Odyn-based Sequential Quadratic Programming (SQP) solver. More...

#include <odyn-sqp.hpp>

Inheritance diagram for SolverOdynSQPTpl< _Scalar >:

Public Types
typedef ShootingProblem::ActionDataAbstract	ActionDataAbstract
typedef ShootingProblem::ActionModelAbstract	ActionModelAbstract
typedef CallbackAbstractTpl< Scalar >	CallbackAbstract
using	Data = typename odyn::SparseDataTpl<Scalar>
typedef MathBaseTpl< Scalar >	MathBase
typedef MathBase::MatrixXs	MatrixXs
typedef MathBase::MatrixXsRowMajor	MatrixXsRowMajor
using	Model = typename odyn::SparseModelTpl<Scalar>
using	Params = typename odyn::ParamsTpl<Scalar>
typedef ShootingProblemTpl< Scalar >	ShootingProblem
using	Solver = typename odyn::SparseQPTpl<Scalar>
typedef SolverAbstractTpl< Scalar >	SolverAbstract
typedef MathBase::Vector3s	Vector3s
typedef MathBase::VectorXs	VectorXs
Public Types inherited from SolverAbstractTpl< _Scalar >
typedef ShootingProblem::ActionDataAbstract	ActionDataAbstract
typedef ShootingProblem::ActionModelAbstract	ActionModelAbstract
typedef CallbackAbstractTpl< Scalar >	CallbackAbstract
typedef MathBaseTpl< Scalar >	MathBase
typedef ShootingProblemTpl< Scalar >	ShootingProblem
typedef MathBase::Vector3s	Vector3s
typedef MathBase::VectorXs	VectorXs

