Inheritance diagram for SolverKKT:

Public Member Functions
EIGEN_MAKE_ALIGNED_OPERATOR_NEW	SolverKKT (std::shared_ptr< ShootingProblem > problem)
virtual void	computeDirection (const bool recalc=true)
	Compute the search direction $(\delta\mathbf{x}^k,\delta\mathbf{u}^k)$ for the current guess $(\mathbf{x}^k_s,\mathbf{u}^k_s)$ .
virtual const Eigen::Vector2d &	expectedImprovement ()
	Return the expected improvement $dV_{exp}$ from a given current search direction $(\delta\mathbf{x}^k,\delta\mathbf{u}^k)$ .
const std::vector< Eigen::VectorXd > &	get_dus () const
const std::vector< Eigen::VectorXd > &	get_dxs () const
const Eigen::MatrixXd &	get_kkt () const
const Eigen::VectorXd &	get_kktref () const
const std::vector< Eigen::VectorXd > &	get_lambdas () const
std::size_t	get_ndx () const
std::size_t	get_nu () const
std::size_t	get_nx () const
const Eigen::VectorXd &	get_primaldual () const
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 regInit=1e-9)
	Compute the optimal trajectory $\mathbf{x}^_s,\mathbf{u}^_s$ as lists of and terms.
virtual double	stoppingCriteria ()
	Return a positive value that quantifies the algorithm termination.
virtual double	tryStep (const double steplength=1)
	Try a predefined step length $\alpha$ and compute its cost improvement .
Public Member Functions inherited from SolverAbstract
EIGEN_MAKE_ALIGNED_OPERATOR_NEW	SolverAbstract (std::shared_ptr< ShootingProblem > problem)
	Initialize the solver.
double	computeDynamicFeasibility ()
	Compute the dynamic feasibility $\\|\mathbf{f}_{\mathbf{s}}\\|_{\infty,1}$ for the current guess $(\mathbf{x}^k,\mathbf{u}^k)$ .
double	computeEqualityFeasibility ()
	Compute the feasibility of the equality constraints for the current guess.
double	computeInequalityFeasibility ()
	Compute the feasibility of the inequality constraints for the current guess.
	DEPRECATED ("Use get_preg for primal-variable regularization", double get_xreg() const ;) DEPRECATED("Use get_preg for primal-variable regularization"
	DEPRECATED ("Use set_preg for primal-variable regularization", void set_xreg(const double xreg);) DEPRECATED("Use set_preg for primal-variable regularization"
double	get_cost () const
	Return the cost for the current guess.
const Eigen::Vector2d &	get_d () const
	Return the linear and quadratic terms of the expected improvement.
double	get_dfeas () const
	Return the reduction in the feasibility.
double	get_dPhi () const
	Return the reduction in the merit function $\Delta\Phi$ .
double	get_dPhiexp () const
	Return the expected reduction in the merit function $\Delta\Phi_{exp}$ .
double	get_dreg () const
	Return the dual-variable regularization.
double	get_dV () const
	Return the reduction in the cost function $\Delta V$ .
double	get_dVexp () const
	Return the expected reduction in the cost function $\Delta V_{exp}$ .
double	get_feas () const
	Return the total feasibility for the current guess.
FeasibilityNorm	get_feasnorm () const
	Return the type of norm used to evaluate the dynamic and constraints feasibility.
double	get_ffeas () const
	Return the dynamic feasibility for the current guess.
double	get_ffeas_try () const
	Return the dynamic feasibility for the current step length.
const std::vector< Eigen::VectorXd > &	get_fs () const
	Return the dynamic infeasibility $\mathbf{f}_{s}$ .
double	get_gfeas () const
	Return the inequality feasibility for the current guess.
double	get_gfeas_try () const
	Return the inequality feasibility for the current step length.
double	get_hfeas () const
	Return the equality feasibility for the current guess.
double	get_hfeas_try () const
	Return the equality feasibility for the current step length.
bool	get_is_feasible () const
	Return the feasibility status of the $(\mathbf{x}_s,\mathbf{u}_s)$ trajectory.
std::size_t	get_iter () const
	Return the number of iterations performed by the solver.
double	get_merit () const
	Return the merit for the current guess.
double	get_preg () const
	Return the primal-variable regularization.
const std::shared_ptr< ShootingProblem > &	get_problem () const
	Return the shooting problem.
double	get_steplength () const
	Return the step length $\alpha$ .
double	get_stop () const
	Return the stopping-criteria value computed by stoppingCriteria()
double	get_th_acceptstep () const
	Return the threshold used for accepting a step.
double	get_th_gaptol () const
	Return the threshold for accepting a gap as non-zero.
double	get_th_stop () const
	Return the tolerance for stopping the algorithm.
double	get_ureg () const
const std::vector< Eigen::VectorXd > &	get_us () const
	Return the control trajectory $\mathbf{u}_s$ .
const std::vector< Eigen::VectorXd > &	get_xs () const
	Return the state trajectory $\mathbf{x}_s$ .
const std::vector< std::shared_ptr< CallbackAbstract > > &	getCallbacks () const
	Return the list of callback functions using for diagnostic.
virtual void	resizeData ()
	Resizing the solver data.
void	set_dreg (const double dreg)
	Modify the dual-variable regularization value.
void	set_feasnorm (const FeasibilityNorm feas_norm)
	Modify the current norm used for computed the dynamic and constraint feasibility.
void	set_preg (const double preg)
	Modify the primal-variable regularization value.
void	set_th_acceptstep (const double th_acceptstep)
	Modify the threshold used for accepting step.
void	set_th_gaptol (const double th_gaptol)
	Modify the threshold for accepting a gap as non-zero.
void	set_th_stop (const double th_stop)
	Modify the tolerance for stopping the algorithm.
void	set_ureg (const double ureg)
void	set_us (const std::vector< Eigen::VectorXd > &us)
	Modify the control trajectory $\mathbf{u}_s$ .
void	set_xs (const std::vector< Eigen::VectorXd > &xs)
	Modify the state trajectory $\mathbf{x}_s$ .
void	setCallbacks (const std::vector< std::shared_ptr< CallbackAbstract > > &callbacks)
	Set a list of callback functions using for the solver diagnostic.
void	setCandidate (const std::vector< Eigen::VectorXd > &xs_warm=DEFAULT_VECTOR, const std::vector< Eigen::VectorXd > &us_warm=DEFAULT_VECTOR, const bool is_feasible=false)
	Set the solver candidate trajectories $(\mathbf{x}_s,\mathbf{u}_s)$ .

