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/////////////////////////////////////////////////////////////////////////////// |
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// BSD 3-Clause License |
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// |
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// Copyright (C) 2021-2023, Heriot-Watt University, University of Edinburgh |
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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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#include "crocoddyl/core/solvers/intro.hpp" |
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#include <iostream> |
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#include "crocoddyl/core/utils/exception.hpp" |
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#include "crocoddyl/core/utils/stop-watch.hpp" |
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namespace crocoddyl { |
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SolverIntro::SolverIntro(boost::shared_ptr<ShootingProblem> problem) |
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: SolverFDDP(problem), |
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eq_solver_(LuNull), |
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th_feas_(1e-4), |
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rho_(0.3), |
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upsilon_(0.), |
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✓✗ |
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zero_upsilon_(false) { |
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const std::size_t T = problem_->get_T(); |
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✓✗ |
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Hu_rank_.resize(T); |
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✓✗ |
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KQuu_tmp_.resize(T); |
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✓✗ |
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YZ_.resize(T); |
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✓✗ |
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Hy_.resize(T); |
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✓✗ |
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Qz_.resize(T); |
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✓✗ |
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Qzz_.resize(T); |
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✓✗ |
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Qxz_.resize(T); |
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✓✗ |
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Quz_.resize(T); |
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kz_.resize(T); |
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Kz_.resize(T); |
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ks_.resize(T); |
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Ks_.resize(T); |
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QuuinvHuT_.resize(T); |
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Qzz_llt_.resize(T); |
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Hu_lu_.resize(T); |
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Hu_qr_.resize(T); |
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Hy_lu_.resize(T); |
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const std::size_t ndx = problem_->get_ndx(); |
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const std::vector<boost::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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✓✓ |
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for (std::size_t t = 0; t < T; ++t) { |
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const boost::shared_ptr<ActionModelAbstract>& model = models[t]; |
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const std::size_t nu = model->get_nu(); |
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✓✗ |
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const std::size_t nh = model->get_nh(); |
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Hu_rank_[t] = nh; |
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✓✗✓✗
|
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KQuu_tmp_[t] = Eigen::MatrixXd::Zero(ndx, nu); |
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✓✗✓✗
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YZ_[t] = Eigen::MatrixXd::Zero(nu, nu); |
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Hy_[t] = Eigen::MatrixXd::Zero(nh, nh); |
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Qz_[t] = Eigen::VectorXd::Zero(nh); |
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Qzz_[t] = Eigen::MatrixXd::Zero(nh, nh); |
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✓✗✓✗
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Qxz_[t] = Eigen::MatrixXd::Zero(ndx, nh); |
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✓✗✓✗
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Quz_[t] = Eigen::MatrixXd::Zero(nu, nh); |
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kz_[t] = Eigen::VectorXd::Zero(nu); |
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Kz_[t] = Eigen::MatrixXd::Zero(nu, ndx); |
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ks_[t] = Eigen::VectorXd::Zero(nh); |
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Ks_[t] = Eigen::MatrixXd::Zero(nh, ndx); |
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QuuinvHuT_[t] = Eigen::MatrixXd::Zero(nu, nh); |
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Qzz_llt_[t] = Eigen::LLT<Eigen::MatrixXd>(nh); |
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Hu_lu_[t] = Eigen::FullPivLU<Eigen::MatrixXd>(nh, nu); |
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Hu_qr_[t] = Eigen::ColPivHouseholderQR<Eigen::MatrixXd>(nu, nh); |
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Hy_lu_[t] = Eigen::PartialPivLU<Eigen::MatrixXd>(nh); |
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} |
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} |
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SolverIntro::~SolverIntro() {} |
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bool SolverIntro::solve(const std::vector<Eigen::VectorXd>& init_xs, |
