for the Lévy-Gradient Descent (L-GD) method.
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* Default parameter for the Lévy-Gradient Descent (L-GD) method.
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*/
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*/
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static const gctl::lgd_para lgd_defparam = {1000, 0, 0, 1e-5, 1.0, 1.5, 0.01, 1e-8, -1.0};
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static const gctl::lgd_para lgd_defparam = {1000, 0, 0, 1e-5, 1.0, 1.5, 0.01, 1e-8, -1.0, 0.5, 0.05};
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gctl::lgd_solver::lgd_solver()
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gctl::lgd_solver::lgd_solver()
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{
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{
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@ -370,6 +370,62 @@ void gctl::lgd_solver::LGD_Minimize(array<double> &best_m, array<double> &mean_m
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return;
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return;
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}
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}
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void gctl::lgd_solver::LGD_Minimize_Momentum(array<double> &best_m, array<double> &mean_m, array<double> &std_m,
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std::ostream &ss, bool verbose, bool err_throw)
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{
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if (lgd_silent_)
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{
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lgd_return_code ret = lgd_momentum(best_m, mean_m, std_m);
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if (ret < 0) lgd_error_str(ret, ss, true);
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return;
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}
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// 使用lcg求解 注意当我们使用函数指针来调用求解函数时默认参数不可以省略
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#ifdef GCTL_OPENMP
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double start = omp_get_wtime();
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lgd_return_code ret = lgd_momentum(best_m, mean_m, std_m);
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double end = omp_get_wtime();
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double costime = 1000*(end-start);
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#else
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clock_t start = clock();
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lgd_return_code ret = lgd_momentum(best_m, mean_m, std_m);
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clock_t end = clock();
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double costime = 1000*(end-start)/(double)CLOCKS_PER_SEC;
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#endif
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if (!err_throw) std::clog << std::endl << "Solver: LGDM. Time cost: " << costime << " ms" << std::endl;
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if (verbose) lgd_error_str(ret, ss, err_throw);
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else if (ret < 0) lgd_error_str(ret, ss, err_throw);
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return;
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}
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void gctl::lgd_solver::LGD_Minimize_Momentum(array<double> &best_m, array<double> &mean_m, array<double> &std_m,
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const array<double> &lows, const array<double> &higs, std::ostream &ss, bool verbose, bool err_throw)
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{
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lgd_ques_num_ = best_m.size();
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if (lgd_ques_num_ != lows.size() || lgd_ques_num_ != higs.size())
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{
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throw std::runtime_error("[gctl::lgd_solver] arraies' size do not match.");
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}
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lgd_low_.resize(lgd_ques_num_);
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lgd_hig_.resize(lgd_ques_num_);
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for (int i = 0; i < lgd_ques_num_; i++)
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{
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if (lows[i] >= higs[i])
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{
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throw std::runtime_error("[gctl::lgd_solver] Invalid bound value.");
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}
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lgd_low_[i] = lows[i];
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lgd_hig_[i] = higs[i];
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}
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lgd_has_range_ = true;
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LGD_Minimize_Momentum(best_m, mean_m, std_m, ss, verbose, err_throw);
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return;
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}
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gctl::lgd_return_code gctl::lgd_solver::lgd(array<double> &best_m, array<double> &mean_m, array<double> &std_m)
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gctl::lgd_return_code gctl::lgd_solver::lgd(array<double> &best_m, array<double> &mean_m, array<double> &std_m)
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{
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{
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lgd_ques_num_ = best_m.size();
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lgd_ques_num_ = best_m.size();
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@ -534,3 +590,177 @@ gctl::lgd_return_code gctl::lgd_solver::lgd(array<double> &best_m, array<double>
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veccpy(best_m, b_m);
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veccpy(best_m, b_m);
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return LGD_REACHED_MAX_ITERATIONS;
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return LGD_REACHED_MAX_ITERATIONS;
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}
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}
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gctl::lgd_return_code gctl::lgd_solver::lgd_momentum(array<double> &best_m, array<double> &mean_m, array<double> &std_m)
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{
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lgd_ques_num_ = best_m.size();
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// check parameters
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if (lgd_ques_num_ <= 0) return LGD_INVALID_SOLUTION_SIZE;
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if (lgd_param_.flight_times <= 0) return LGD_INVALID_MAX_ITERATIONS;
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if (lgd_param_.epsilon <= 0) return LGD_INVALID_EPSILON;
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if (lgd_param_.stddev_v <= 0) return LGD_INVALID_STDV;
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if (lgd_param_.beta <= 1.0 || lgd_param_.beta >= 2.0) return LGD_INVALID_BETA;
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if (lgd_param_.alpha <= 0) return LGD_INVALID_ALPHA;
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if (lgd_param_.sigma <= 0) return LGD_INVALID_SIGMA;
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// initiate solutions
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mean_m.resize(lgd_ques_num_, 0.0); std_m.resize(lgd_ques_num_, 0.0);
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double gamma1 = tgamma(lgd_param_.beta + 1.0);
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double gamma2 = tgamma(0.5*(lgd_param_.beta + 1.0));
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double stddev_u = pow((gamma1*sin(0.5*GCTL_Pi*lgd_param_.beta)) / (gamma2*lgd_param_.beta*pow(2, 0.5*(lgd_param_.beta-1.0))), 1.0/lgd_param_.beta);
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unsigned int sd;
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if (lgd_param_.seed == 0) sd = std::chrono::system_clock::now().time_since_epoch().count();
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else sd = lgd_param_.seed;
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std::default_random_engine generator(sd);
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std::normal_distribution<double> dist_u(0, stddev_u);
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std::normal_distribution<double> dist_v(0, lgd_param_.stddev_v);
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std::uniform_real_distribution<double> dist_s(1.0, 2.0);
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array<double> g, g_mem, g_orth, new_mean, b_m, alphas, fst_mt, sec_mt;
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g.resize(lgd_ques_num_); g_mem.resize(2*lgd_ques_num_); g_orth.resize(2*lgd_ques_num_);