Public Member Functions
	SolverOdynSQPTpl (std::shared_ptr< ShootingProblem > problem)
	Initialize the OdynSQP solver.
template<typename NewScalar>
SolverOdynSQPTpl< NewScalar >	cast () const
	Cast the OdynSQP solver to a different scalar type.
virtual bool	checkAcceptance () override
	Check if we should accept or not the step.
virtual void	computeCandidate (const Scalar step_length=Scalar(1.)) override
	Compute new candidate solution using step length.
virtual void	computeDirection (const bool recalc=true) override
	Compute the search direction $(\delta\mathbf{x}^k,\delta\mathbf{u}^k)$ for the current guess $(\mathbf{x}^k_s,\mathbf{u}^k_s)$ .
Scalar	computeDynamicFeasibility ()
	Compute the dynamic feasibility $\\|\mathbf{f}_{\mathbf{s}}\\|_{\infty,1}$ for the current guess $(\mathbf{x}^k,\mathbf{u}^k)$ .
Scalar	computeEqualityFeasibility ()
	Compute the feasibility of the equality constraints for the current guess.
virtual void	computeExpectedMeritFunctionImprovement () override
	Compute the expected merit function improvement for the current step.
Scalar	computeFeasibility (const std::vector< VectorXs > &fs)
	Compute the feasibility from a given residual vector.
Scalar	computeInequalityFeasibility ()
	Compute the feasibility of the inequality constraints for the current guess.
virtual void	computeMeritFunctionImprovement () override
	Compute the merit function improvement for the current step.
void	computeQuadraticModel ()
	Build the local Quadratic Program (QP) for the current iterate.
void	decreaseRegularization () override
	Decrease the state and control regularization values by a regfactor_ factor.
bool	decreaseRegularizationCriteria () override
	Criteria used to decrease regularization.
virtual Vector3s	expectedImprovement () override
	Return the expected improvement $dV_{exp}$ from a given current search direction $(\delta\mathbf{x}^k,\delta\mathbf{u}^k)$ .
void	extractQpDirection (const VectorXs &x)
	Extract the QP direction into the solver data structures.
std::size_t	get_m () const noexcept
	Return the number of equality constraints in the SQP.
std::size_t	get_n () const noexcept
	Return the number of decision variables in the SQP.
std::size_t	get_p () const noexcept
	Return the number of inequality constraints in the SQP.
Scalar	get_reg_decfactor () const
	Return the regularization factor used to decrease the damping value.
Scalar	get_reg_incfactor () const
	Return the regularization factor used to increase the damping value.
Scalar	get_rho () const
	Return the rho parameter used in the merit function.
Scalar	get_th_acceptminstep () const
	Return the threshold used for accepting minimum steps.
Scalar	get_th_acceptnegstep () const
	Return the threshold used for accepting step along ascent direction.
Scalar	get_th_grad () const
	Return the tolerance of the expected gradient used for testing the step.
Scalar	get_th_minfeas () const
	Return the threshold for switching to feasibility.
Scalar	get_th_minimprove () const
	Return the minimum improvement threshold used to increase regularization.
Scalar	get_th_stepdec () const
	Return the step-length threshold used to decrease regularization.
Scalar	get_th_stepinc () const
	Return the step-length threshold used to increase regularization.
Scalar	get_upsilon () const
	Return the estimated penalty parameter that balances relative contribution of the cost function and equality constraints.
Scalar	get_upsilon_decfactor () const
	Return the upsilon decresing factor used to estimate to balance optimality and feasibility.
odyn::VerboseLevel	get_verbose_level () const noexcept
	Return the verbose level used in the OdynSQP solver.
bool	get_zero_upsilon () const
	Return the zero-upsilon label.
void	increaseRegularization () override
	Increase the state and control regularization values by a regfactor_ factor.
bool	increaseRegularizationCriteria () override
	Criteria used to increase regularization.
Data &	qp_data () noexcept
	Return the QP data used in the OdynSQP solver.
Model &	qp_model () noexcept
	Return the QP model used in the OdynSQP solver.
Params &	qp_params () noexcept
	Return the QP params used in the OdynSQP solver.
virtual void	resizeData ()
	Resizing the solver data.
void	set_qp_params (const Params &params)
	Modify the QP params used in the OdynSQP solver.
void	set_reg_decfactor (const Scalar reg_factor)
	Modify the regularization factor used to decrease the damping value.
void	set_reg_incfactor (const Scalar reg_factor)
	Modify the regularization factor used to increase the damping value.
void	set_rho (const Scalar rho)
	Modify the rho parameter used in the merit function.
void	set_th_acceptminstep (const Scalar th_acceptminstep)
	Modify the threshold used for accepting minimum steps.
void	set_th_acceptnegstep (const Scalar th_acceptnegstep)
	Modify the threshold used for accepting step along ascent direction.
void	set_th_grad (const Scalar th_grad)
	Modify the tolerance of the expected gradient used for testing the step.
void	set_th_minfeas (const Scalar th_minfeas)
	Modify the threshold for switching to feasibility.
void	set_th_minimprove (const Scalar th_step)
	Modify the minimum improvement threshold used to increase regularization.
void	set_th_noimprovement (const Scalar th_noimprovement)
	Modify the threshold used to accept steps that cannot be be improved due to numerical errors the th noimprovement object.
void	set_th_stepdec (const Scalar th_step)
	Modify the step-length threshold used to decrease regularization.
void	set_th_stepinc (const Scalar th_step)
	Modify the step-length threshold used to increase regularization.
void	set_upsilon_decfactor (const Scalar th_step)
	Modify the upsilon decresing factor used to estimate to balance optimality and feasibility.
void	set_verbose_level (const odyn::VerboseLevel verbose_level)
	Modify the verbose level used in the OdynSQP solver.
void	set_zero_upsilon (const bool zero_upsilon)
	Modify the zero-upsilon label.
virtual Scalar	stoppingCriteria () override
	Return a positive value that quantifies the algorithm termination.
void	updateCandidate () override
	Update the candidate solution: cost, feasibilities, and merit value.
virtual void	updateMeritFunction () override
	Update the merit function value for the current guess.
Public Member Functions inherited from SolverAbstractTpl< _Scalar >
	SolverAbstractTpl (std::shared_ptr< ShootingProblem > problem)
	Initialize the solver.
virtual void	calcDir ()
