NLP Class Reference

#include <IpNLP.hpp>

Inheritance diagram for NLP:

Detailed Description

Brief Class Description.

Detailed Class Description.

Public Member Functions

virtual void GetQuasiNewtonApproximationSpaces (SmartPtr< VectorSpace > &approx_space, SmartPtr< Matrix > &P_approx)

Method for obtaining the subspace in which the limited-memory Hessian approximation should be done.

Constructors/Destructors

NLP ()

Default constructor.

virtual ~NLP ()

Default destructor.

DECLARE_STD_EXCEPTION (USER_SCALING_NOT_IMPLEMENTED)

Exceptions.

NLP Initialization (overload in

derived classes).

virtual bool ProcessOptions (const OptionsList &options, const std::string &prefix)

Overload if you want the chance to process options or parameters that may be specific to the NLP.

virtual bool GetSpaces (SmartPtr< const VectorSpace > &x_space, SmartPtr< const VectorSpace > &c_space, SmartPtr< const VectorSpace > &d_space, SmartPtr< const VectorSpace > &x_l_space, SmartPtr< const MatrixSpace > &px_l_space, SmartPtr< const VectorSpace > &x_u_space, SmartPtr< const MatrixSpace > &px_u_space, SmartPtr< const VectorSpace > &d_l_space, SmartPtr< const MatrixSpace > &pd_l_space, SmartPtr< const VectorSpace > &d_u_space, SmartPtr< const MatrixSpace > &pd_u_space, SmartPtr< const MatrixSpace > &Jac_c_space, SmartPtr< const MatrixSpace > &Jac_d_space, SmartPtr< const SymMatrixSpace > &Hess_lagrangian_space)=0

Method for creating the derived vector / matrix types.

virtual bool GetBoundsInformation (const Matrix &Px_L, Vector &x_L, const Matrix &Px_U, Vector &x_U, const Matrix &Pd_L, Vector &d_L, const Matrix &Pd_U, Vector &d_U)=0

Method for obtaining the bounds information.

virtual bool GetStartingPoint (SmartPtr< Vector > x, bool need_x, SmartPtr< Vector > y_c, bool need_y_c, SmartPtr< Vector > y_d, bool need_y_d, SmartPtr< Vector > z_L, bool need_z_L, SmartPtr< Vector > z_U, bool need_z_U)=0

Method for obtaining the starting point for all the iterates.

virtual bool GetWarmStartIterate (IteratesVector &warm_start_iterate)

Method for obtaining an entire iterate as a warmstart point.

NLP evaluation routines (overload

in derived classes.

virtual bool Eval_f (const Vector &x, Number &f)=0

virtual bool Eval_grad_f (const Vector &x, Vector &g_f)=0

virtual bool Eval_c (const Vector &x, Vector &c)=0

virtual bool Eval_jac_c (const Vector &x, Matrix &jac_c)=0

virtual bool Eval_d (const Vector &x, Vector &d)=0

virtual bool Eval_jac_d (const Vector &x, Matrix &jac_d)=0

virtual bool Eval_h (const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd, SymMatrix &h)=0

NLP solution routines. Have default dummy

implementations that can be overloaded.

virtual void FinalizeSolution (SolverReturn status, const Vector &x, const Vector &z_L, const Vector &z_U, const Vector &c, const Vector &d, const Vector &y_c, const Vector &y_d, Number obj_value)

This method is called at the very end of the optimization.

virtual bool IntermediateCallBack (AlgorithmMode mode, Index iter, Number obj_value, Number inf_pr, Number inf_du, Number mu, Number d_norm, Number regularization_size, Number alpha_du, Number alpha_pr, Index ls_trials, const IpoptData *ip_data, IpoptCalculatedQuantities *ip_cq)

This method is called once per iteration, after the iteration summary output has been printed.

virtual void GetScalingParameters (const SmartPtr< const VectorSpace > x_space, const SmartPtr< const VectorSpace > c_space, const SmartPtr< const VectorSpace > d_space, Number &obj_scaling, SmartPtr< Vector > &x_scaling, SmartPtr< Vector > &c_scaling, SmartPtr< Vector > &d_scaling) const

Routines to get the scaling parameters.