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new_mean.resize(lgd_ques_num_); b_m.resize(lgd_ques_num_); alphas.resize(lgd_ques_num_);
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double mt = 1.0, vt = 1.0;
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fst_mt.resize(lgd_ques_num_, 0.0);
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sec_mt.resize(lgd_ques_num_, 0.0);
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// 初始化参数变化范围为lgd_param_.alpha
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vecset(alphas, lgd_param_.alpha);
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if (lgd_has_range_)
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{
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vecdiff(alphas, lgd_hig_, lgd_low_);
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vecscale(alphas, lgd_param_.alpha);
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}
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if (lgd_has_alpha_)
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{
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veccpy(alphas, lgd_alpha_, lgd_param_.alpha);
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}
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double fx_best, fx_tmp, direct_mod, levy_length;
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double fx_mean = NAN;
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// 开始飞行
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int rcd_times = 0;
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lgd_trace_times_ = 0;
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for (int ft = 0; ft <= lgd_param_.flight_times; ft++)
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{
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// 计算尝试解
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fx_tmp = LGD_Evaluate(best_m, g);
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if (ft == 0 || fx_tmp < fx_best)
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{
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fx_best = fx_tmp;
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veccpy(b_m, best_m);
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}
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// 记录飞行轨迹
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if (lgd_param_.lambda <= 0.0 || (lgd_param_.lambda > 0.0 && fx_tmp <= lgd_param_.lambda))
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{
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for (int i = 0; i < lgd_ques_num_; i++)
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{
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std_m[i] = dynamic_stddev(std_m[i], rcd_times, mean_m[i], best_m[i], new_mean[i]);
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mean_m[i] = new_mean[i];
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}
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rcd_times++;
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if (lgd_save_trace_)
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{
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lgd_trace_.append_array(best_m);
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lgd_trace_times_++;
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}
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}
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if (LGD_Progress(ft, fx_tmp, fx_mean, fx_best, lgd_param_))
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{
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// 将迭代结果返还给m
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veccpy(best_m, b_m);
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return LGD_STOP;
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}
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if (lgd_param_.batch > 0 && (rcd_times+1)%lgd_param_.batch == 0)
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{
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fx_mean = LGD_Evaluate(mean_m, g);
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if (fx_mean < lgd_param_.epsilon)
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{
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LGD_Progress(ft, fx_tmp, fx_mean, fx_best, lgd_param_);
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// 将迭代结果返还给m
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veccpy(best_m, b_m);
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return LGD_CONVERGENCE;
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}
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}
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// 驻点检测
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direct_mod = sqrt(vecdot(g, g));
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if (direct_mod < lgd_param_.sigma)
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{
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if (ft == 0) // 初次飞行时无记录
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{
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do // 如果梯度消失 则采用一个随机方向
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{
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for (int i = 0; i < lgd_ques_num_; i++)
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{
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g[i] = dist_s(generator);
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}
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direct_mod = sqrt(vecdot(g, g));
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}
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while (direct_mod < lgd_param_.sigma);
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}
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else // 如果梯度消失 则朝着上一次迭代方向的正交方向走一步
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{
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for (int i = 0; i < lgd_ques_num_; i++)
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{
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g_mem[i+lgd_ques_num_] = dist_s(generator);
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}
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schmidt_orthogonal(g_mem, g_orth, 2);
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for (int i = 0; i < lgd_ques_num_; i++)
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{
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g[i] = g_orth[i+lgd_ques_num_];
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}
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direct_mod = 1.0; // 此时的模量为单位模量
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}
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}
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// 莱维飞行的步长 注意原公式中无最外层绝对值符号
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// 这是我们需要步长的绝对值 因此取绝对值
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levy_length = fabs(dist_u(generator)/pow(fabs(dist_v(generator)), 1.0/lgd_param_.beta));
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mt *= lgd_param_.fmt;
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vt *= lgd_param_.smt;
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for (int i = 0; i < lgd_ques_num_; i++)
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{
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g[i] = levy_length*alphas[i]*g[i]/direct_mod;
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fst_mt[i] = (lgd_param_.fmt*fst_mt[i] + (1.0 - lgd_param_.fmt)*g[i])/(1.0 - mt);
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sec_mt[i] = (lgd_param_.smt*sec_mt[i] + (1.0 - lgd_param_.smt)*g[i]*g[i])/(1.0 - vt);
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best_m[i] -= fst_mt[i]/(sqrt(sec_mt[i]) + 1e-10);
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}
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if (!vecvalid(best_m))
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{
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return LGD_NAN_VALUE;
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}
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// 记录梯度方向
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for (int i = 0; i < lgd_ques_num_; i++)
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{
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g_mem[i] = g[i];
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}
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// 这里可以添加取值范围的约束
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if (lgd_has_range_)
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{
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vecbtm(best_m, lgd_low_);
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vectop(best_m, lgd_hig_);
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}
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}
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// 将迭代结果返还给m
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veccpy(best_m, b_m);
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return LGD_REACHED_MAX_ITERATIONS;
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}
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@ -138,8 +138,24 @@ namespace gctl
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* is set. The default is -1.0.