	Refresh the linearization of the optimal control problem around the current candidate.
Scalar	computeControlFeasibility (const std::vector< VectorXs > &us)
	Compute the control box-constraint feasibility from a given control trajectory.
Scalar	computeDynamicFeasibility ()
	Compute the dynamic feasibility $\\|\mathbf{f}_{\mathbf{s}}\\|_{\infty,1}$ for the current guess $(\mathbf{x}^k,\mathbf{u}^k)$ .
Scalar	computeEqualityFeasibility ()
	Compute the feasibility of the equality constraints for the current guess.
Scalar	computeFeasibility (const std::vector< VectorXs > &fs)
	Compute the feasibility from a given residual vector.
Scalar	computeInequalityFeasibility ()
	Compute the feasibility of the inequality constraints for the current guess.
Scalar	computeStateFeasibility (const std::vector< VectorXs > &xs)
	Compute the state box-constraint feasibility from a given state trajectory.
	DEPRECATED ("Do not use this threshold. It is not needed by our solvers", Scalar get_th_gaptol() const { return th_gaptol_;}) FeasibilityNorm get_feasnorm() const
	Return the threshold for accepting a gap as non-zero.
	DEPRECATED ("Do not use this threshold. It is not needed by our solvers", void set_th_gaptol(const Scalar th_gaptol);) void set_feasnorm(const FeasibilityNorm feas_norm)
	Modify the threshold for accepting a gap as non-zero.
	DEPRECATED ("Use get_preg for primal-variable regularization", Scalar get_xreg() const { return preg_;}) DEPRECATED("Use get_preg for primal-variable regularization"
	DEPRECATED ("Use set_preg for primal-variable regularization", void set_xreg(const Scalar xreg) { if(xreg< Scalar(0.)) { throw_pretty("Invalid argument: "<< "xreg value has to be positive.");} xreg_=xreg;preg_=xreg;}) DEPRECATED("Use set_preg for primal-variable regularization"
const std::vector< Scalar > &	get_alphas () const
	Return the set of step lengths using by the line-search procedure.
Scalar	get_cost () const
	Return the cost for the current guess.
Scalar	get_dfeas () const
	Return the reduction in the feasibility.
Scalar	get_dPhi () const
	Return the reduction in the merit function $\Delta\Phi$ .
Scalar	get_dPhiexp () const
	Return the expected reduction in the merit function $\Delta\Phi_{exp}$ .
Scalar	get_dreg () const
	Return the dual-variable regularization.
const std::vector< VectorXs > &	get_dus () const
	Return the feedforward gains $\delta\mathbf{u}_s$ .
const Vector3s &	get_DV () const
	Return the constant, linear, and quadratic terms of the expected improvement.
Scalar	get_dV () const
	Return the reduction in the cost function $\Delta V$ .
Scalar	get_dVexp () const
	Return the expected reduction in the cost function $\Delta V_{exp}$ .
const std::vector< VectorXs > &	get_dxs () const
	Return the linear update in $\delta\mathbf{x}_s$ .
Scalar	get_feas () const
	Return the total feasibility for the current guess.
Scalar	get_ffeas () const
	Return the dynamic feasibility for the current guess.
Scalar	get_ffeas_try () const
	Return the dynamic feasibility for the current step length.
const std::vector< VectorXs > &	get_fs () const
	Return the dynamic infeasibility $\mathbf{f}_{s}$ .
const std::vector< VectorXs > &	get_fs_try () const
	Return the trail dynamic infeasibility $\mathbf{f}_{s}$ .
Scalar	get_gfeas () const
	Return the inequality feasibility for the current guess.
Scalar	get_gfeas_try () const
	Return the inequality feasibility for the current step length.
Scalar	get_hfeas () const
	Return the equality feasibility for the current guess.
Scalar	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.
Scalar	get_merit () const
	Return the merit for the current guess.
Scalar	get_preg () const
	Return the primal-variable regularization.
const std::shared_ptr< ShootingProblem > &	get_problem () const
	Return the shooting problem.
Scalar	get_reg_max () const
	Return the maximum regularization value.
Scalar	get_reg_min () const
	Return the minimum regularization value.
Scalar	get_steplength () const
	Return the step length $\alpha$ .
Scalar	get_stop () const
	Return the stopping-criteria value computed by stoppingCriteria().
Scalar	get_th_acceptstep () const
	Return the threshold used for accepting a step.
Scalar	get_th_stop () const
	Return the tolerance for stopping the algorithm.
Scalar	get_ureg () const
const std::vector< VectorXs > &	get_us () const
	Return the control trajectory $\mathbf{u}_s$ .
const std::vector< VectorXs > &	get_us_try () const
	Return the trial control trajectory $\mathbf{u}_s$ .
const std::vector< VectorXs > &	get_xs () const
	Return the state trajectory $\mathbf{x}_s$ .
const std::vector< VectorXs > &	get_xs_try () const
	Return the trial state trajectory $\mathbf{x}_s$ .
const std::vector< std::shared_ptr< CallbackAbstract > > &	getCallbacks () const
	Return the list of callback functions using for diagnostic.
void	set_alphas (const std::vector< Scalar > &alphas)
	Modify the set of step lengths using by the line-search procedure.
void	set_dreg (const Scalar dreg)
	Modify the dual-variable regularization value.
void	set_preg (const Scalar preg)
	Modify the primal-variable regularization value.
void	set_reg_max (const Scalar regmax)
	Modify the maximum regularization value.
void	set_reg_min (const Scalar regmin)
	Modify the minimum regularization value.
void	set_th_acceptstep (const Scalar th_acceptstep)
	Modify the threshold used for accepting step.
void	set_th_stop (const Scalar th_stop)
	Modify the tolerance for stopping the algorithm.
void	set_ureg (const Scalar ureg)
void	set_us (const std::vector< VectorXs > &us)
	Modify the control trajectory $\mathbf{u}_s$ .
void	set_xs (const std::vector< VectorXs > &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.
virtual void	setCandidate (const std::vector< VectorXs > &xs_warm=DefaultVector< Scalar >::value, const std::vector< VectorXs > &us_warm=DefaultVector< Scalar >::value, const bool is_feasible=false)
	Set the solver candidate trajectories $(\mathbf{x}_s,\mathbf{u}_s)$ .
virtual bool	solve (const std::vector< VectorXs > &init_xs=DefaultVector< Scalar >::value, const std::vector< VectorXs > &init_us=DefaultVector< Scalar >::value, const std::size_t maxiter=100, const bool is_feasible=false, const Scalar reg_init=std::numeric_limits< Scalar >::quiet_NaN())
	Compute the optimal trajectory $\mathbf{x}^_s,\mathbf{u}^_s$ as lists of and terms.
virtual Scalar	tryStep (const Scalar steplength=Scalar(1.))
	Try a predefined step length $\alpha$ and compute its cost improvement and merit improvement $d\Phi$ .

