Ipopt::TNLPAdapter Class Reference

This class Adapts the TNLP interface so it looks like an NLP interface. More...

#include <IpTNLPAdapter.hpp>

Inheritance diagram for Ipopt::TNLPAdapter:

[legend]Collaboration diagram for Ipopt::TNLPAdapter:


Method implementing the detection of linearly dependent
equality constraints
bool	DetermineDependentConstraints (Index n_x_var, const Index x_not_fixed_map, const Number x_l, const Number x_u, const Number g_l, const Number g_u, Index n_c, const Index c_map, std::list< Index > &c_deps)
SmartPtr< TNLP >	tnlp_
	Pointer to the TNLP class (class specific to Number* vectors and harwell triplet matrices).
SmartPtr< const Journalist >	jnlst_
	Journalist.
SmartPtr< TDependencyDetector >	dependency_detector_
	Object that can be used to detect linearly dependent rows in the equality constraint Jacobian.
Public Types
	MAKE_PARAMETER = 0
	MAKE_CONSTRAINT
	RELAX_BOUNDS
	NO_TEST = 0
	FIRST_ORDER_TEST
	SECOND_ORDER_TEST
	JAC_EXACT = 0
	JAC_FINDIFF_VALUES
enum	FixedVariableTreatmentEnum { MAKE_PARAMETER = 0, MAKE_CONSTRAINT, RELAX_BOUNDS }
	Enum for treatment of fixed variables option. More...
enum	DerivativeTestEnum { NO_TEST = 0, FIRST_ORDER_TEST, SECOND_ORDER_TEST }
	Enum for specifying which derivative test is to be performed. More...
enum	JacobianApproxEnum { JAC_EXACT = 0, JAC_FINDIFF_VALUES }
	Enum for specifying technique for computing Jacobian. More...
Public Member Functions
virtual void	GetQuasiNewtonApproximationSpaces (SmartPtr< VectorSpace > &approx_space, SmartPtr< Matrix > &P_approx)
	Method returning information on quasi-Newton approximation.
bool	CheckDerivatives (DerivativeTestEnum deriv_test)
	Method for performing the derivative test.
SmartPtr< TNLP >	tnlp () const
	Accessor method for the underlying TNLP.
Constructors/Destructors
	TNLPAdapter (const SmartPtr< TNLP > tnlp, const SmartPtr< const Journalist > jnlst=NULL)
	Default constructor.
virtual	~TNLPAdapter ()
	Default destructor.
Exceptions
	DECLARE_STD_EXCEPTION (INVALID_TNLP)
	DECLARE_STD_EXCEPTION (ERROR_IN_TNLP_DERIVATIVE_TEST)
TNLPAdapter Initialization.
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)
	Method for creating the derived vector / matrix types (Do not delete these, the ).
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)
	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)
	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.
TNLPAdapter evaluation routines.
virtual bool	Eval_f (const Vector &x, Number &f)
virtual bool	Eval_grad_f (const Vector &x, Vector &g_f)
virtual bool	Eval_c (const Vector &x, Vector &c)
virtual bool	Eval_jac_c (const Vector &x, Matrix &jac_c)
virtual bool	Eval_d (const Vector &x, Vector &d)
virtual bool	Eval_jac_d (const Vector &x, Matrix &jac_d)
virtual bool	Eval_h (const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd, SymMatrix &h)
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.
Solution Reporting Methods
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, const IpoptData ip_data, IpoptCalculatedQuantities ip_cq)
	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.
Methods for translating data for IpoptNLP into the TNLP
data. These methods are used to obtain the current (or final) data for the TNLP formulation from the IpoptNLP structure.
void	ResortX (const Vector &x, Number *x_orig)
	Sort the primal variables, and add the fixed values in x.
void	ResortG (const Vector &c, const Vector &d, Number *g_orig)
	Sort the primal variables, and add the fixed values in x.
void	ResortBnds (const Vector &x_L, Number x_L_orig, const Vector &x_U, Number x_U_orig)
	Sort the primal variables, and add the fixed values in x.
Static Public Member Functions
Methods for IpoptType
static void	RegisterOptions (SmartPtr< RegisteredOptions > roptions)
Private Member Functions
Default Compiler Generated Methods
(Hidden to avoid implicit creation/calling). These methods are not implemented and we do not want the compiler to implement them for us, so we declare them private and do not define them. This ensures that they will not be implicitly created/called.
	TNLPAdapter (const TNLPAdapter &)
	Copy Constructor.
void	operator= (const TNLPAdapter &)
	Overloaded Equals Operator.
Methods to update the values in the local copies of vectors
bool	update_local_x (const Vector &x)
bool	update_local_lambda (const Vector &y_c, const Vector &y_d)
Internal routines for evaluating g and jac_g (values stored since
they are used in both c and d routines
bool	internal_eval_g (bool new_x)