Constructor & Destructor Documentation

NLP ( ) [inline]

Default constructor.

virtual ~NLP ( ) [inline, virtual]

Default destructor.

Member Function Documentation

DECLARE_STD_EXCEPTION ( USER_SCALING_NOT_IMPLEMENTED )

Exceptions.

virtual bool ProcessOptions	(	const OptionsList &	options,
		const std::string &	prefix
	)			`[inline, virtual]`

Overload if you want the chance to process options or parameters that may be specific to the NLP.

Reimplemented in TNLPAdapter.

virtual bool GetSpaces	(	SmartPtr< const VectorSpace > &	x_space,
		SmartPtr< const VectorSpace > &	c_space,
		SmartPtr< const VectorSpace > &	d_space,
		SmartPtr< const VectorSpace > &	x_l_space,
		SmartPtr< const MatrixSpace > &	px_l_space,
		SmartPtr< const VectorSpace > &	x_u_space,
		SmartPtr< const MatrixSpace > &	px_u_space,
		SmartPtr< const VectorSpace > &	d_l_space,
		SmartPtr< const MatrixSpace > &	pd_l_space,
		SmartPtr< const VectorSpace > &	d_u_space,
		SmartPtr< const MatrixSpace > &	pd_u_space,
		SmartPtr< const MatrixSpace > &	Jac_c_space,
		SmartPtr< const MatrixSpace > &	Jac_d_space,
		SmartPtr< const SymMatrixSpace > &	Hess_lagrangian_space
	)			`[pure virtual]`

Method for creating the derived vector / matrix types.

The Hess_lagrangian_space pointer can be NULL if a quasi-Newton options is chosen.

Implemented in TNLPAdapter.

virtual bool GetBoundsInformation	(	const Matrix &	Px_L,
		Vector &	x_L,
		const Matrix &	Px_U,
		Vector &	x_U,
		const Matrix &	Pd_L,
		Vector &	d_L,
		const Matrix &	Pd_U,
		Vector &	d_U
	)			`[pure virtual]`

Method for obtaining the bounds information.

Implemented in TNLPAdapter.

virtual bool GetStartingPoint	(	SmartPtr< Vector >	x,
		bool	need_x,
		SmartPtr< Vector >	y_c,
		bool	need_y_c,
		SmartPtr< Vector >	y_d,
		bool	need_y_d,
		SmartPtr< Vector >	z_L,
		bool	need_z_L,
		SmartPtr< Vector >	z_U,
		bool	need_z_U
	)			`[pure virtual]`

Method for obtaining the starting point for all the iterates.

ToDo it might not make sense to ask for initial values for v_L and v_U?

Implemented in TNLPAdapter.

virtual bool GetWarmStartIterate ( IteratesVector & warm_start_iterate ) [inline, virtual]

Method for obtaining an entire iterate as a warmstart point.

The incoming IteratesVector has to be filled. The default dummy implementation returns false.

Reimplemented in TNLPAdapter.

virtual bool Eval_f	(	const Vector &	x,
		Number &	f
	)			`[pure virtual]`

Implemented in TNLPAdapter.

virtual bool Eval_grad_f	(	const Vector &	x,
		Vector &	g_f
	)			`[pure virtual]`

Implemented in TNLPAdapter.

virtual bool Eval_c	(	const Vector &	x,
		Vector &	c
	)			`[pure virtual]`

Implemented in TNLPAdapter.

virtual bool Eval_jac_c	(	const Vector &	x,
		Matrix &	jac_c
	)			`[pure virtual]`

Implemented in TNLPAdapter.