Public Attributes
EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef _Scalar	Scalar
Public Attributes inherited from SolverAbstractTpl< _Scalar >
EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef _Scalar	Scalar

Protected Member Functions
void	allocateData ()
	Allocate all the internal data needed for the solver.
virtual void	resizeRunningData () override
	Resize data associated with the running models of the shooting problem.
virtual void	resizeTerminalData () override
	Resize data associated with the terminal model of the shooting problem.
void	updateStateAndControlIndex ()
Protected Member Functions inherited from SolverAbstractTpl< _Scalar >
void	allocateData ()
	Allocate all the basic data needed in solvers.
	DEPRECATED ("Do not use this threshold. It is not needed by our solvers", Scalar th_gaptol_;) enum FeasibilityNorm feasnorm_
	DEPRECATED ("Use preg_ for primal-variable regularization", Scalar xreg_;) DEPRECATED("Use dreg_ for primal-variable regularization"

Protected Attributes
bool	acceptstep_
std::vector< Scalar >	alphas_
	Set of step lengths using by the line-search procedure.
std::vector< std::shared_ptr< CallbackAbstract > >	callbacks_
	Callback functions.
Scalar	cost_
	Cost for the current guess.
Scalar	cost_try_
	Total cost computed by line-search procedure.
Scalar	dfeas_
	Reduction in the feasibility.
Scalar	dImpr_
Scalar	dPhi_
	Reduction in the merit function computed by tryStep().
Scalar	dPhiexp_
Scalar	dreg_
	Current dual-variable regularization value.
std::vector< VectorXs >	dus_
	Linear control direction (size T).
Vector3s	DV_
	LQ approximation of the expected improvement.
Scalar	dV_
	Reduction in the cost function computed by tryStep().
Scalar	dVexp_
Scalar	dVexp_full_
std::vector< VectorXs >	dxs_
	Linear state direction (size T + 1).
Scalar	feas_
	Total feasibility for the current guess.
Scalar	ffeas_
	Feasibility of the dynamic constraints for the current guess.
Scalar	ffeas_try_
std::vector< VectorXs >	fs_
	Dynamics gaps in each node.
std::vector< VectorXs >	fs_try_
Scalar	gfeas_
Scalar	gfeas_try_
Scalar	hfeas_
Scalar	hfeas_try_
bool	is_feasible_
	Label that indicates is the iteration is feasible.
std::size_t	iter_
	Number of iteration performed by the solver.
std::vector< VectorXs >	Luu_du_
std::vector< VectorXs >	Lxu_du_
std::vector< VectorXs >	Lxx_dx_
std::size_t	m_
Scalar	merit_
	Merit for the current guess.
std::size_t	n_
std::size_t	ng_T_
	Dimension of termianl inequality constraints.
std::size_t	nh_T_
	Dimension of terminal equality constraints.
std::size_t	p_
Scalar	preg_
	Current primal-variable regularization value.
std::shared_ptr< ShootingProblem >	problem_
	optimal control problem
Data	qp_data_
Model	qp_model_
Params	qp_params_
Solver	qp_solver_
Scalar	reg_decfactor_
Scalar	reg_incfactor_
Scalar	reg_max_
	Maximum allowed regularization value.
Scalar	reg_min_
	< Current control regularization values
Scalar	rho_
Scalar	steplength_
	Current applied step length.
Scalar	stop_
	Value computed by stoppingCriteria().
Scalar	th_acceptminstep_
Scalar	th_acceptnegstep_
Scalar	th_acceptstep_
	Threshold used for accepting step.
Scalar	th_grad_
Scalar	th_minfeas_
	Threshold for switching to feasibility.
Scalar	th_minimprove_
Scalar	th_noimprovement_
Scalar	th_stepdec_
	Step-length threshold used to decrease regularization.
Scalar	th_stepinc_
	Step-length threshold used to increase regularization.
Scalar	th_stop_
	Tolerance for stopping the algorithm.
Scalar	upsilon_
Scalar	upsilon_decfactor_
std::vector< VectorXs >	us_
	Control trajectory.
std::vector< std::size_t >	us_idx_
std::vector< VectorXs >	us_try_
	Control trajectory computed by line-search procedure.
odyn::VerboseLevel	verbose_level_
VectorXs	x_
std::vector< VectorXs >	xs_
	State trajectory.
std::vector< std::size_t >	xs_idx_
std::vector< VectorXs >	xs_try_
	State trajectory computed by line-search procedure.
bool	zero_upsilon_
Protected Attributes inherited from SolverAbstractTpl< _Scalar >
bool	acceptstep_
std::vector< Scalar >	alphas_
	Set of step lengths using by the line-search procedure.
std::vector< std::shared_ptr< CallbackAbstract > >	callbacks_
	Callback functions.
Scalar	cost_
	Cost for the current guess.
Scalar	cost_try_
	Total cost computed by line-search procedure.
Scalar	dfeas_
	Reduction in the feasibility.
Scalar	dImpr_
Scalar	dPhi_
	Reduction in the merit function computed by tryStep().
Scalar	dPhiexp_
Scalar	dreg_
	Current dual-variable regularization value.
std::vector< VectorXs >	dus_
	Linear control direction (size T).
Vector3s	DV_
	LQ approximation of the expected improvement.
Scalar	dV_
	Reduction in the cost function computed by tryStep().
Scalar	dVexp_
Scalar	dVexp_full_
std::vector< VectorXs >	dxs_
	Linear state direction (size T + 1).
Scalar	feas_
	Total feasibility for the current guess.
Scalar	ffeas_
	Feasibility of the dynamic constraints for the current guess.
Scalar	ffeas_try_
std::vector< VectorXs >	fs_
	Dynamics gaps in each node.
std::vector< VectorXs >	fs_try_
std::vector< VectorXs >	g_adj_
	Adjusted inequality bound.
Scalar	gfeas_
Scalar	gfeas_try_
Scalar	hfeas_
Scalar	hfeas_try_
bool	is_feasible_
	Label that indicates is the iteration is feasible.
std::size_t	iter_
	Number of iteration performed by the solver.
Scalar	merit_
	Merit for the current guess.
std::size_t	ng_T_
	Dimension of termianl inequality constraints.
std::size_t	nh_T_
	Dimension of terminal equality constraints.
Scalar	preg_
	Current primal-variable regularization value.
std::shared_ptr< ShootingProblem >	problem_
	optimal control problem
bool	recalcdir_
Scalar	reg_max_
	Maximum allowed regularization value.
Scalar	reg_min_
	< Current control regularization values
Scalar	steplength_
	Current applied step length.
Scalar	stop_
	Value computed by stoppingCriteria().
Scalar	th_acceptstep_
	Threshold used for accepting step.
Scalar	th_stop_
	Tolerance for stopping the algorithm.
Scalar	tmp_feas_
	Temporal variables used for computed the feasibility.
std::vector< VectorXs >	u_adj_
	Adjusted control bound.
Scalar	ureg_
std::vector< VectorXs >	us_
	Control trajectory.
std::vector< VectorXs >	us_try_
	Control trajectory computed by line-search procedure.
bool	was_feasible_
std::vector< VectorXs >	x_adj_
	Adjusted state bound.
std::vector< VectorXs >	xs_
	State trajectory.
std::vector< VectorXs >	xs_try_
	State trajectory computed by line-search procedure.

Detailed Description

template<typename _Scalar>
class crocoddyl::SolverOdynSQPTpl< _Scalar >

Odyn-based Sequential Quadratic Programming (SQP) solver.

This solver wraps Odyn’s sparse QP engine to solve Crocoddyl shooting problems with equality and inequality constraints. At each iteration it builds a sparse QP in computeQuadraticModel(), solves it with Odyn, and then maps the QP step back to state/control updates in computeSearchDirection() / tryStep(). The try step supports infeasible iterates (open dynamics gaps) and line-searches them, which improves globalization on hard-constrained problems. The expected improvement calculation also accounts for the dynamics gaps.

See SolverAbstract(), computeSearchDirection(), tryStep(), and expectedImprovement() for the main iteration steps.

Definition at line 38 of file odyn-sqp.hpp.

Member Typedef Documentation

◆ SolverAbstract

template<typename _Scalar>

typedef SolverAbstractTpl<Scalar> SolverAbstract

Definition at line 44 of file odyn-sqp.hpp.

◆ ShootingProblem

template<typename _Scalar>

typedef ShootingProblemTpl<Scalar> ShootingProblem

Definition at line 45 of file odyn-sqp.hpp.

◆ ActionModelAbstract

template<typename _Scalar>

typedef ShootingProblem::ActionModelAbstract ActionModelAbstract

Definition at line 46 of file odyn-sqp.hpp.

