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/////////////////////////////////////////////////////////////////////////////// |
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// BSD 3-Clause License |
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// |
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// Copyright (C) 2019-2024, LAAS-CNRS, University of Edinburgh, |
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// Heriot-Watt University, University of Oxford |
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// Copyright note valid unless otherwise stated in individual files. |
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// All rights reserved. |
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/////////////////////////////////////////////////////////////////////////////// |
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#ifdef CROCODDYL_WITH_MULTITHREADING |
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#include <omp.h> |
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#endif // CROCODDYL_WITH_MULTITHREADING |
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#include "crocoddyl/core/solver-base.hpp" |
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namespace crocoddyl { |
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SolverAbstract::SolverAbstract(std::shared_ptr<ShootingProblem> problem) |
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: problem_(problem), |
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is_feasible_(false), |
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was_feasible_(false), |
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cost_(0.), |
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merit_(0.), |
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stop_(0.), |
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dV_(0.), |
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dPhi_(0.), |
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dVexp_(0.), |
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dPhiexp_(0.), |
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dfeas_(0.), |
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feas_(0.), |
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ffeas_(0.), |
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gfeas_(0.), |
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hfeas_(0.), |
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ffeas_try_(0.), |
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gfeas_try_(0.), |
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hfeas_try_(0.), |
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preg_(0.), |
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dreg_(0.), |
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steplength_(1.), |
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th_acceptstep_(0.1), |
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th_stop_(1e-9), |
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th_gaptol_(1e-16), |
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feasnorm_(LInf), |
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iter_(0), |
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tmp_feas_(0.) { |
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// Allocate common data |
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const std::size_t ndx = problem_->get_ndx(); |
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const std::size_t T = problem_->get_T(); |
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const std::size_t ng_T = problem_->get_terminalModel()->get_ng_T(); |
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xs_.resize(T + 1); |
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us_.resize(T); |
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fs_.resize(T + 1); |
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g_adj_.resize(T + 1); |
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const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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for (std::size_t t = 0; t < T; ++t) { |
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const std::shared_ptr<ActionModelAbstract>& model = models[t]; |
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const std::size_t nu = model->get_nu(); |
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const std::size_t ng = model->get_ng(); |
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xs_[t] = model->get_state()->zero(); |
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us_[t] = Eigen::VectorXd::Zero(nu); |
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fs_[t] = Eigen::VectorXd::Zero(ndx); |
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g_adj_[t] = Eigen::VectorXd::Zero(ng); |
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} |
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xs_.back() = problem_->get_terminalModel()->get_state()->zero(); |
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fs_.back() = Eigen::VectorXd::Zero(ndx); |
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g_adj_.back() = Eigen::VectorXd::Zero(ng_T); |
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} |
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SolverAbstract::~SolverAbstract() {} |
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void SolverAbstract::resizeData() { |
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START_PROFILER("SolverAbstract::resizeData"); |
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const std::size_t T = problem_->get_T(); |