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const std::vector<Eigen::VectorXd>& init_us, |
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const std::size_t maxiter, const bool is_feasible, |
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const double init_reg) { |
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START_PROFILER("SolverIntro::solve"); |
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if (problem_->is_updated()) { |
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resizeData(); |
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} |
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xs_try_[0] = |
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problem_->get_x0(); // it is needed in case that init_xs[0] is infeasible |
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setCandidate(init_xs, init_us, is_feasible); |
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if (std::isnan(init_reg)) { |
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preg_ = reg_min_; |
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dreg_ = reg_min_; |
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} else { |
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preg_ = init_reg; |
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dreg_ = init_reg; |
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} |
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was_feasible_ = false; |
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if (zero_upsilon_) { |
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upsilon_ = 0.; |
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} |
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bool recalcDiff = true; |
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for (iter_ = 0; iter_ < maxiter; ++iter_) { |
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while (true) { |
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try { |
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computeDirection(recalcDiff); |
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} catch (std::exception& e) { |
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recalcDiff = false; |
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increaseRegularization(); |
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if (preg_ == reg_max_) { |
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return false; |
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} else { |
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continue; |
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} |
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} |
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break; |
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} |
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updateExpectedImprovement(); |
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expectedImprovement(); |
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// Update the penalty parameter for computing the merit function and its |
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// directional derivative For more details see Section 3 of "An Interior |
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// Point Algorithm for Large Scale Nonlinear Programming" |
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if (hfeas_ != 0 && iter_ != 0) { |
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upsilon_ = |
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std::max(upsilon_, (d_[0] + .5 * d_[1]) / ((1 - rho_) * hfeas_)); |
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} |
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// We need to recalculate the derivatives when the step length passes |
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recalcDiff = false; |
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for (std::vector<double>::const_iterator it = alphas_.begin(); |
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it != alphas_.end(); ++it) { |
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steplength_ = *it; |
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try { |
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dV_ = tryStep(steplength_); |
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dfeas_ = hfeas_ - hfeas_try_; |
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dPhi_ = dV_ + upsilon_ * dfeas_; |
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} catch (std::exception& e) { |
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continue; |
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} |
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expectedImprovement(); |
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dVexp_ = steplength_ * (d_[0] + 0.5 * steplength_ * d_[1]); |
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dPhiexp_ = dVexp_ + steplength_ * upsilon_ * dfeas_; |
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if (dPhiexp_ >= 0) { // descend direction |
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if (std::abs(d_[0]) < th_grad_ || dPhi_ > th_acceptstep_ * dPhiexp_) { |
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was_feasible_ = is_feasible_; |
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setCandidate(xs_try_, us_try_, (was_feasible_) || (steplength_ == 1)); |
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cost_ = cost_try_; |
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hfeas_ = hfeas_try_; |
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merit_ = cost_ + upsilon_ * hfeas_; |
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recalcDiff = true; |
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break; |
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} |
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} else { // reducing the gaps by allowing a small increment in the cost |
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// value |
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if (dV_ > th_acceptnegstep_ * dVexp_) { |
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was_feasible_ = is_feasible_; |
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setCandidate(xs_try_, us_try_, (was_feasible_) || (steplength_ == 1)); |