◆ ActionDataAbstract

template<typename _Scalar>

typedef ShootingProblem::ActionDataAbstract ActionDataAbstract

Definition at line 47 of file odyn-sqp.hpp.

◆ CallbackAbstract

template<typename _Scalar>

typedef CallbackAbstractTpl<Scalar> CallbackAbstract

Definition at line 48 of file odyn-sqp.hpp.

◆ MathBase

template<typename _Scalar>

typedef MathBaseTpl<Scalar> MathBase

Definition at line 49 of file odyn-sqp.hpp.

◆ VectorXs

template<typename _Scalar>

typedef MathBase::VectorXs VectorXs

Definition at line 50 of file odyn-sqp.hpp.

◆ Vector3s

template<typename _Scalar>

typedef MathBase::Vector3s Vector3s

Definition at line 51 of file odyn-sqp.hpp.

◆ MatrixXs

template<typename _Scalar>

typedef MathBase::MatrixXs MatrixXs

Definition at line 52 of file odyn-sqp.hpp.

◆ MatrixXsRowMajor

template<typename _Scalar>

typedef MathBase::MatrixXsRowMajor MatrixXsRowMajor

Definition at line 53 of file odyn-sqp.hpp.

◆ Solver

template<typename _Scalar>

using Solver = typename odyn::SparseQPTpl<Scalar>

Definition at line 54 of file odyn-sqp.hpp.

◆ Model

template<typename _Scalar>

using Model = typename odyn::SparseModelTpl<Scalar>

Definition at line 55 of file odyn-sqp.hpp.

◆ Data

template<typename _Scalar>

using Data = typename odyn::SparseDataTpl<Scalar>

Definition at line 56 of file odyn-sqp.hpp.

◆ Params

template<typename _Scalar>

using Params = typename odyn::ParamsTpl<Scalar>

Definition at line 57 of file odyn-sqp.hpp.

Constructor & Destructor Documentation

◆ SolverOdynSQPTpl()

template<typename _Scalar>

SolverOdynSQPTpl ( std::shared_ptr< ShootingProblem > problem )

explicit

Initialize the OdynSQP solver.

Parameters

[in]	problem	Shooting problem
[in]	dyn_solver	Type of dynamic solver
[in]	term_solver	Type of terminal solver

Member Function Documentation

◆ computeDirection()

template<typename _Scalar>

virtual void computeDirection ( const bool recalc = true )

overridevirtual

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

Implements SolverAbstractTpl< _Scalar >.

◆ stoppingCriteria()

template<typename _Scalar>

virtual Scalar stoppingCriteria ( )

overridevirtual

Return a positive value that quantifies the algorithm termination.

Implements SolverAbstractTpl< _Scalar >.

◆ expectedImprovement()

template<typename _Scalar>

virtual Vector3s expectedImprovement ( )

overridevirtual

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

Implements SolverAbstractTpl< _Scalar >.

◆ computeMeritFunctionImprovement()

template<typename _Scalar>

virtual void computeMeritFunctionImprovement ( )

overridevirtual

Compute the merit function improvement for the current step.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ computeExpectedMeritFunctionImprovement()

template<typename _Scalar>

virtual void computeExpectedMeritFunctionImprovement ( )

overridevirtual

Compute the expected merit function improvement for the current step.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ updateMeritFunction()

template<typename _Scalar>

virtual void updateMeritFunction ( )

overridevirtual

Update the merit function value for the current guess.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ checkAcceptance()

template<typename _Scalar>

virtual bool checkAcceptance ( )

overridevirtual

Check if we should accept or not the step.

Returns: True if we should accept the step. False otherwise

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ computeCandidate()

template<typename _Scalar>

virtual void computeCandidate ( const Scalar step_length = Scalar(1.) )

overridevirtual

Compute new candidate solution using step length.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ computeQuadraticModel()

template<typename _Scalar>

void computeQuadraticModel ( )

Build the local Quadratic Program (QP) for the current iterate.

The QP has the form

$\‍[ \min_{x}\ \tfrac{1}{2}\, x^\top Q\, x \;+\; c^\top x \quad\text{s.t.}\quad A\,x = b,\;\; G\,x \le h . \‍]$

Here, x is the full decision vector over the trajectory and controls, and Q, c, A, b, G, h are the corresponding (sparse, structured) cost and constraint matrices/vectors induced by the problem’s temporal layout.

Decision vector ordering:

$\‍[ x = [\, \Delta x_0,\ \Delta u_0,\ \Delta x_1,\ \Delta u_1,\ \ldots,\ \Delta x_T \,]. \‍]$

The sparsity pattern reflects time-coupling (dynamics and local costs/constraints).

Template Parameters

Scalar Scalar type (e.g., float, double).

◆ updateCandidate()

template<typename _Scalar>

void updateCandidate ( )

overridevirtual

Update the candidate solution: cost, feasibilities, and merit value.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ decreaseRegularizationCriteria()

template<typename _Scalar>

bool decreaseRegularizationCriteria ( )

overridevirtual

Criteria used to decrease regularization.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ increaseRegularizationCriteria()

template<typename _Scalar>

bool increaseRegularizationCriteria ( )

overridevirtual

Criteria used to increase regularization.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ increaseRegularization()

template<typename _Scalar>

void increaseRegularization ( )

overridevirtual

Increase the state and control regularization values by a regfactor_ factor.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ decreaseRegularization()

template<typename _Scalar>

void decreaseRegularization ( )

overridevirtual

Decrease the state and control regularization values by a regfactor_ factor.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ extractQpDirection()

template<typename _Scalar>

void extractQpDirection ( const VectorXs & x )

Extract the QP direction into the solver data structures.

Parameters

[in] x decision vector from the QP solver

◆ cast()

template<typename _Scalar>

template<typename NewScalar>

SolverOdynSQPTpl< NewScalar > cast ( ) const

Cast the OdynSQP solver to a different scalar type.

It is useful for operations requiring different precision or scalar types.