Protected Attributes
double	cost_try_
double	reg_decfactor_
double	reg_incfactor_
double	reg_max_
double	reg_min_
std::vector< Eigen::VectorXd >	us_try_
std::vector< Eigen::VectorXd >	xs_try_
Protected Attributes inherited from SolverAbstract
std::vector< std::shared_ptr< CallbackAbstract > >	callbacks_
	Callback functions.
double	cost_
	Cost for the current guess.
Eigen::Vector2d	d_
	LQ approximation of the expected improvement.
double	dfeas_
	Reduction in the feasibility.
double	dPhi_
	Reduction in the merit function computed by tryStep()
double	dPhiexp_
	Expected reduction in the merit function.
double	dreg_
	Current dual-variable regularization value.
double	dV_
	Reduction in the cost function computed by tryStep()
double	dVexp_
	Expected reduction in the cost function.
double	feas_
	Total feasibility for the current guess.
enum FeasibilityNorm	feasnorm_
double	ffeas_
	Feasibility of the dynamic constraints for the current guess.
double	ffeas_try_
std::vector< Eigen::VectorXd >	fs_
	Gaps/defects between shooting nodes.
std::vector< Eigen::VectorXd >	g_adj_
	Adjusted inequality bound.
double	gfeas_
double	gfeas_try_
double	hfeas_
double	hfeas_try_
bool	is_feasible_
	Label that indicates is the iteration is feasible.
std::size_t	iter_
	Number of iteration performed by the solver.
double	merit_
	Merit for the current guess.
double	preg_
	Current primal-variable regularization value.
std::shared_ptr< ShootingProblem >	problem_
	optimal control problem
double	steplength_
	< Current control regularization values
double	stop_
	Value computed by stoppingCriteria()
double	th_acceptstep_
	Threshold used for accepting step.
double	th_gaptol_
	Threshold limit to check non-zero gaps.
double	th_stop_
	Tolerance for stopping the algorithm.
double	tmp_feas_
	Temporal variables used for computed the feasibility.
double	ureg_
std::vector< Eigen::VectorXd >	us_
	Control trajectory.
bool	was_feasible_
std::vector< Eigen::VectorXd >	xs_
	State trajectory.

Additional Inherited Members
Protected Member Functions inherited from SolverAbstract
	DEPRECATED ("Use preg_ for primal-variable regularization", double xreg_;) DEPRECATED("Use dreg_ for primal-variable regularization"

Detailed Description

Definition at line 18 of file kkt.hpp.

Constructor & Destructor Documentation

◆ SolverKKT()

SolverKKT ( std::shared_ptr< ShootingProblem > problem )

explicit

Definition at line 15 of file kkt.cpp.

◆ ~SolverKKT()

~SolverKKT ( )

virtual

Definition at line 34 of file kkt.cpp.

Member Function Documentation

◆ solve()

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	reg_init = 1e-9 )

virtual

Compute the optimal trajectory $\mathbf{x}^*_s,\mathbf{u}^*_s$ as lists of $T+1$ and $T$ terms.

From an initial guess init_xs, init_us (feasible or not), iterate over computeDirection() and tryStep() until stoppingCriteria() is below threshold. It also describes the globalization strategy used during the numerical optimization.

Parameters

[in]	init_xs	initial guess for state trajectory with elements (default [])
[in]	init_us	initial guess for control trajectory with elements (default [])
[in]	maxiter	maximum allowed number of iterations (default 100)
[in]	is_feasible	true if the `init_xs` are obtained from integrating the `init_us` (rollout) (default false)
[in]	init_reg	initial guess for the regularization value. Very low values are typical used with very good guess points (default 1e-9).