bool	internal_eval_jac_g (bool new_x)
Internal methods for dealing with finite difference
approxation
void	initialize_findiff_jac (const Index iRow, const Index jCol)
	Initialize sparsity structure for finite difference Jacobian.
Private Attributes
TNLP::IndexStyleEnum	index_style_
	Numbering style of variables and constraints.
Algorithmic parameters
Number	nlp_lower_bound_inf_
	Value for a lower bound that denotes -infinity.
Number	nlp_upper_bound_inf_
	Value for a upper bound that denotes infinity.
FixedVariableTreatmentEnum	fixed_variable_treatment_
	Flag indicating how fixed variables should be handled.
Number	bound_relax_factor_
	Value for a lower bound that denotes -infinity.
DerivativeTestEnum	derivative_test_
	Enum indicating whether and which derivative test should be performed at starting point.
Number	derivative_test_perturbation_
	Size of the perturbation for the derivative test.
Number	derivative_test_tol_
	Relative threshold for marking deviation from finite difference test.
bool	derivative_test_print_all_
	Flag indicating if all test values should be printed, or only those violating the threshold.
bool	warm_start_same_structure_
	Flag indicating whether the TNLP with identical structure has already been solved before.
HessianApproximationType	hessian_approximation_
	Flag indicating what Hessian information is to be used.
Index	num_linear_variables_
	Number of linear variables.
JacobianApproxEnum	jacobian_approximation_
	Flag indicating how Jacobian is computed.
Number	findiff_perturbation_
	Size of the perturbation for the derivative approximation.
Number	point_perturbation_radius_
	Maximal perturbation of the initial point.
bool	dependency_detection_with_rhs_
	Flag indicating if rhs should be considered during dependency detection.
Number	tol_
	Overall convergence tolerance.
Problem Size Data
Index	n_full_x_
	full dimension of x (fixed + non-fixed)
Index	n_full_g_
	full dimension of g (c + d)
Index	nz_jac_c_
	non-zeros of the jacobian of c
Index	nz_jac_c_no_extra_
	non-zeros of the jacobian of c without added constraints for fixed variables.
Index	nz_jac_d_
	non-zeros of the jacobian of d
Index	nz_full_jac_g_
	number of non-zeros in full-size Jacobian of g
Index	nz_full_h_
	number of non-zeros in full-size Hessian
Index	nz_h_
	number of non-zeros in the non-fixed-size Hessian
Index	n_x_fixed_
	Number of fixed variables.
Local copy of spaces (for warm start)
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_
Local Copy of the Data
Number *	full_x_
Number *	full_lambda_
	copy of the full x vector (fixed & non-fixed)
Number *	full_g_
	copy of lambda (yc & yd)
Number *	jac_g_
	copy of g (c & d)
Number *	c_rhs_
	the values for the full jacobian of g
Tags for deciding when to update internal copies of vectors
TaggedObject::Tag	x_tag_for_iterates_
TaggedObject::Tag	y_c_tag_for_iterates_
TaggedObject::Tag	y_d_tag_for_iterates_
TaggedObject::Tag	x_tag_for_g_
TaggedObject::Tag	x_tag_for_jac_g_
Internal Permutation Spaces and matrices
SmartPtr< ExpansionMatrix >	P_x_full_x_
	Expansion from fixed x (ipopt) to full x.
SmartPtr< ExpansionMatrixSpace >	P_x_full_x_space_
	Expansion from fixed x (ipopt) to full x.
SmartPtr< ExpansionMatrix >	P_x_x_L_
	Expansion from fixed x_L (ipopt) to full x.
SmartPtr< ExpansionMatrixSpace >	P_x_x_L_space_
	Expansion from fixed x (ipopt) to full x.
SmartPtr< ExpansionMatrix >	P_x_x_U_
	Expansion from fixed x_U (ipopt) to full x.
SmartPtr< ExpansionMatrixSpace >	P_x_x_U_space_
	Expansion from fixed x (ipopt) to full x.
SmartPtr< ExpansionMatrixSpace >	P_c_g_space_
	Expansion from c only (ipopt) to full ampl c.
SmartPtr< ExpansionMatrix >	P_c_g_
	Expansion from fixed x (ipopt) to full x.
SmartPtr< ExpansionMatrixSpace >	P_d_g_space_
	Expansion from d only (ipopt) to full ampl d.
SmartPtr< ExpansionMatrix >	P_d_g_
	Expansion from fixed x (ipopt) to full x.
Index *	jac_idx_map_
	Expansion from fixed x (ipopt) to full x.
Index *	h_idx_map_
	Expansion from fixed x (ipopt) to full x.
Index *	x_fixed_map_
	Position of fixed variables.
Data for finite difference approximations of derivatives
Index	findiff_jac_nnz_
	Number of unique nonzeros in constraint Jacobian.
Index *	findiff_jac_ia_
	Start position for nonzero indices in ja for each column of Jacobian.
Index *	findiff_jac_ja_
	Ordered by columns, for each column the row indices in Jacobian.
Index *	findiff_jac_postriplet_
	Position of entry in original triplet matrix.
Number *	findiff_x_l_
	Copy of the lower bounds.
Number *	findiff_x_u_
	Copy of the upper bounds.