Template Parameters

NewScalar The new scalar type to cast to.

Returns: SolverOdynSQPTpl<NewScalar> A OdynSQP solver with the new scalar type.

◆ get_zero_upsilon()

template<typename _Scalar>

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.

◆ set_zero_upsilon()

template<typename _Scalar>

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.

◆ resizeRunningData()

template<typename _Scalar>

virtual void resizeRunningData ( )

overrideprotectedvirtual

Resize data associated with the running models of the shooting problem.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ resizeTerminalData()

template<typename _Scalar>

virtual void resizeTerminalData ( )

overrideprotectedvirtual

Resize data associated with the terminal model of the shooting problem.

Reimplemented from SolverAbstractTpl< _Scalar >.

◆ computeDynamicFeasibility()

template<typename _Scalar>

Scalar computeDynamicFeasibility ( )

Compute the dynamic feasibility $\|\mathbf{f}_{\mathbf{s}}\|_{\infty,1}$ for the current guess $(\mathbf{x}^k,\mathbf{u}^k)$ .

The feasibility can be computed using different norms (e.g, $\ell_\infty$ or $\ell_1$ norms). By default we use the $\ell_\infty$ norm, however, we can change the type of norm using set_feasnorm. Note that $\mathbf{f}_{\mathbf{s}}$ are the gaps on the dynamics, which are computed at each node as $\mathbf{x}^{'}-\mathbf{f}(\mathbf{x},\mathbf{u})$ .

◆ computeEqualityFeasibility()

template<typename _Scalar>

Scalar computeEqualityFeasibility ( )

Compute the feasibility of the equality constraints for the current guess.

The feasibility can be computed using different norms (e.g, $\ell_\infty$ or $\ell_1$ norms). By default we use the $\ell_\infty$ norm, however, we can change the type of norm using set_feasnorm.

◆ computeFeasibility()

template<typename _Scalar>

Scalar computeFeasibility ( const std::vector< VectorXs > & fs )

Compute the feasibility from a given residual vector.

As in the computeDynamicFeasibility, computeInequalityFeasibility or computeEqualityFeasibility, we can compute the feasibility using different norms (e.g, $\ell_\infty$ or $\ell_1$ norms). By default we use the $\ell_\infty$ norm, however, we can change the type of norm using set_feasnorm.

Parameters

[in] fs Vector of residual vectors which we wish to compute the feasibility

Returns: the residuals' feasibility

◆ computeInequalityFeasibility()

template<typename _Scalar>

Scalar computeInequalityFeasibility ( )

Compute the feasibility of the inequality constraints for the current guess.

The feasibility can be computed using different norms (e.g, $\ell_\infty$ or $\ell_1$ norms). By default we use the $\ell_\infty$ norm, however, we can change the type of norm using set_feasnorm.

◆ resizeData()

template<typename _Scalar>

virtual void resizeData ( )

virtual

Resizing the solver data.

If the shooting problem has changed after construction, then this function resizes all the data before starting resolve the problem.

Reimplemented from SolverAbstractTpl< _Scalar >.

Member Data Documentation

◆ Scalar

template<typename _Scalar>

EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef _Scalar Scalar

Definition at line 43 of file odyn-sqp.hpp.

◆ reg_incfactor_

template<typename _Scalar>

Scalar reg_incfactor_

protected

Regularization factor used to increase the damping value

Definition at line 397 of file odyn-sqp.hpp.

◆ reg_decfactor_

template<typename _Scalar>

Scalar reg_decfactor_

protected

Regularization factor used to decrease the damping value

Definition at line 399 of file odyn-sqp.hpp.

◆ th_grad_

template<typename _Scalar>

Scalar th_grad_

protected

Tolerance of the expected gradient used for testing the step

Definition at line 401 of file odyn-sqp.hpp.

◆ th_noimprovement_

template<typename _Scalar>

Scalar th_noimprovement_

protected

Threshold used to accept steps that cannot be be improved due to numerical errors

Definition at line 403 of file odyn-sqp.hpp.

◆ th_stepdec_

template<typename _Scalar>

Scalar th_stepdec_

protected

Step-length threshold used to decrease regularization.

Definition at line 406 of file odyn-sqp.hpp.

◆ th_stepinc_

template<typename _Scalar>

Scalar th_stepinc_

protected

Step-length threshold used to increase regularization.

Definition at line 408 of file odyn-sqp.hpp.

◆ th_minimprove_

template<typename _Scalar>

Scalar th_minimprove_

protected

Minimum improvement threshold used in the regularization scheme

Definition at line 409 of file odyn-sqp.hpp.

◆ th_acceptnegstep_

template<typename _Scalar>

Scalar th_acceptnegstep_

protected

Threshold used for accepting step along ascent direction

Definition at line 411 of file odyn-sqp.hpp.

◆ th_acceptminstep_

template<typename _Scalar>

Scalar th_acceptminstep_

protected

Threshold used for accepting step along with a minimum length

Definition at line 413 of file odyn-sqp.hpp.

◆ rho_

template<typename _Scalar>

Scalar rho_

protected

Parameter used in the merit function to predict the expected reduction

Definition at line 415 of file odyn-sqp.hpp.

◆ th_minfeas_

template<typename _Scalar>

Scalar th_minfeas_

protected

Threshold for switching to feasibility.

Definition at line 417 of file odyn-sqp.hpp.

◆ upsilon_

template<typename _Scalar>

Scalar upsilon_

protected

Estimated penalty parameter that balances relative contribution of the cost function and equality constraints

Definition at line 419 of file odyn-sqp.hpp.

◆ upsilon_decfactor_

template<typename _Scalar>

Scalar upsilon_decfactor_

protected

Estimated penalty parameter factor used to decrease its value

Definition at line 421 of file odyn-sqp.hpp.

◆ zero_upsilon_

template<typename _Scalar>

bool zero_upsilon_

protected

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

Definition at line 423 of file odyn-sqp.hpp.