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const std::size_t ng_T = problem_->get_terminalModel()->get_ng_T(); |
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const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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for (std::size_t t = 0; t < T; ++t) { |
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const std::shared_ptr<ActionModelAbstract>& model = models[t]; |
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const std::size_t nu = model->get_nu(); |
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const std::size_t ng = model->get_ng(); |
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us_[t].conservativeResize(nu); |
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g_adj_[t].conservativeResize(ng); |
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} |
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g_adj_.back().conservativeResize(ng_T); |
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STOP_PROFILER("SolverAbstract::resizeData"); |
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} |
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double SolverAbstract::computeDynamicFeasibility() { |
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tmp_feas_ = 0.; |
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const std::size_t T = problem_->get_T(); |
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const Eigen::VectorXd& x0 = problem_->get_x0(); |
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const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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const std::vector<std::shared_ptr<ActionDataAbstract> >& datas = |
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problem_->get_runningDatas(); |
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models[0]->get_state()->diff(xs_[0], x0, fs_[0]); |
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#ifdef CROCODDYL_WITH_MULTITHREADING |
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#pragma omp parallel for num_threads(problem_->get_nthreads()) |
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#endif |
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for (std::size_t t = 0; t < T; ++t) { |
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const std::shared_ptr<ActionModelAbstract>& m = models[t]; |
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const std::shared_ptr<ActionDataAbstract>& d = datas[t]; |
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m->get_state()->diff(xs_[t + 1], d->xnext, fs_[t + 1]); |
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} |
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switch (feasnorm_) { |
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case LInf: |
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tmp_feas_ = std::max(tmp_feas_, fs_[0].lpNorm<Eigen::Infinity>()); |
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for (std::size_t t = 0; t < T; ++t) { |
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tmp_feas_ = std::max(tmp_feas_, fs_[t + 1].lpNorm<Eigen::Infinity>()); |
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} |
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break; |
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case L1: |
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tmp_feas_ = fs_[0].lpNorm<1>(); |
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for (std::size_t t = 0; t < T; ++t) { |
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tmp_feas_ += fs_[t + 1].lpNorm<1>(); |
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} |
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break; |
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} |
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return tmp_feas_; |
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} |
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double SolverAbstract::computeInequalityFeasibility() { |
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tmp_feas_ = 0.; |
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const std::size_t T = problem_->get_T(); |
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const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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const std::vector<std::shared_ptr<ActionDataAbstract> >& datas = |
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problem_->get_runningDatas(); |
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switch (feasnorm_) { |
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case LInf: |
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for (std::size_t t = 0; t < T; ++t) { |
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if (models[t]->get_ng() > 0) { |
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g_adj_[t] = datas[t] |
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->g.cwiseMax(models[t]->get_g_lb()) |
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.cwiseMin(models[t]->get_g_ub()); |
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tmp_feas_ = std::max( |
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tmp_feas_, (datas[t]->g - g_adj_[t]).lpNorm<Eigen::Infinity>()); |
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} |
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} |
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if (problem_->get_terminalModel()->get_ng_T() > 0) { |
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g_adj_.back() = |