Detailed Description

This class Adapts the TNLP interface so it looks like an NLP interface.

This is an Adapter class (Design Patterns) that converts a TNLP to an NLP. This allows users to write to the "more convenient" TNLP interface.

Definition at line 30 of file IpTNLPAdapter.hpp.

Member Enumeration Documentation

enum Ipopt::TNLPAdapter::FixedVariableTreatmentEnum

Enum for treatment of fixed variables option.

Enumerator:

MAKE_PARAMETER
MAKE_CONSTRAINT
RELAX_BOUNDS

Definition at line 159 of file IpTNLPAdapter.hpp.

enum Ipopt::TNLPAdapter::DerivativeTestEnum

Enum for specifying which derivative test is to be performed.

Enumerator:

NO_TEST
FIRST_ORDER_TEST
SECOND_ORDER_TEST

Definition at line 167 of file IpTNLPAdapter.hpp.

enum Ipopt::TNLPAdapter::JacobianApproxEnum

Enum for specifying technique for computing Jacobian.

Enumerator:

JAC_EXACT
JAC_FINDIFF_VALUES

Definition at line 175 of file IpTNLPAdapter.hpp.

Constructor & Destructor Documentation

Ipopt::TNLPAdapter::TNLPAdapter	(	const SmartPtr< TNLP >	tnlp,
		const SmartPtr< const Journalist >	jnlst = `NULL`
	)

Default constructor.

virtual Ipopt::TNLPAdapter::~TNLPAdapter ( ) [virtual]

Default destructor.

Ipopt::TNLPAdapter::TNLPAdapter ( const TNLPAdapter & ) [private]

Copy Constructor.

Member Function Documentation

Ipopt::TNLPAdapter::DECLARE_STD_EXCEPTION ( INVALID_TNLP )

Ipopt::TNLPAdapter::DECLARE_STD_EXCEPTION ( ERROR_IN_TNLP_DERIVATIVE_TEST )

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

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

Reimplemented from Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::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
	)			`[virtual]`

Method for creating the derived vector / matrix types (Do not delete these, the ).

Implements Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::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
	)			`[virtual]`

Method for obtaining the bounds information.

Implements Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::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
	)			`[virtual]`

Method for obtaining the starting point for all the iterates.

Implements Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::GetWarmStartIterate ( IteratesVector & warm_start_iterate ) [virtual]

Method for obtaining an entire iterate as a warmstart point.

The incoming IteratesVector has to be filled.

Reimplemented from Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::Eval_f	(	const Vector &	x,
		Number &	f
	)			`[virtual]`

Implements Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::Eval_grad_f	(	const Vector &	x,
		Vector &	g_f
	)			`[virtual]`

Implements Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::Eval_c	(	const Vector &	x,
		Vector &	c
	)			`[virtual]`

Implements Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::Eval_jac_c	(	const Vector &	x,
		Matrix &	jac_c
	)			`[virtual]`

Implements Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::Eval_d	(	const Vector &	x,
		Vector &	d
	)			`[virtual]`

Implements Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::Eval_jac_d	(	const Vector &	x,
		Matrix &	jac_d
	)			`[virtual]`

Implements Ipopt::NLP.

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

Implements Ipopt::NLP.

virtual void Ipopt::TNLPAdapter::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 `[virtual]`

Routines to get the scaling parameters.

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

Reimplemented from Ipopt::NLP.

virtual void Ipopt::TNLPAdapter::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,
		const IpoptData *	ip_data,
		IpoptCalculatedQuantities *	ip_cq
	)			`[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 from Ipopt::NLP.

virtual bool Ipopt::TNLPAdapter::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
	)			`[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 from Ipopt::NLP.

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

Method returning information on quasi-Newton approximation.

Reimplemented from Ipopt::NLP.

bool Ipopt::TNLPAdapter::CheckDerivatives ( DerivativeTestEnum deriv_test )

Method for performing the derivative test.

static void Ipopt::TNLPAdapter::RegisterOptions ( SmartPtr< RegisteredOptions > roptions ) [static]

SmartPtr<TNLP> Ipopt::TNLPAdapter::tnlp ( ) const [inline]

Accessor method for the underlying TNLP.

Definition at line 190 of file IpTNLPAdapter.hpp.