Returns: A boolean that describes if convergence was reached.

Implements SolverAbstract.

Definition at line 36 of file kkt.cpp.

◆ computeDirection()

void computeDirection ( const bool recalc = true )

virtual

Compute the search direction $(\delta\mathbf{x}^k,\delta\mathbf{u}^k)$ for the current guess $(\mathbf{x}^k_s,\mathbf{u}^k_s)$ .

You must call setCandidate() first in order to define the current guess. A current guess defines a state and control trajectory $(\mathbf{x}^k_s,\mathbf{u}^k_s)$ of $T+1$ and $T$ elements, respectively.

Parameters

[in] recalc true for recalculating the derivatives at current state and control

Returns: The search direction $(\delta\mathbf{x},\delta\mathbf{u})$ and the dual lambdas as lists of , and lengths, respectively

Implements SolverAbstract.

Definition at line 89 of file kkt.cpp.

◆ tryStep()

double tryStep ( const double steplength = 1 )

virtual

Try a predefined step length $\alpha$ and compute its cost improvement $dV$ .

It uses the search direction found by computeDirection() to try a determined step length $\alpha$ . Therefore, it assumes that we have run computeDirection() first. Additionally, it returns the cost improvement $dV$ along the predefined step length $\alpha$ .

Parameters

[in] steplength applied step length ( $0\leq\alpha\leq1$ )

Returns: the cost improvement

Implements SolverAbstract.

Definition at line 119 of file kkt.cpp.

◆ stoppingCriteria()

double stoppingCriteria ( )

virtual

Return a positive value that quantifies the algorithm termination.

These values typically represents the gradient norm which tell us that it's been reached the local minima. The stopping criteria strictly speaking depends on the search direction (calculated by computeDirection()) but it could also depend on the chosen step length, tested by tryStep().

Implements SolverAbstract.

Definition at line 138 of file kkt.cpp.

◆ expectedImprovement()

const Eigen::Vector2d & expectedImprovement ( )

virtual

Return the expected improvement $dV_{exp}$ from a given current search direction $(\delta\mathbf{x}^k,\delta\mathbf{u}^k)$ .

For computing the expected improvement, you need to compute the search direction first via computeDirection().

Implements SolverAbstract.

Definition at line 165 of file kkt.cpp.

◆ get_kkt()

const Eigen::MatrixXd & get_kkt ( ) const

Definition at line 175 of file kkt.cpp.

◆ get_kktref()

const Eigen::VectorXd & get_kktref ( ) const

Definition at line 177 of file kkt.cpp.

◆ get_primaldual()

const Eigen::VectorXd & get_primaldual ( ) const

Definition at line 179 of file kkt.cpp.

◆ get_dxs()

const std::vector< Eigen::VectorXd > & get_dxs ( ) const

Definition at line 181 of file kkt.cpp.

◆ get_dus()

const std::vector< Eigen::VectorXd > & get_dus ( ) const

Definition at line 183 of file kkt.cpp.

◆ get_lambdas()

const std::vector< Eigen::VectorXd > & get_lambdas ( ) const

Definition at line 185 of file kkt.cpp.

◆ get_nx()

std::size_t get_nx ( ) const

Definition at line 189 of file kkt.cpp.

◆ get_ndx()

std::size_t get_ndx ( ) const

Definition at line 191 of file kkt.cpp.

◆ get_nu()

std::size_t get_nu ( ) const

Definition at line 193 of file kkt.cpp.

Member Data Documentation

◆ reg_incfactor_

double reg_incfactor_

protected

Definition at line 46 of file kkt.hpp.

◆ reg_decfactor_

double reg_decfactor_

protected

Definition at line 47 of file kkt.hpp.

◆ reg_min_

double reg_min_

protected

Definition at line 48 of file kkt.hpp.

◆ reg_max_

double reg_max_

protected

Definition at line 49 of file kkt.hpp.

◆ cost_try_

double cost_try_

protected

Definition at line 50 of file kkt.hpp.

◆ xs_try_

std::vector<Eigen::VectorXd> xs_try_

protected

Definition at line 51 of file kkt.hpp.

◆ us_try_

std::vector<Eigen::VectorXd> us_try_

protected

Definition at line 52 of file kkt.hpp.

The documentation for this class was generated from the following files:

/build/source/include/crocoddyl/core/solvers/kkt.hpp
/build/source/src/core/solvers/kkt.cpp

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ SolverKKT()

◆ ~SolverKKT()

Member Function Documentation

◆ solve()

◆ computeDirection()

◆ tryStep()

◆ stoppingCriteria()

◆ expectedImprovement()

◆ get_kkt()

◆ get_kktref()

◆ get_primaldual()

◆ get_dxs()

◆ get_dus()

◆ get_lambdas()

◆ get_nx()

◆ get_ndx()

◆ get_nu()

Member Data Documentation

◆ reg_incfactor_

◆ reg_decfactor_

◆ reg_min_

◆ reg_max_

◆ cost_try_

◆ xs_try_

◆ us_try_