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problem_->get_terminalData() |
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->g.cwiseMax(problem_->get_terminalModel()->get_g_lb()) |
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.cwiseMin(problem_->get_terminalModel()->get_g_ub()); |
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tmp_feas_ += (problem_->get_terminalData()->g - g_adj_.back()) |
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.lpNorm<Eigen::Infinity>(); |
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} |
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break; |
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case L1: |
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for (std::size_t t = 0; t < T; ++t) { |
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if (models[t]->get_ng() > 0) { |
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g_adj_[t] = datas[t] |
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->g.cwiseMax(models[t]->get_g_lb()) |
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.cwiseMin(models[t]->get_g_ub()); |
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tmp_feas_ = |
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std::max(tmp_feas_, (datas[t]->g - g_adj_[t]).lpNorm<1>()); |
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} |
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} |
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if (problem_->get_terminalModel()->get_ng_T() > 0) { |
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g_adj_.back() = |
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problem_->get_terminalData() |
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->g.cwiseMax(problem_->get_terminalModel()->get_g_lb()) |
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.cwiseMin(problem_->get_terminalModel()->get_g_ub()); |
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tmp_feas_ += |
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(problem_->get_terminalData()->g - g_adj_.back()).lpNorm<1>(); |
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} |
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break; |
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} |
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return tmp_feas_; |
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} |
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double SolverAbstract::computeEqualityFeasibility() { |
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tmp_feas_ = 0.; |
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const std::size_t T = problem_->get_T(); |
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const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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const std::vector<std::shared_ptr<ActionDataAbstract> >& datas = |
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problem_->get_runningDatas(); |
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switch (feasnorm_) { |
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case LInf: |
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for (std::size_t t = 0; t < T; ++t) { |
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if (models[t]->get_nh() > 0) { |
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tmp_feas_ = |
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std::max(tmp_feas_, datas[t]->h.lpNorm<Eigen::Infinity>()); |
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} |
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} |
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if (problem_->get_terminalModel()->get_nh_T() > 0) { |
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tmp_feas_ = |
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std::max(tmp_feas_, |
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problem_->get_terminalData()->h.lpNorm<Eigen::Infinity>()); |
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} |
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break; |
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case L1: |
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for (std::size_t t = 0; t < T; ++t) { |
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if (models[t]->get_nh() > 0) { |
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tmp_feas_ += datas[t]->h.lpNorm<1>(); |
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} |
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} |
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if (problem_->get_terminalModel()->get_nh_T() > 0) { |
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tmp_feas_ += problem_->get_terminalData()->h.lpNorm<1>(); |
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} |
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break; |
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} |
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return tmp_feas_; |
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} |
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void SolverAbstract::setCandidate(const std::vector<Eigen::VectorXd>& xs_warm, |
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const std::vector<Eigen::VectorXd>& us_warm, |
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bool is_feasible) { |
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const std::size_t T = problem_->get_T(); |
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const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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if (xs_warm.size() == 0) { |
