10 #ifndef CROCODDYL_CORE_SOLVERS_KKT_HPP_ 11 #define CROCODDYL_CORE_SOLVERS_KKT_HPP_ 13 #include <Eigen/Dense> 14 #include <Eigen/Cholesky> 16 #include "crocoddyl/core/solver-base.hpp" 22 EIGEN_MAKE_ALIGNED_OPERATOR_NEW
24 explicit SolverKKT(boost::shared_ptr<ShootingProblem> problem);
27 virtual bool solve(
const std::vector<Eigen::VectorXd>& init_xs = DEFAULT_VECTOR,
28 const std::vector<Eigen::VectorXd>& init_us = DEFAULT_VECTOR,
const std::size_t& maxiter = 100,
29 const bool& is_feasible =
false,
const double& regInit = 1e-9);
31 virtual double tryStep(
const double& steplength = 1);
35 const Eigen::MatrixXd& get_kkt()
const;
36 const Eigen::VectorXd& get_kktref()
const;
37 const Eigen::VectorXd& get_primaldual()
const;
38 const std::vector<Eigen::VectorXd>& get_dxs()
const;
39 const std::vector<Eigen::VectorXd>& get_dus()
const;
40 const std::vector<Eigen::VectorXd>& get_lambdas()
const;
41 const std::size_t& get_nx()
const;
42 const std::size_t& get_ndx()
const;
43 const std::size_t& get_nu()
const;
50 std::vector<Eigen::VectorXd> xs_try_;
51 std::vector<Eigen::VectorXd> us_try_;
57 std::vector<Eigen::VectorXd> dxs_;
58 std::vector<Eigen::VectorXd> dus_;
59 std::vector<Eigen::VectorXd> lambdas_;
61 void computePrimalDual();
62 void increaseRegularization();
63 void decreaseRegularization();
68 Eigen::VectorXd kktref_;
69 Eigen::VectorXd primaldual_;
70 Eigen::VectorXd primal_;
71 Eigen::VectorXd dual_;
72 std::vector<double> alphas_;
75 Eigen::VectorXd kkt_primal_;
81 #endif // CROCODDYL_CORE_SOLVERS_KKT_HPP_ virtual double stoppingCriteria()
Return a positive value that quantifies the algorithm termination.
virtual bool solve(const std::vector< Eigen::VectorXd > &init_xs=DEFAULT_VECTOR, const std::vector< Eigen::VectorXd > &init_us=DEFAULT_VECTOR, const std::size_t &maxiter=100, const bool &is_feasible=false, const double ®Init=1e-9)
Compute the optimal trajectory as lists of and terms.
virtual const Eigen::Vector2d & expectedImprovement()
Return the expected improvement from a given current search direction.
Abstract class for optimal control solvers.
virtual double tryStep(const double &steplength=1)
Try a predefined step length and compute its cost improvement.
virtual void computeDirection(const bool &recalc=true)
Compute the search direction for the current guess .