virtual bool Eval_d	(	const Vector &	x,
		Vector &	d
	)			`[pure virtual]`

Implemented in TNLPAdapter.

virtual bool Eval_jac_d	(	const Vector &	x,
		Matrix &	jac_d
	)			`[pure virtual]`

Implemented in TNLPAdapter.

virtual bool Eval_h	(	const Vector &	x,
		Number	obj_factor,
		const Vector &	yc,
		const Vector &	yd,
		SymMatrix &	h
	)			`[pure virtual]`

Implemented in TNLPAdapter.

virtual void FinalizeSolution	(	SolverReturn	status,
		const Vector &	x,
		const Vector &	z_L,
		const Vector &	z_U,
		const Vector &	c,
		const Vector &	d,
		const Vector &	y_c,
		const Vector &	y_d,
		Number	obj_value
	)			`[inline, virtual]`

This method is called at the very end of the optimization.

It provides the final iterate to the user, so that it can be stored as the solution. The status flag indicates the outcome of the optimization, where SolverReturn is defined in IpAlgTypes.hpp.

Reimplemented in TNLPAdapter.

virtual bool IntermediateCallBack	(	AlgorithmMode	mode,
		Index	iter,
		Number	obj_value,
		Number	inf_pr,
		Number	inf_du,
		Number	mu,
		Number	d_norm,
		Number	regularization_size,
		Number	alpha_du,
		Number	alpha_pr,
		Index	ls_trials,
		const IpoptData *	ip_data,
		IpoptCalculatedQuantities *	ip_cq
	)			`[inline, virtual]`

This method is called once per iteration, after the iteration summary output has been printed.

It provides the current information to the user to do with it anything she wants. It also allows the user to ask for a premature termination of the optimization by returning false, in which case Ipopt will terminate with a corresponding return status. The basic information provided in the argument list has the quantities values printed in the iteration summary line. If more information is required, a user can obtain it from the IpData and IpCalculatedQuantities objects. However, note that the provided quantities are all for the problem that Ipopt sees, i.e., the quantities might be scaled, fixed variables might be sorted out, etc. The status indicates things like whether the algorithm is in the restoration phase... In the restoration phase, the dual variables are probably not not changing.

Reimplemented in TNLPAdapter.

virtual void GetScalingParameters	(	const SmartPtr< const VectorSpace >	x_space,
		const SmartPtr< const VectorSpace >	c_space,
		const SmartPtr< const VectorSpace >	d_space,
		Number &	obj_scaling,
		SmartPtr< Vector > &	x_scaling,
		SmartPtr< Vector > &	c_scaling,
		SmartPtr< Vector > &	d_scaling
	)			const `[inline, virtual]`

Routines to get the scaling parameters.

These do not need to be overloaded unless the options are set for User scaling

Reimplemented in TNLPAdapter.

References THROW_EXCEPTION.

virtual void GetQuasiNewtonApproximationSpaces	(	SmartPtr< VectorSpace > &	approx_space,
		SmartPtr< Matrix > &	P_approx
	)			`[inline, virtual]`

Method for obtaining the subspace in which the limited-memory Hessian approximation should be done.

This is only called if the limited-memory Hessian approximation is chosen. Since the Hessian is zero in the space of all variables that appear in the problem functions only linearly, this allows the user to provide a VectorSpace for all nonlinear variables, and an ExpansionMatrix to lift from this VectorSpace to the VectorSpace of the primal variables x. If the returned values are NULL, it is assumed that the Hessian is to be approximated in the space of all x variables. The default instantiation of this method returns NULL, and a user only has to overwrite this method if the approximation is to be done only in a subspace.

Reimplemented in TNLPAdapter.

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

IpNLP.hpp


Public Member Functions
virtual void	GetQuasiNewtonApproximationSpaces (SmartPtr< VectorSpace > &approx_space, SmartPtr< Matrix > &P_approx)
	Method for obtaining the subspace in which the limited-memory Hessian approximation should be done.
Constructors/Destructors
	NLP ()
	Default constructor.
virtual	~NLP ()
	Default destructor.