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* is set. The default is -1.0.
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*/
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*/
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double lambda;
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double lambda;
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/**
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* @brief First order moment.
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*
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*/
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double fmt;
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/**
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* @brief Second order moment.
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*
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*/
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double smt;
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};
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};
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/**
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* @brief The lévy gradient descent solver (LGD).
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*
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*/
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class lgd_solver
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class lgd_solver
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{
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{
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||||||
public:
|
public:
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@ -169,13 +185,39 @@ namespace gctl
|
|||||||
virtual int LGD_Progress(const int curr_t, const double curr_fx, const double mean_fx,
|
virtual int LGD_Progress(const int curr_t, const double curr_fx, const double mean_fx,
|
||||||
const double best_fx, const lgd_para ¶m);
|
const double best_fx, const lgd_para ¶m);
|
||||||
|
|
||||||
|
/**
|
||||||
|
* @brief Susppend all output info.
|
||||||
|
*
|
||||||
|
*/
|
||||||
void lgd_silent();
|
void lgd_silent();
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||||||
|
|
||||||
|
/**
|
||||||
|
* @brief Set interval to run the LGD_Progress function.
|
||||||
|
*
|
||||||
|
* @param inter
|
||||||
|
*/
|
||||||
void set_lgd_report_interval(int inter);
|
void set_lgd_report_interval(int inter);
|
||||||
|
|
||||||
|
/**
|
||||||
|
* @brief Show solver setup.
|
||||||
|
*
|
||||||
|
*/
|
||||||
void show_solver();
|
void show_solver();
|
||||||
|
|
||||||
void set_lgd_record_trace(); ///< Turn on the recording of fight traces.
|
/**
|
||||||
// Save fight traces to file. Not that only qualified solutions will be
|
* @brief Turn on the recording of fight traces. Use this with caution as it may use a lot of momeries.
|
||||||
// saved if the recording threshold is set.
|
*
|
||||||
|
*/
|
||||||
|
void set_lgd_record_trace();
|
||||||
|
|
||||||
|
/**
|
||||||
|
* @brief Save fight traces to file.
|
||||||
|
*
|
||||||
|
* @note Must turn on the trace recording for this function to work properly.
|
||||||
|
* Only qualified solutions will be saved if the recording threshold is set.
|
||||||
|
*
|
||||||
|
* @param trace_file output file.
|
||||||
|
*/
|
||||||
void save_lgd_trace(std::string trace_file);
|
void save_lgd_trace(std::string trace_file);
|
||||||
|
|
||||||
/**
|
/**
|
||||||
@ -227,9 +269,17 @@ namespace gctl
|
|||||||
double low, double hig, std::ostream &ss = std::clog, bool verbose = true,
|
double low, double hig, std::ostream &ss = std::clog, bool verbose = true,
|
||||||
bool err_throw = false);
|
bool err_throw = false);
|
||||||
|
|
||||||
|
void LGD_Minimize_Momentum(array<double> &best_m, array<double> &mean_m, array<double> &std_m,
|
||||||
|
std::ostream &ss = std::clog, bool verbose = true, bool err_throw = false);
|
||||||
|
|
||||||
|
void LGD_Minimize_Momentum(array<double> &best_m, array<double> &mean_m, array<double> &std_m,
|
||||||
|
const array<double> &lows, const array<double> &higs, std::ostream &ss = std::clog,
|
||||||
|
bool verbose = true, bool err_throw = false);
|
||||||
|
|
||||||
private:
|
private:
|
||||||
void lgd_error_str(lgd_return_code err_code, std::ostream &ss = std::clog, bool err_throw = false);
|
void lgd_error_str(lgd_return_code err_code, std::ostream &ss = std::clog, bool err_throw = false);
|
||||||
lgd_return_code lgd(array<double> &best_m, array<double> &mean_m, array<double> &std_m);
|
lgd_return_code lgd(array<double> &best_m, array<double> &mean_m, array<double> &std_m);
|
||||||
|
lgd_return_code lgd_momentum(array<double> &best_m, array<double> &mean_m, array<double> &std_m);
|
||||||
|
|
||||||
private:
|
private:
|
||||||
lgd_para lgd_param_;
|
lgd_para lgd_param_;
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user