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cost_ = cost_try_; |
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hfeas_ = hfeas_try_; |
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merit_ = cost_ + upsilon_ * hfeas_; |
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recalcDiff = true; |
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break; |
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} |
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} |
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} |
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stoppingCriteria(); |
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const std::size_t n_callbacks = callbacks_.size(); |
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for (std::size_t c = 0; c < n_callbacks; ++c) { |
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CallbackAbstract& callback = *callbacks_[c]; |
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callback(*this); |
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} |
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if (steplength_ > th_stepdec_ && dV_ >= 0.) { |
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decreaseRegularization(); |
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} |
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if (steplength_ <= th_stepinc_ || std::abs(d_[1]) <= th_feas_) { |
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if (preg_ == reg_max_) { |
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STOP_PROFILER("SolverIntro::solve"); |
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return false; |
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} |
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increaseRegularization(); |
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} |
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if (is_feasible_ && stop_ < th_stop_) { |
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STOP_PROFILER("SolverIntro::solve"); |
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return true; |
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} |
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} |
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STOP_PROFILER("SolverIntro::solve"); |
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return false; |
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} |
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double SolverIntro::tryStep(const double steplength) { |
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forwardPass(steplength); |
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hfeas_try_ = computeEqualityFeasibility(); |
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return cost_ - cost_try_; |
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} |
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double SolverIntro::stoppingCriteria() { |
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stop_ = std::max(hfeas_, std::abs(d_[0] + 0.5 * d_[1])); |
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return stop_; |
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} |
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void SolverIntro::resizeData() { |
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START_PROFILER("SolverIntro::resizeData"); |
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SolverFDDP::resizeData(); |
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const std::size_t T = problem_->get_T(); |
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const std::size_t ndx = problem_->get_ndx(); |
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const std::vector<boost::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 boost::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 nh = model->get_nh(); |
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KQuu_tmp_[t].conservativeResize(ndx, nu); |
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YZ_[t].conservativeResize(nu, nu); |
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Hy_[t].conservativeResize(nh, nh); |
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Qz_[t].conservativeResize(nh); |
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Qzz_[t].conservativeResize(nh, nh); |
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Qxz_[t].conservativeResize(ndx, nh); |
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Quz_[t].conservativeResize(nu, nh); |
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kz_[t].conservativeResize(nu); |
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Kz_[t].conservativeResize(nu, ndx); |
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ks_[t].conservativeResize(nh); |
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Ks_[t].conservativeResize(nh, ndx); |
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QuuinvHuT_[t].conservativeResize(nu, nh); |
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} |
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STOP_PROFILER("SolverIntro::resizeData"); |
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} |
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double SolverIntro::calcDiff() { |
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START_PROFILER("SolverIntro::calcDiff"); |
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SolverFDDP::calcDiff(); |
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const std::size_t T = problem_->get_T(); |
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const std::vector<boost::shared_ptr<ActionModelAbstract> >& models = |
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problem_->get_runningModels(); |
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const std::vector<boost::shared_ptr<ActionDataAbstract> >& datas = |
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problem_->get_runningDatas(); |
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switch (eq_solver_) { |
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case LuNull: |
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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 boost::shared_ptr<crocoddyl::ActionModelAbstract>& model = |