	DECLARE_STD_EXCEPTION (USER_SCALING_NOT_IMPLEMENTED)
	Exceptions.
NLP Initialization (overload in
derived classes).
virtual bool	ProcessOptions (const OptionsList &options, const std::string &prefix)
	Overload if you want the chance to process options or parameters that may be specific to the NLP.
virtual bool	GetSpaces (SmartPtr< const VectorSpace > &x_space, SmartPtr< const VectorSpace > &c_space, SmartPtr< const VectorSpace > &d_space, SmartPtr< const VectorSpace > &x_l_space, SmartPtr< const MatrixSpace > &px_l_space, SmartPtr< const VectorSpace > &x_u_space, SmartPtr< const MatrixSpace > &px_u_space, SmartPtr< const VectorSpace > &d_l_space, SmartPtr< const MatrixSpace > &pd_l_space, SmartPtr< const VectorSpace > &d_u_space, SmartPtr< const MatrixSpace > &pd_u_space, SmartPtr< const MatrixSpace > &Jac_c_space, SmartPtr< const MatrixSpace > &Jac_d_space, SmartPtr< const SymMatrixSpace > &Hess_lagrangian_space)=0
	Method for creating the derived vector / matrix types.
virtual bool	GetBoundsInformation (const Matrix &Px_L, Vector &x_L, const Matrix &Px_U, Vector &x_U, const Matrix &Pd_L, Vector &d_L, const Matrix &Pd_U, Vector &d_U)=0
	Method for obtaining the bounds information.
virtual bool	GetStartingPoint (SmartPtr< Vector > x, bool need_x, SmartPtr< Vector > y_c, bool need_y_c, SmartPtr< Vector > y_d, bool need_y_d, SmartPtr< Vector > z_L, bool need_z_L, SmartPtr< Vector > z_U, bool need_z_U)=0
	Method for obtaining the starting point for all the iterates.
virtual bool	GetWarmStartIterate (IteratesVector &warm_start_iterate)
	Method for obtaining an entire iterate as a warmstart point.
NLP evaluation routines (overload
in derived classes.
virtual bool	Eval_f (const Vector &x, Number &f)=0
virtual bool	Eval_grad_f (const Vector &x, Vector &g_f)=0
virtual bool	Eval_c (const Vector &x, Vector &c)=0
virtual bool	Eval_jac_c (const Vector &x, Matrix &jac_c)=0
virtual bool	Eval_d (const Vector &x, Vector &d)=0
virtual bool	Eval_jac_d (const Vector &x, Matrix &jac_d)=0
virtual bool	Eval_h (const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd, SymMatrix &h)=0
NLP solution routines. Have default dummy
implementations that can be overloaded.
virtual void	FinalizeSolution (SolverReturn status, const Vector &x, const Vector &z_L, const Vector &z_U, const Vector &c, const Vector &d, const Vector &y_c, const Vector &y_d, Number obj_value)
	This method is called at the very end of the optimization.
virtual bool	IntermediateCallBack (AlgorithmMode mode, Index iter, Number obj_value, Number inf_pr, Number inf_du, Number mu, Number d_norm, Number regularization_size, Number alpha_du, Number alpha_pr, Index ls_trials, const IpoptData ip_data, IpoptCalculatedQuantities ip_cq)
	This method is called once per iteration, after the iteration summary output has been printed.

virtual void	GetScalingParameters (const SmartPtr< const VectorSpace > x_space, const SmartPtr< const VectorSpace > c_space, const SmartPtr< const VectorSpace > d_space, Number &obj_scaling, SmartPtr< Vector > &x_scaling, SmartPtr< Vector > &c_scaling, SmartPtr< Vector > &d_scaling) const
	Routines to get the scaling parameters.