◆ n_

template<typename _Scalar>

std::size_t n_

protected

Definition at line 426 of file odyn-sqp.hpp.

◆ m_

template<typename _Scalar>

std::size_t m_

protected

Definition at line 427 of file odyn-sqp.hpp.

◆ p_

template<typename _Scalar>

std::size_t p_

protected

Definition at line 428 of file odyn-sqp.hpp.

◆ x_

template<typename _Scalar>

VectorXs x_

protected

Definition at line 429 of file odyn-sqp.hpp.

◆ qp_solver_

template<typename _Scalar>

Solver qp_solver_

protected

Definition at line 430 of file odyn-sqp.hpp.

◆ qp_model_

template<typename _Scalar>

Model qp_model_

protected

Definition at line 431 of file odyn-sqp.hpp.

◆ qp_data_

template<typename _Scalar>

Data qp_data_

protected

Definition at line 432 of file odyn-sqp.hpp.

◆ qp_params_

template<typename _Scalar>

Params qp_params_

protected

Definition at line 433 of file odyn-sqp.hpp.

◆ verbose_level_

template<typename _Scalar>

odyn::VerboseLevel verbose_level_

protected

Definition at line 434 of file odyn-sqp.hpp.

◆ xs_idx_

template<typename _Scalar>

std::vector<std::size_t> xs_idx_

protected

Definition at line 435 of file odyn-sqp.hpp.

◆ us_idx_

template<typename _Scalar>

std::vector<std::size_t> us_idx_

protected

Definition at line 436 of file odyn-sqp.hpp.

◆ Lxx_dx_

template<typename _Scalar>

std::vector<VectorXs> Lxx_dx_

protected

Second-order change of the cost function $\boldsymbol{\ell}_{\mathbf{{xx}}}\delta\mathbf{x}$

Definition at line 438 of file odyn-sqp.hpp.

◆ Luu_du_

template<typename _Scalar>

std::vector<VectorXs> Luu_du_

protected

Second-order change of the cost function $\boldsymbol{\ell}_{\mathbf{{uu}}}\delta\mathbf{u}$

Definition at line 441 of file odyn-sqp.hpp.

◆ Lxu_du_

template<typename _Scalar>

std::vector<VectorXs> Lxu_du_

protected

Second-order change of the cost function $\boldsymbol{\ell}_{\mathbf{{xu}}}\delta\mathbf{u}$

Definition at line 444 of file odyn-sqp.hpp.

◆ acceptstep_

template<typename _Scalar>

bool acceptstep_

protected

Definition at line 771 of file solver-base.hpp.

◆ alphas_

template<typename _Scalar>

std::vector<Scalar> alphas_

protected

Set of step lengths using by the line-search procedure.

Definition at line 702 of file solver-base.hpp.

◆ callbacks_

template<typename _Scalar>

std::vector<std::shared_ptr<CallbackAbstract> > callbacks_

protected

Callback functions.

Definition at line 700 of file solver-base.hpp.

◆ cost_

template<typename _Scalar>

Scalar cost_

protected

Cost for the current guess.

Definition at line 727 of file solver-base.hpp.

◆ cost_try_

template<typename _Scalar>

Scalar cost_try_

protected

Total cost computed by line-search procedure.

Definition at line 728 of file solver-base.hpp.

◆ dfeas_

template<typename _Scalar>

Scalar dfeas_

protected

Reduction in the feasibility.

Definition at line 741 of file solver-base.hpp.

◆ dImpr_

template<typename _Scalar>

Scalar dImpr_

protected

Reduction in the iteration improvement (i.e., maximum between cost and merit values)

Definition at line 711 of file solver-base.hpp.

◆ dPhi_

template<typename _Scalar>

Scalar dPhi_

protected

Reduction in the merit function computed by tryStep().

Definition at line 734 of file solver-base.hpp.

◆ dPhiexp_

template<typename _Scalar>

Scalar dPhiexp_

protected

Expected reduction in the merit function for the selected step length

Definition at line 739 of file solver-base.hpp.

◆ dreg_

template<typename _Scalar>

Scalar dreg_

protected

Current dual-variable regularization value.

Definition at line 757 of file solver-base.hpp.

◆ dus_

template<typename _Scalar>

std::vector<VectorXs> dus_

protected

Linear control direction (size T).

Definition at line 723 of file solver-base.hpp.

◆ DV_

template<typename _Scalar>

Vector3s DV_

protected

LQ approximation of the expected improvement.

Definition at line 732 of file solver-base.hpp.

◆ dV_

template<typename _Scalar>

Scalar dV_

protected

Reduction in the cost function computed by tryStep().

Definition at line 733 of file solver-base.hpp.

◆ dVexp_

template<typename _Scalar>

Scalar dVexp_

protected

Expected reduction in the cost function for the selected step length

Definition at line 737 of file solver-base.hpp.

◆ dVexp_full_

template<typename _Scalar>

Scalar dVexp_full_

protected

Expected reduction in the cost function for a full step length

Definition at line 735 of file solver-base.hpp.

◆ dxs_

template<typename _Scalar>

std::vector<VectorXs> dxs_

protected

Linear state direction (size T + 1).

Definition at line 722 of file solver-base.hpp.

◆ feas_

template<typename _Scalar>

Scalar feas_

protected

Total feasibility for the current guess.

Definition at line 742 of file solver-base.hpp.

◆ ffeas_

template<typename _Scalar>

Scalar ffeas_

protected

Feasibility of the dynamic constraints for the current guess.

Definition at line 744 of file solver-base.hpp.

◆ ffeas_try_

template<typename _Scalar>

Scalar ffeas_try_

protected

Feasibility of the dynamic constraints evaluated for the current step length

Definition at line 749 of file solver-base.hpp.

◆ fs_

template<typename _Scalar>

std::vector<VectorXs> fs_

protected

Dynamics gaps in each node.

Definition at line 715 of file solver-base.hpp.