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models[t]; |
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const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data = datas[t]; |
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if (model->get_nu() > 0 && model->get_nh() > 0) { |
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Hu_lu_[t].compute(data->Hu); |
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YZ_[t] << Hu_lu_[t].matrixLU().transpose(), Hu_lu_[t].kernel(); |
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Hu_rank_[t] = Hu_lu_[t].rank(); |
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const Eigen::Block<Eigen::MatrixXd, Eigen::Dynamic, Eigen::Dynamic, |
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Eigen::RowMajor> |
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Y = YZ_[t].leftCols(Hu_lu_[t].rank()); |
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Hy_[t].noalias() = data->Hu * Y; |
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Hy_lu_[t].compute(Hy_[t]); |
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const Eigen::Inverse<Eigen::PartialPivLU<Eigen::MatrixXd> > Hy_inv = |
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Hy_lu_[t].inverse(); |
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ks_[t].noalias() = Hy_inv * data->h; |
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Ks_[t].noalias() = Hy_inv * data->Hx; |
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kz_[t].noalias() = Y * ks_[t]; |
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Kz_[t].noalias() = Y * Ks_[t]; |
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} |
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} |
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break; |
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case QrNull: |
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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 boost::shared_ptr<crocoddyl::ActionModelAbstract>& model = |
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models[t]; |
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const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data = datas[t]; |
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if (model->get_nu() > 0 && model->get_nh() > 0) { |
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Hu_qr_[t].compute(data->Hu.transpose()); |
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YZ_[t] = Hu_qr_[t].householderQ(); |
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Hu_rank_[t] = Hu_qr_[t].rank(); |
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const Eigen::Block<Eigen::MatrixXd, Eigen::Dynamic, Eigen::Dynamic, |
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Eigen::RowMajor> |
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Y = YZ_[t].leftCols(Hu_qr_[t].rank()); |
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Hy_[t].noalias() = data->Hu * Y; |
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Hy_lu_[t].compute(Hy_[t]); |
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const Eigen::Inverse<Eigen::PartialPivLU<Eigen::MatrixXd> > Hy_inv = |
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Hy_lu_[t].inverse(); |
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ks_[t].noalias() = Hy_inv * data->h; |
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Ks_[t].noalias() = Hy_inv * data->Hx; |
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kz_[t].noalias() = Y * ks_[t]; |
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Kz_[t].noalias() = Y * Ks_[t]; |
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} |
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} |
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break; |
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case Schur: |
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break; |
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} |
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STOP_PROFILER("SolverIntro::calcDiff"); |
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return cost_; |
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} |
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void SolverIntro::computeValueFunction( |
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const std::size_t t, const boost::shared_ptr<ActionModelAbstract>& model) { |
300 |
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const std::size_t nu = model->get_nu(); |
301 |
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Vx_[t] = Qx_[t]; |
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Vxx_[t] = Qxx_[t]; |
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if (nu != 0) { |
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START_PROFILER("SolverIntro::Vx"); |
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Quuk_[t].noalias() = Quu_[t] * k_[t]; |
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Vx_[t].noalias() -= Qxu_[t] * k_[t]; |
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Qu_[t] -= Quuk_[t]; |
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Vx_[t].noalias() -= K_[t].transpose() * Qu_[t]; |
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Qu_[t] += Quuk_[t]; |
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STOP_PROFILER("SolverIntro::Vx"); |
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START_PROFILER("SolverIntro::Vxx"); |
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KQuu_tmp_[t].noalias() = K_[t].transpose() * Quu_[t]; |
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KQuu_tmp_[t].noalias() -= 2 * Qxu_[t]; |
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Vxx_[t].noalias() += KQuu_tmp_[t] * K_[t]; |
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STOP_PROFILER("SolverIntro::Vxx"); |
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} |
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Vxx_tmp_ = 0.5 * (Vxx_[t] + Vxx_[t].transpose()); |
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Vxx_[t] = Vxx_tmp_; |
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if (!std::isnan(preg_)) { |
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Vxx_[t].diagonal().array() += preg_; |
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} |
