Ipopt solver. More...

#include <ipopt.hpp>

Inheritance diagram for SolverIpopt:

Public Types
typedef IpoptInterfaceTpl< Scalar >	IpoptInterface
typedef MathBaseTpl< Scalar >	MathBase
typedef ShootingProblemTpl< Scalar >	ShootingProblem
typedef SolverAbstractTpl< Scalar >	SolverAbstract
typedef MathBase::Vector3s	Vector3s
typedef MathBase::VectorXs	VectorXs
Public Types inherited from SolverAbstractTpl< Ipopt::Number >
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
	SolverIpopt (std::shared_ptr< ShootingProblem > problem)
	Initialize the Ipopt solver.
virtual void	resizeData () override
	Resizing the solver data.
void	set_th_stop (const Scalar th_stop)
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 $(\mathbf{x}_s,\mathbf{u}_s)$ .
void	setNumericIpoptOption (const std::string &tag, Ipopt::Number value)
	Set a string ipopt option.
void	setStringIpoptOption (const std::string &tag, const std::string &value)
	Set a string ipopt option.
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=Scalar(1e-9)) override
Public Member Functions inherited from SolverAbstractTpl< Ipopt::Number >
	SolverAbstractTpl (std::shared_ptr< ShootingProblem > problem)
	Initialize the solver.
virtual void	calcDir ()
	Refresh the linearization of the optimal control problem around the current candidate.
virtual bool	checkAcceptance ()
	Check if we should accept or not the step.
virtual void	computeCandidate (const Scalar steplength=Scalar(1.))
	Compute new candidate solution using step length.
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.
virtual void	computeExpectedMeritFunctionImprovement ()
	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 ()
	Compute the merit function improvement for the current step.
Scalar	computeStateFeasibility (const std::vector< VectorXs > &xs)
	Compute the state box-constraint feasibility from a given state trajectory.
virtual void	decreaseRegularization ()
	Decrease the state and control regularization values by a regfactor_ factor.
virtual bool	decreaseRegularizationCriteria ()
	Criteria used to decrease regularization.
	DEPRECATED ("Use get_preg for primal-variable regularization", Scalar get_xreg() const { return preg_;}) DEPRECATED("Use get_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.
virtual void	increaseRegularization ()
	Increase the state and control regularization values by a regfactor_ factor.
virtual bool	increaseRegularizationCriteria ()
	Criteria used to increase regularization.
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 void	updateCandidate ()
	Update the candidate solution: cost, feasibilities, and merit value.
virtual void	updateMeritFunction ()
	Update the merit function value for the current guess.

Public Attributes
EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef Ipopt::Number	Scalar
Public Attributes inherited from SolverAbstractTpl< Ipopt::Number >
EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef Ipopt::Number	Scalar

Additional Inherited Members
Protected Member Functions inherited from SolverAbstractTpl< Ipopt::Number >
void	allocateData ()
	Allocate all the basic data needed in solvers.
virtual void	resizeRunningData ()
	Resize data associated with the running models of the shooting problem.
virtual void	resizeTerminalData ()
	Resize data associated with the terminal model of the shooting problem.
Protected Attributes inherited from SolverAbstractTpl< Ipopt::Number >
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

Ipopt solver.

This solver solves the optimal control problem by transcribing with the multiple shooting approach.

See also: solve()

Definition at line 30 of file ipopt.hpp.

Member Typedef Documentation

◆ SolverAbstract

typedef SolverAbstractTpl<Scalar> SolverAbstract

Definition at line 36 of file ipopt.hpp.

◆ ShootingProblem

typedef ShootingProblemTpl<Scalar> ShootingProblem

Definition at line 37 of file ipopt.hpp.

◆ IpoptInterface

typedef IpoptInterfaceTpl<Scalar> IpoptInterface

Definition at line 38 of file ipopt.hpp.

◆ MathBase

typedef MathBaseTpl<Scalar> MathBase

Definition at line 39 of file ipopt.hpp.

◆ VectorXs

typedef MathBase::VectorXs VectorXs

Definition at line 40 of file ipopt.hpp.

◆ Vector3s

typedef MathBase::Vector3s Vector3s

Definition at line 41 of file ipopt.hpp.

Constructor & Destructor Documentation

◆ SolverIpopt()

SolverIpopt ( std::shared_ptr< ShootingProblem > problem )

Initialize the Ipopt solver.

Parameters

[in] problem solver to be diagnostic

Member Function Documentation

◆ resizeData()

virtual void resizeData ( )

overridevirtual

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< Ipopt::Number >.

◆ setStringIpoptOption()

void setStringIpoptOption	(	const std::string &	tag,
		const std::string &	value )

Set a string ipopt option.

Parameters

[in]	tag	name of the parameter
[in]	value	string value for the parameter

◆ setNumericIpoptOption()

void setNumericIpoptOption	(	const std::string &	tag,
		Ipopt::Number	value )

Set a string ipopt option.

Parameters

[in]	tag	name of the parameter
[in]	value	numeric value for the parameter

◆ setCandidate()

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 $(\mathbf{x}_s,\mathbf{u}_s)$ .

The solver candidates are defined as a state and control trajectories $(\mathbf{x}_s,\mathbf{u}_s)$ of and elements, respectively. Additionally, we need to define the dynamic feasibility of the $(\mathbf{x}_s,\mathbf{u}_s)$ pair. Note that the trajectories are feasible if $\mathbf{x}_s$ is the resulting trajectory from the system rollout with $\mathbf{u}_s$ inputs. Updating the candidate invalidates any previously computed linearization; the next call to computeDirection() will therefore refresh the derivatives on demand.

Parameters

[in]	xs	state trajectory of elements (default [])
[in]	us	control trajectory of elements (default [])
[in]	isFeasible	true if the `xs` are obtained from integrating the `us` (rollout)

Member Data Documentation

◆ Scalar

EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef Ipopt::Number Scalar

Definition at line 35 of file ipopt.hpp.

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

/build/source/include/crocoddyl/core/solvers/ipopt.hpp

Public Types

Public Member Functions

Public Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ SolverAbstract

◆ ShootingProblem

◆ IpoptInterface

◆ MathBase

◆ VectorXs

◆ Vector3s

Constructor & Destructor Documentation

◆ SolverIpopt()

Member Function Documentation

◆ resizeData()

◆ setStringIpoptOption()

◆ setNumericIpoptOption()

◆ setCandidate()

Member Data Documentation

◆ Scalar