Class SolverIntro

Defined in File intro.hpp

Inheritance Relationships

Base Type

public crocoddyl::SolverFDDP (Class SolverFDDP)

Class Documentation

class SolverIntro : public crocoddyl::SolverFDDP 

Public Functions

explicit EIGEN_MAKE_ALIGNED_OPERATOR_NEW SolverIntro(std::shared_ptr<ShootingProblem> problem)

Initialize the INTRO solver.

Parameters:

problem – [in] Shooting problem
reduced – [in] Use the reduced Schur-complement approach (default true)

virtual ~SolverIntro()

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_reg = NAN)

virtual double tryStep(const double step_length = 1)

virtual double stoppingCriteria()

virtual void resizeData()

virtual double calcDiff()

virtual void computeValueFunction(const std::size_t t, const std::shared_ptr<ActionModelAbstract> &model)

virtual void computeGains(const std::size_t t)

EqualitySolverType get_equality_solver() const: Return the type of solver used for handling the equality constraints.

double get_th_feas() const: Return the threshold for switching to feasibility.

double get_rho() const: Return the rho parameter used in the merit function.

double get_upsilon() const: Return the estimated penalty parameter that balances relative contribution of the cost function and equality constraints.

const std::vector<Eigen::MatrixXd> &get_YZ() const: Return the rank of control-equality constraints \(\mathbf{H_u}\f */ const std::vector<std::size_t>& get_Hu_rank() const; /** @brief Return the span and kernel of control-equality constraints \)\mathbf{H_u}\f.

const std::vector<Eigen::MatrixXd> &get_Qzz() const: Return Hessian of the reduced Hamiltonian \(\mathbf{Q_{zz}}\).

const std::vector<Eigen::MatrixXd> &get_Qxz() const: Return Hessian of the reduced Hamiltonian \(\mathbf{Q_{xz}}\).

const std::vector<Eigen::MatrixXd> &get_Quz() const: Return Hessian of the reduced Hamiltonian \(\mathbf{Q_{uz}}\).

const std::vector<Eigen::VectorXd> &get_Qz() const: Return Jacobian of the reduced Hamiltonian \(\mathbf{Q_{z}}\).

const std::vector<Eigen::MatrixXd> &get_Hy() const: Return span-projected Jacobian of the equality-constraint with respect to the control.

const std::vector<Eigen::VectorXd> &get_kz() const: Return feedforward term related to the nullspace of \(\mathbf{H_u}\).

const std::vector<Eigen::MatrixXd> &get_Kz() const: Return feedback gain related to the nullspace of \(\mathbf{H_u}\).

const std::vector<Eigen::VectorXd> &get_ks() const: Return feedforward term related to the equality constraints.

const std::vector<Eigen::MatrixXd> &get_Ks() const: Return feedback gain related to the equality constraints.

bool get_zero_upsilon() const

Return the zero-upsilon label.

True if we set the estimated penalty parameter (upsilon) to zero when solve is called.

void set_equality_solver(const EqualitySolverType type)

Modify the type of solver used for handling the equality constraints.

Note that the default solver is nullspace LU. When we enable parallelization, this strategy is generally faster than others for medium to large systems.

void set_th_feas(const double th_feas): Modify the threshold for switching to feasibility.

void set_rho(const double rho): Modify the rho parameter used in the merit function.

void set_zero_upsilon(const bool zero_upsilon)

Modify the zero-upsilon label.

Parameters:: zero_upsilon – True if we set estimated penalty parameter (upsilon) to zero when solve is called.

Protected Attributes

enum EqualitySolverType eq_solver_: Strategy used for handling the equality constraints.

double th_feas_: Threshold for switching to feasibility.

double rho_: Parameter used in the merit function to predict the expected reduction

double upsilon_: Estimated penalty parameter that balances relative contribution of the cost function and equality constraints

bool zero_upsilon_: True if we wish to set estimated penalty parameter (upsilon) to zero when solve is called.

std::vector<std::size_t> Hu_rank_: Rank of the control Jacobian of the equality constraints.

std::vector<Eigen::MatrixXd> KQuu_tmp_

std::vector<Eigen::MatrixXd> YZ_: Span \(\mathbf{Y}\in\mathbb{R}^{rank}\) and kernel \(\mathbf{Z}\in\mathbb{R}^{nullity}\) of the control-equality constraints \(\mathbf{H_u}\)

std::vector<Eigen::MatrixXd> Hy_: Span-projected Jacobian of the equality-constraint with respect to the control

std::vector<Eigen::VectorXd> Qz_: Jacobian of the reduced Hamiltonian \(\mathbf{Q_{z}}\).

std::vector<Eigen::MatrixXd> Qzz_: Hessian of the reduced Hamiltonian \(\mathbf{Q_{zz}}\).

std::vector<Eigen::MatrixXd> Qxz_: Hessian of the reduced Hamiltonian \(\mathbf{Q_{xz}}\).

std::vector<Eigen::MatrixXd> Quz_: Hessian of the reduced Hamiltonian \(\mathbf{Q_{uz}}\).

std::vector<Eigen::VectorXd> kz_: Feedforward term in the nullspace of \(\mathbf{H_u}\).

std::vector<Eigen::MatrixXd> Kz_: Feedback gain in the nullspace of \(\mathbf{H_u}\).

std::vector<Eigen::VectorXd> ks_: Feedforward term related to the equality constraints.

std::vector<Eigen::MatrixXd> Ks_: Feedback gain related to the equality constraints.

std::vector<Eigen::MatrixXd> QuuinvHuT_

std::vector<Eigen::LLT<Eigen::MatrixXd>> Qzz_llt_: Cholesky LLT solver.

std::vector<Eigen::FullPivLU<Eigen::MatrixXd>> Hu_lu_: Full-pivot LU solvers used for computing the span and nullspace matrices

std::vector<Eigen::ColPivHouseholderQR<Eigen::MatrixXd>> Hu_qr_: Column-pivot QR solvers used for computing the span and nullspace matrices

std::vector<Eigen::PartialPivLU<Eigen::MatrixXd>> Hy_lu_: Partial-pivot LU solvers used for computing the feedforward and feedback gain related to the equality constraint