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// Compute and store the Vx gradient at end of the interval (rollout state) |
325 |
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if (!is_feasible_) { |
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Vx_[t].noalias() += Vxx_[t] * fs_[t]; |
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} |
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} |
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330 |
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void SolverIntro::computeGains(const std::size_t t) { |
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START_PROFILER("SolverIntro::computeGains"); |
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const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model = |
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problem_->get_runningModels()[t]; |
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const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data = |
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problem_->get_runningDatas()[t]; |
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337 |
|
|
const std::size_t nu = model->get_nu(); |
338 |
|
|
const std::size_t nh = model->get_nh(); |
339 |
|
|
switch (eq_solver_) { |
340 |
|
|
case LuNull: |
341 |
|
|
case QrNull: |
342 |
|
|
if (nu > 0 && nh > 0) { |
343 |
|
|
START_PROFILER("SolverIntro::Qzz_inv"); |
344 |
|
|
const std::size_t rank = Hu_rank_[t]; |
345 |
|
|
const std::size_t nullity = data->Hu.cols() - rank; |
346 |
|
|
const Eigen::Block<Eigen::MatrixXd, Eigen::Dynamic, Eigen::Dynamic, |
347 |
|
|
Eigen::RowMajor> |
348 |
|
|
Z = YZ_[t].rightCols(nullity); |
349 |
|
|
Quz_[t].noalias() = Quu_[t] * Z; |
350 |
|
|
Qzz_[t].noalias() = Z.transpose() * Quz_[t]; |
351 |
|
|
Qzz_llt_[t].compute(Qzz_[t]); |
352 |
|
|
STOP_PROFILER("SolverIntro::Qzz_inv"); |
353 |
|
|
const Eigen::ComputationInfo& info = Qzz_llt_[t].info(); |
354 |
|
|
if (info != Eigen::Success) { |
355 |
|
|
throw_pretty("backward error"); |
356 |
|
|
} |
357 |
|
|
|
358 |
|
|
k_[t] = kz_[t]; |
359 |
|
|
K_[t] = Kz_[t]; |
360 |
|
|
Eigen::Transpose<Eigen::MatrixXd> QzzinvQzu = Quz_[t].transpose(); |
361 |
|
|
Qzz_llt_[t].solveInPlace(QzzinvQzu); |
362 |
|
|
Qz_[t].noalias() = Z.transpose() * Qu_[t]; |
363 |
|
|
Qzz_llt_[t].solveInPlace(Qz_[t]); |
364 |
|
|
Qxz_[t].noalias() = Qxu_[t] * Z; |
365 |
|
|
Eigen::Transpose<Eigen::MatrixXd> Qzx = Qxz_[t].transpose(); |
366 |
|
|
Qzz_llt_[t].solveInPlace(Qzx); |
367 |
|
|
Qz_[t].noalias() -= QzzinvQzu * kz_[t]; |
368 |
|
|
Qzx.noalias() -= QzzinvQzu * Kz_[t]; |
369 |
|
|
k_[t].noalias() += Z * Qz_[t]; |
370 |
|
|
K_[t].noalias() += Z * Qzx; |
371 |
|
|
} else { |
372 |
|
|
SolverFDDP::computeGains(t); |
373 |
|
|
} |
374 |
|
|
break; |
375 |
|
|
case Schur: |
376 |
|
|
SolverFDDP::computeGains(t); |
377 |
|
|
if (nu > 0 && nh > 0) { |
378 |
|
|
START_PROFILER("SolverIntro::Qzz_inv"); |
379 |
|
|
QuuinvHuT_[t] = data->Hu.transpose(); |
380 |
|
|
Quu_llt_[t].solveInPlace(QuuinvHuT_[t]); |
381 |
|
|
Qzz_[t].noalias() = data->Hu * QuuinvHuT_[t]; |
382 |
|
|
Qzz_llt_[t].compute(Qzz_[t]); |
383 |
|
|
STOP_PROFILER("SolverIntro::Qzz_inv"); |
384 |
|
|
const Eigen::ComputationInfo& info = Qzz_llt_[t].info(); |
385 |
|
|
if (info != Eigen::Success) { |
386 |
|
|
throw_pretty("backward error"); |
387 |
|
|
} |
388 |
|
|
Eigen::Transpose<Eigen::MatrixXd> HuQuuinv = QuuinvHuT_[t].transpose(); |
389 |
|
|
Qzz_llt_[t].solveInPlace(HuQuuinv); |
390 |
|
|
ks_[t] = data->h; |
391 |
|
|
ks_[t].noalias() -= data->Hu * k_[t]; |
392 |
|
|
Ks_[t] = data->Hx; |
393 |
|
|
Ks_[t].noalias() -= data->Hu * K_[t]; |
394 |
|
|
k_[t].noalias() += QuuinvHuT_[t] * ks_[t]; |
395 |
|
|
K_[t] += QuuinvHuT_[t] * Ks_[t]; |
396 |
|
|
} |
397 |
|
|
break; |
398 |
|
|
} |
399 |
|
|
STOP_PROFILER("SolverIntro::computeGains"); |
400 |
|
|
} |
401 |
|
|
|
402 |
|
|
EqualitySolverType SolverIntro::get_equality_solver() const { |
403 |
|
|
return eq_solver_; |
404 |
|
|
} |
405 |
|
|
|
406 |
|
|
double SolverIntro::get_th_feas() const { return th_feas_; } |
407 |
|
|
|
408 |
|
|
double SolverIntro::get_rho() const { return rho_; } |
409 |
|
|
|
410 |
|
|
double SolverIntro::get_upsilon() const { return upsilon_; } |
411 |
|
|
|
412 |
|
|
bool SolverIntro::get_zero_upsilon() const { return zero_upsilon_; } |
413 |
|
|
|
414 |
|
|
const std::vector<std::size_t>& SolverIntro::get_Hu_rank() const { |
415 |
|
|
return Hu_rank_; |
416 |
|
|
} |
417 |
|
|
|
418 |
|
|
const std::vector<Eigen::MatrixXd>& SolverIntro::get_YZ() const { return YZ_; } |
419 |
|
|
|
420 |
|
|
const std::vector<Eigen::MatrixXd>& SolverIntro::get_Qzz() const { |
421 |
|
|
return Qzz_; |
422 |
|
|
} |
423 |
|
|
|
424 |
|
|
const std::vector<Eigen::MatrixXd>& SolverIntro::get_Qxz() const { |
425 |
|
|
return Qxz_; |
426 |
|
|
} |
427 |
|
|
|
428 |
|
|
const std::vector<Eigen::MatrixXd>& SolverIntro::get_Quz() const { |
429 |
|
|
return Quz_; |
430 |
|
|
} |
431 |
|
|
|
432 |
|
|
const std::vector<Eigen::VectorXd>& SolverIntro::get_Qz() const { return Qz_; } |
433 |
|
|
|
434 |
|
|
const std::vector<Eigen::MatrixXd>& SolverIntro::get_Hy() const { return Hy_; } |
435 |
|
|
|
436 |
|
|
const std::vector<Eigen::VectorXd>& SolverIntro::get_kz() const { return kz_; } |
437 |
|
|
|
438 |
|
|
const std::vector<Eigen::MatrixXd>& SolverIntro::get_Kz() const { return Kz_; } |
439 |
|
|
|
440 |
|
|
const std::vector<Eigen::VectorXd>& SolverIntro::get_ks() const { return ks_; } |
441 |
|
|
|
442 |
|
|
const std::vector<Eigen::MatrixXd>& SolverIntro::get_Ks() const { return Ks_; } |
443 |
|
|
|
444 |
|
|
void SolverIntro::set_equality_solver(const EqualitySolverType type) { |
445 |
|
|
eq_solver_ = type; |
446 |
|
|
} |
447 |
|
|
|
448 |
|
|
void SolverIntro::set_th_feas(const double th_feas) { th_feas_ = th_feas; } |
449 |
|
|
|
450 |
|
|
void SolverIntro::set_rho(const double rho) { |
451 |
|
|
if (0. >= rho || rho > 1.) { |
452 |
|
|
throw_pretty("Invalid argument: " |
453 |
|
|
<< "rho value should between 0 and 1."); |
454 |
|
|
} |
455 |
|
|
rho_ = rho; |
456 |
|
|
} |
457 |
|
|
|
458 |
|
|
void SolverIntro::set_zero_upsilon(const bool zero_upsilon) { |
459 |
|
|
zero_upsilon_ = zero_upsilon; |
460 |
|
|
} |
461 |
|
|
|
462 |
|
|
} // namespace crocoddyl |