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for (std::size_t t = 0; t < T; ++t) { |
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const std::shared_ptr<ActionModelAbstract>& model = models[t]; |
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xs_[t] = model->get_state()->zero(); |
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} |
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xs_.back() = problem_->get_terminalModel()->get_state()->zero(); |
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} else { |
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if (xs_warm.size() != T + 1) { |
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throw_pretty("Warm start state vector has wrong dimension, got " |
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<< xs_warm.size() << " expecting " << (T + 1)); |
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} |
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for (std::size_t t = 0; t < T; ++t) { |
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const std::size_t nx = models[t]->get_state()->get_nx(); |
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if (static_cast<std::size_t>(xs_warm[t].size()) != nx) { |
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throw_pretty("Invalid argument: " |
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<< "xs_init[" + std::to_string(t) + |
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"] has wrong dimension (" |
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<< xs_warm[t].size() |
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<< " provided - it should be equal to " + |
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std::to_string(nx) + "). ActionModel: " |
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<< *models[t]); |
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} |
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} |
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const std::size_t nx = problem_->get_terminalModel()->get_state()->get_nx(); |
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if (static_cast<std::size_t>(xs_warm[T].size()) != nx) { |
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throw_pretty("Invalid argument: " |
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<< "xs_init[" + std::to_string(T) + |
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"] (terminal state) has wrong dimension (" |
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<< xs_warm[T].size() |
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<< " provided - it should be equal to " + |
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std::to_string(nx) + "). ActionModel: " |
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<< *problem_->get_terminalModel()); |
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} |
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std::copy(xs_warm.begin(), xs_warm.end(), xs_.begin()); |
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} |
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| 255 |
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✗ |
if (us_warm.size() == 0) { |
| 256 |
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for (std::size_t t = 0; t < T; ++t) { |
| 257 |
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const std::shared_ptr<ActionModelAbstract>& model = models[t]; |
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const std::size_t nu = model->get_nu(); |
| 259 |
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us_[t] = Eigen::VectorXd::Zero(nu); |
| 260 |
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} |
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} else { |
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if (us_warm.size() != T) { |
| 263 |
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throw_pretty("Warm start control has wrong dimension, got " |
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<< us_warm.size() << " expecting " << T); |
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} |
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for (std::size_t t = 0; t < T; ++t) { |
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const std::shared_ptr<ActionModelAbstract>& model = models[t]; |
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const std::size_t nu = model->get_nu(); |
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if (static_cast<std::size_t>(us_warm[t].size()) != nu) { |
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throw_pretty("Invalid argument: " |
| 271 |
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<< "us_init[" + std::to_string(t) + |
| 272 |
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"] has wrong dimension (" |
| 273 |
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<< us_warm[t].size() |
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<< " provided - it should be equal to " + |
| 275 |
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std::to_string(nu) + "). ActionModel: " |
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<< *model); |
| 277 |
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} |
| 278 |
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} |
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✗ |
std::copy(us_warm.begin(), us_warm.end(), us_.begin()); |
| 280 |
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} |
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is_feasible_ = is_feasible; |
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} |