◆ fs_try_

template<typename _Scalar>

std::vector<VectorXs> fs_try_

protected

Dynamics gaps in each node computed by the line-search procedure

Definition at line 720 of file solver-base.hpp.

◆ gfeas_

template<typename _Scalar>

Scalar gfeas_

protected

Feasibility of the inequality constraints for the current guess

Definition at line 745 of file solver-base.hpp.

◆ gfeas_try_

template<typename _Scalar>

Scalar gfeas_try_

protected

Feasibility of the inequality constraints evaluated for the current step length

Definition at line 751 of file solver-base.hpp.

◆ hfeas_

template<typename _Scalar>

Scalar hfeas_

protected

Feasibility of the equality constraints for the current guess

Definition at line 747 of file solver-base.hpp.

◆ hfeas_try_

template<typename _Scalar>

Scalar hfeas_try_

protected

Feasibility of the equality constraints evaluated for the current step length

Definition at line 753 of file solver-base.hpp.

◆ is_feasible_

template<typename _Scalar>

bool is_feasible_

protected

Label that indicates is the iteration is feasible.

Definition at line 724 of file solver-base.hpp.

◆ iter_

template<typename _Scalar>

std::size_t iter_

protected

Number of iteration performed by the solver.

Definition at line 731 of file solver-base.hpp.

◆ merit_

template<typename _Scalar>

Scalar merit_

protected

Merit for the current guess.

Definition at line 729 of file solver-base.hpp.

◆ ng_T_

template<typename _Scalar>

std::size_t ng_T_

protected

Dimension of termianl inequality constraints.

Definition at line 769 of file solver-base.hpp.

◆ nh_T_

template<typename _Scalar>

std::size_t nh_T_

protected

Dimension of terminal equality constraints.

Definition at line 768 of file solver-base.hpp.

◆ preg_

template<typename _Scalar>

Scalar preg_

protected

Current primal-variable regularization value.

Definition at line 756 of file solver-base.hpp.

◆ problem_

template<typename _Scalar>

std::shared_ptr<ShootingProblem> problem_

protected

optimal control problem

Definition at line 698 of file solver-base.hpp.

◆ reg_max_

template<typename _Scalar>

Scalar reg_max_

protected

Maximum allowed regularization value.

Definition at line 763 of file solver-base.hpp.

◆ reg_min_

template<typename _Scalar>

Scalar reg_min_

protected

< Current control regularization values

Minimum allowed regularization value

Definition at line 762 of file solver-base.hpp.

◆ steplength_

template<typename _Scalar>

Scalar steplength_

protected

Current applied step length.

Definition at line 764 of file solver-base.hpp.

◆ stop_

template<typename _Scalar>

Scalar stop_

protected

Value computed by stoppingCriteria().

Definition at line 730 of file solver-base.hpp.

◆ th_acceptstep_

template<typename _Scalar>

Scalar th_acceptstep_

protected

Threshold used for accepting step.

Definition at line 703 of file solver-base.hpp.

◆ th_stop_

template<typename _Scalar>

Scalar th_stop_

protected

Tolerance for stopping the algorithm.

Definition at line 704 of file solver-base.hpp.

◆ us_

template<typename _Scalar>

std::vector<VectorXs> us_

protected

Control trajectory.

Definition at line 714 of file solver-base.hpp.

◆ us_try_

template<typename _Scalar>

std::vector<VectorXs> us_try_

protected

Control trajectory computed by line-search procedure.

Definition at line 719 of file solver-base.hpp.

◆ xs_

template<typename _Scalar>

std::vector<VectorXs> xs_

protected

State trajectory.

Definition at line 713 of file solver-base.hpp.

◆ xs_try_

template<typename _Scalar>

std::vector<VectorXs> xs_try_

protected

State trajectory computed by line-search procedure.

Definition at line 717 of file solver-base.hpp.

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

/build/source/include/crocoddyl/core/solvers/odyn-sqp.hpp

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ SolverAbstract

◆ ShootingProblem

◆ ActionModelAbstract

◆ ActionDataAbstract

◆ CallbackAbstract

◆ MathBase

◆ VectorXs

◆ Vector3s

◆ MatrixXs

◆ MatrixXsRowMajor

◆ Solver

◆ Model

◆ Data

◆ Params

Constructor & Destructor Documentation

◆ SolverOdynSQPTpl()

Member Function Documentation

◆ computeDirection()

◆ stoppingCriteria()

◆ expectedImprovement()

◆ computeMeritFunctionImprovement()

◆ computeExpectedMeritFunctionImprovement()

◆ updateMeritFunction()

◆ checkAcceptance()

◆ computeCandidate()

◆ computeQuadraticModel()

◆ updateCandidate()

◆ decreaseRegularizationCriteria()

◆ increaseRegularizationCriteria()

◆ increaseRegularization()

◆ decreaseRegularization()

◆ extractQpDirection()

◆ cast()

◆ get_zero_upsilon()

◆ set_zero_upsilon()

◆ resizeRunningData()

◆ resizeTerminalData()

◆ computeDynamicFeasibility()

◆ computeEqualityFeasibility()

◆ computeFeasibility()

◆ computeInequalityFeasibility()

◆ resizeData()

Member Data Documentation

◆ Scalar

◆ reg_incfactor_

◆ reg_decfactor_

◆ th_grad_

◆ th_noimprovement_

◆ th_stepdec_

◆ th_stepinc_

◆ th_minimprove_

◆ th_acceptnegstep_

◆ th_acceptminstep_

◆ rho_

◆ th_minfeas_

◆ upsilon_

◆ upsilon_decfactor_

◆ zero_upsilon_

◆ n_

◆ m_

◆ p_

◆ x_

◆ qp_solver_

◆ qp_model_

◆ qp_data_

◆ qp_params_

◆ verbose_level_

◆ xs_idx_

◆ us_idx_

◆ Lxx_dx_

◆ Luu_du_

◆ Lxu_du_

◆ acceptstep_