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✗ |
void SolverAbstract::setCallbacks( |
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const std::vector<std::shared_ptr<CallbackAbstract> >& callbacks) { |
| 286 |
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callbacks_ = callbacks; |
| 287 |
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} |
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| 289 |
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const std::vector<std::shared_ptr<CallbackAbstract> >& |
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SolverAbstract::getCallbacks() const { |
| 291 |
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return callbacks_; |
| 292 |
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} |
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const std::shared_ptr<ShootingProblem>& SolverAbstract::get_problem() const { |
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return problem_; |
| 296 |
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} |
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| 298 |
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const std::vector<Eigen::VectorXd>& SolverAbstract::get_xs() const { |
| 299 |
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return xs_; |
| 300 |
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} |
| 301 |
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| 302 |
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const std::vector<Eigen::VectorXd>& SolverAbstract::get_us() const { |
| 303 |
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return us_; |
| 304 |
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} |
| 305 |
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| 306 |
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const std::vector<Eigen::VectorXd>& SolverAbstract::get_fs() const { |
| 307 |
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return fs_; |
| 308 |
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} |
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bool SolverAbstract::get_is_feasible() const { return is_feasible_; } |
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double SolverAbstract::get_cost() const { return cost_; } |
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| 314 |
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double SolverAbstract::get_merit() const { return merit_; } |
| 315 |
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| 316 |
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double SolverAbstract::get_stop() const { return stop_; } |
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| 318 |
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const Eigen::Vector2d& SolverAbstract::get_d() const { return d_; } |
| 319 |
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|
| 320 |
|
✗ |
double SolverAbstract::get_dV() const { return dV_; } |
| 321 |
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|
| 322 |
|
✗ |
double SolverAbstract::get_dPhi() const { return dPhi_; } |
| 323 |
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|
| 324 |
|
✗ |
double SolverAbstract::get_dVexp() const { return dVexp_; } |
| 325 |
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|
| 326 |
|
✗ |
double SolverAbstract::get_dPhiexp() const { return dPhiexp_; } |
| 327 |
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|
| 328 |
|
✗ |
double SolverAbstract::get_dfeas() const { return dfeas_; } |
| 329 |
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|
| 330 |
|
✗ |
double SolverAbstract::get_feas() const { return feas_; } |
| 331 |
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|
| 332 |
|
✗ |
double SolverAbstract::get_ffeas() const { return ffeas_; } |
| 333 |
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|
| 334 |
|
✗ |
double SolverAbstract::get_gfeas() const { return gfeas_; } |
| 335 |
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|
| 336 |
|
✗ |
double SolverAbstract::get_hfeas() const { return hfeas_; } |
| 337 |
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|
| 338 |
|
✗ |
double SolverAbstract::get_ffeas_try() const { return ffeas_try_; } |
| 339 |
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|
| 340 |
|
✗ |
double SolverAbstract::get_gfeas_try() const { return gfeas_try_; } |
| 341 |
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|
| 342 |
|
✗ |
double SolverAbstract::get_hfeas_try() const { return hfeas_try_; } |
| 343 |
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|
| 344 |
|
✗ |
double SolverAbstract::get_preg() const { return preg_; } |
| 345 |
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|
| 346 |
|
✗ |
double SolverAbstract::get_dreg() const { return dreg_; } |
| 347 |
|
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|
| 348 |
|
✗ |
DEPRECATED( |
| 349 |
|
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"Use get_preg for gettting the primal-dual regularization", |
| 350 |
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double SolverAbstract::get_xreg() const { return preg_; }) |
| 351 |
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|
| 352 |
|
✗ |
DEPRECATED( |
| 353 |
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|
"Use get_preg for gettting the primal-dual regularization", |
| 354 |
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double SolverAbstract::get_ureg() const { return preg_; }) |
| 355 |
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|
| 356 |
|
✗ |
double SolverAbstract::get_steplength() const { return steplength_; } |
| 357 |
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|
| 358 |
|
✗ |
double SolverAbstract::get_th_acceptstep() const { return th_acceptstep_; } |
| 359 |
|
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|
| 360 |
|
✗ |
double SolverAbstract::get_th_stop() const { return th_stop_; } |
| 361 |
|
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|
| 362 |
|
✗ |
double SolverAbstract::get_th_gaptol() const { return th_gaptol_; } |
| 363 |
|
|
|
| 364 |
|
✗ |
FeasibilityNorm SolverAbstract::get_feasnorm() const { return feasnorm_; } |
| 365 |
|
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|
| 366 |
|
✗ |
std::size_t SolverAbstract::get_iter() const { return iter_; } |
| 367 |
|
|
|
| 368 |
|
✗ |
void SolverAbstract::set_xs(const std::vector<Eigen::VectorXd>& xs) { |
| 369 |
|
✗ |
const std::size_t T = problem_->get_T(); |
| 370 |
|
✗ |
if (xs.size() != T + 1) { |
| 371 |
|
✗ |
throw_pretty("Invalid argument: " << "xs list has to be of length " + |
| 372 |
|
|
std::to_string(T + 1)); |
| 373 |
|
|
} |
| 374 |
|
|
|
| 375 |
|
✗ |
const std::size_t nx = problem_->get_nx(); |
| 376 |
|
✗ |
for (std::size_t t = 0; t < T; ++t) { |
| 377 |
|
✗ |
if (static_cast<std::size_t>(xs[t].size()) != nx) { |
| 378 |
|
✗ |
throw_pretty("Invalid argument: " |
| 379 |
|
|
<< "xs[" + std::to_string(t) + "] has wrong dimension (" |
| 380 |
|
|
<< xs[t].size() |
| 381 |
|
|
<< " provided - it should be " + std::to_string(nx) + ")") |
| 382 |
|
|
} |
| 383 |
|
|
} |
| 384 |
|
✗ |
if (static_cast<std::size_t>(xs[T].size()) != nx) { |
| 385 |
|
✗ |
throw_pretty("Invalid argument: " |
| 386 |
|
|
<< "xs[" + std::to_string(T) + |
| 387 |
|
|
"] (terminal state) has wrong dimension (" |
| 388 |
|
|
<< xs[T].size() |
| 389 |
|
|
<< " provided - it should be " + std::to_string(nx) + ")") |
| 390 |
|
|
} |
| 391 |
|
✗ |
xs_ = xs; |
| 392 |
|
|
} |
| 393 |
|
|
|
| 394 |
|
✗ |
void SolverAbstract::set_us(const std::vector<Eigen::VectorXd>& us) { |
| 395 |
|
✗ |
const std::size_t T = problem_->get_T(); |
| 396 |
|
✗ |
if (us.size() != T) { |
| 397 |
|
✗ |
throw_pretty("Invalid argument: " << "us list has to be of length " + |
| 398 |
|
|
std::to_string(T)); |
| 399 |
|
|
} |
| 400 |
|
|
|
| 401 |
|
|
const std::vector<std::shared_ptr<ActionModelAbstract> >& models = |
| 402 |
|
✗ |
problem_->get_runningModels(); |
| 403 |
|
✗ |
for (std::size_t t = 0; t < T; ++t) { |
| 404 |
|
✗ |
const std::shared_ptr<ActionModelAbstract>& model = models[t]; |
| 405 |
|
✗ |
const std::size_t nu = model->get_nu(); |
| 406 |
|
✗ |
if (static_cast<std::size_t>(us[t].size()) != nu) { |
| 407 |
|
✗ |
throw_pretty("Invalid argument: " |
| 408 |
|
|
<< "us[" + std::to_string(t) + "] has wrong dimension (" |
| 409 |
|
|
<< us[t].size() |
| 410 |
|
|
<< " provided - it should be " + std::to_string(nu) + ")") |
| 411 |
|
|
} |
| 412 |
|
|
} |
| 413 |
|
✗ |
us_ = us; |
| 414 |
|
|
} |
| 415 |
|
|
|
| 416 |
|
✗ |
void SolverAbstract::set_preg(const double preg) { |
| 417 |
|
✗ |
if (preg < 0.) { |
| 418 |
|
✗ |
throw_pretty("Invalid argument: " << "preg value has to be positive."); |
| 419 |
|
|
} |
| 420 |
|
✗ |
preg_ = preg; |
| 421 |
|
|
} |
| 422 |
|
|
|
| 423 |
|
✗ |
void SolverAbstract::set_dreg(const double dreg) { |
| 424 |
|
✗ |
if (dreg < 0.) { |
| 425 |
|
✗ |
throw_pretty("Invalid argument: " << "dreg value has to be positive."); |
| 426 |
|
|
} |
| 427 |
|
✗ |
dreg_ = dreg; |
| 428 |
|
|
} |
| 429 |
|
|
|
| 430 |
|
✗ |
DEPRECATED( |
| 431 |
|
|
"Use set_preg for gettting the primal-variable regularization", |
| 432 |
|
|
void SolverAbstract::set_xreg(const double xreg) { |
| 433 |
|
|
if (xreg < 0.) { |
| 434 |
|
|
throw_pretty("Invalid argument: " << "xreg value has to be positive."); |
| 435 |
|
|
} |
| 436 |
|
|
xreg_ = xreg; |
| 437 |
|
|
preg_ = xreg; |
| 438 |
|
|
}) |
| 439 |
|
|
|
| 440 |
|
✗ |
DEPRECATED( |
| 441 |
|
|
"Use set_preg for gettting the primal-variable regularization", |
| 442 |
|
|
void SolverAbstract::set_ureg(const double ureg) { |
| 443 |
|
|
if (ureg < 0.) { |
| 444 |
|
|
throw_pretty("Invalid argument: " << "ureg value has to be positive."); |
| 445 |
|
|
} |
| 446 |
|
|
ureg_ = ureg; |
| 447 |
|
|
preg_ = ureg; |
| 448 |
|
|
}) |
| 449 |
|
|
|
| 450 |
|
✗ |
void SolverAbstract::set_th_acceptstep(const double th_acceptstep) { |
| 451 |
|
✗ |
if (0. >= th_acceptstep || th_acceptstep > 1) { |
| 452 |
|
✗ |
throw_pretty( |
| 453 |
|
|
"Invalid argument: " << "th_acceptstep value should between 0 and 1."); |
| 454 |
|
|
} |
| 455 |
|
✗ |
th_acceptstep_ = th_acceptstep; |
| 456 |
|
|
} |
| 457 |
|
|
|
| 458 |
|
✗ |
void SolverAbstract::set_th_stop(const double th_stop) { |
| 459 |
|
✗ |
if (th_stop <= 0.) { |
| 460 |
|
✗ |
throw_pretty("Invalid argument: " << "th_stop value has to higher than 0."); |
| 461 |
|
|
} |
| 462 |
|
✗ |
th_stop_ = th_stop; |
| 463 |
|
|
} |
| 464 |
|
|
|
| 465 |
|
✗ |
void SolverAbstract::set_th_gaptol(const double th_gaptol) { |
| 466 |
|
✗ |
if (0. > th_gaptol) { |
| 467 |
|
✗ |
throw_pretty("Invalid argument: " << "th_gaptol value has to be positive."); |
| 468 |
|
|
} |
| 469 |
|
✗ |
th_gaptol_ = th_gaptol; |
| 470 |
|
|
} |
| 471 |
|
|
|
| 472 |
|
✗ |
void SolverAbstract::set_feasnorm(const FeasibilityNorm feasnorm) { |
| 473 |
|
✗ |
feasnorm_ = feasnorm; |
| 474 |
|
|
} |
| 475 |
|
|
|
| 476 |
|
✗ |
bool raiseIfNaN(const double value) { |
| 477 |
|
✗ |
if (std::isnan(value) || std::isinf(value) || value >= 1e30) { |
| 478 |
|
✗ |
return true; |
| 479 |
|
|
} else { |
| 480 |
|
✗ |
return false; |
| 481 |
|
|
} |
| 482 |
|
|
} |
| 483 |
|
|
|
| 484 |
|
|
} // namespace crocoddyl |
| 485 |
|
|
|