References tnlp_.

void Ipopt::TNLPAdapter::ResortX	(	const Vector &	x,
		Number *	x_orig
	)

Sort the primal variables, and add the fixed values in x.

void Ipopt::TNLPAdapter::ResortG	(	const Vector &	c,
		const Vector &	d,
		Number *	g_orig
	)

Sort the primal variables, and add the fixed values in x.

void Ipopt::TNLPAdapter::ResortBnds	(	const Vector &	x_L,
		Number *	x_L_orig,
		const Vector &	x_U,
		Number *	x_U_orig
	)

Sort the primal variables, and add the fixed values in x.

void Ipopt::TNLPAdapter::operator= ( const TNLPAdapter & ) [private]

Overloaded Equals Operator.

bool Ipopt::TNLPAdapter::DetermineDependentConstraints	(	Index	n_x_var,
		const Index *	x_not_fixed_map,
		const Number *	x_l,
		const Number *	x_u,
		const Number *	g_l,
		const Number *	g_u,
		Index	n_c,
		const Index *	c_map,
		std::list< Index > &	c_deps
	)			`[private]`

bool Ipopt::TNLPAdapter::update_local_x ( const Vector & x ) [private]

bool Ipopt::TNLPAdapter::update_local_lambda	(	const Vector &	y_c,
		const Vector &	y_d
	)			`[private]`

bool Ipopt::TNLPAdapter::internal_eval_g ( bool new_x ) [private]

bool Ipopt::TNLPAdapter::internal_eval_jac_g ( bool new_x ) [private]

void Ipopt::TNLPAdapter::initialize_findiff_jac	(	const Index *	iRow,
		const Index *	jCol
	)			`[private]`

Initialize sparsity structure for finite difference Jacobian.

Member Data Documentation

SmartPtr<TNLP> Ipopt::TNLPAdapter::tnlp_ [private]

Pointer to the TNLP class (class specific to Number* vectors and harwell triplet matrices).

Definition at line 234 of file IpTNLPAdapter.hpp.

Referenced by tnlp().

SmartPtr<const Journalist> Ipopt::TNLPAdapter::jnlst_ [private]

Journalist.

Definition at line 237 of file IpTNLPAdapter.hpp.

SmartPtr<TDependencyDetector> Ipopt::TNLPAdapter::dependency_detector_ [private]

Object that can be used to detect linearly dependent rows in the equality constraint Jacobian.

Definition at line 241 of file IpTNLPAdapter.hpp.

Number Ipopt::TNLPAdapter::nlp_lower_bound_inf_ [private]

Value for a lower bound that denotes -infinity.

Definition at line 246 of file IpTNLPAdapter.hpp.

Number Ipopt::TNLPAdapter::nlp_upper_bound_inf_ [private]

Value for a upper bound that denotes infinity.

Definition at line 248 of file IpTNLPAdapter.hpp.

FixedVariableTreatmentEnum Ipopt::TNLPAdapter::fixed_variable_treatment_ [private]

Flag indicating how fixed variables should be handled.

Definition at line 250 of file IpTNLPAdapter.hpp.

Number Ipopt::TNLPAdapter::bound_relax_factor_ [private]

Value for a lower bound that denotes -infinity.

Definition at line 252 of file IpTNLPAdapter.hpp.

DerivativeTestEnum Ipopt::TNLPAdapter::derivative_test_ [private]

Enum indicating whether and which derivative test should be performed at starting point.

Definition at line 260 of file IpTNLPAdapter.hpp.

Number Ipopt::TNLPAdapter::derivative_test_perturbation_ [private]

Size of the perturbation for the derivative test.

Definition at line 262 of file IpTNLPAdapter.hpp.

Number Ipopt::TNLPAdapter::derivative_test_tol_ [private]

Relative threshold for marking deviation from finite difference test.

Definition at line 265 of file IpTNLPAdapter.hpp.

bool Ipopt::TNLPAdapter::derivative_test_print_all_ [private]

Flag indicating if all test values should be printed, or only those violating the threshold.

Definition at line 268 of file IpTNLPAdapter.hpp.

bool Ipopt::TNLPAdapter::warm_start_same_structure_ [private]

Flag indicating whether the TNLP with identical structure has already been solved before.

Definition at line 271 of file IpTNLPAdapter.hpp.

HessianApproximationType Ipopt::TNLPAdapter::hessian_approximation_ [private]

Flag indicating what Hessian information is to be used.

Definition at line 273 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::num_linear_variables_ [private]

Number of linear variables.

Definition at line 275 of file IpTNLPAdapter.hpp.

JacobianApproxEnum Ipopt::TNLPAdapter::jacobian_approximation_ [private]

Flag indicating how Jacobian is computed.

Definition at line 277 of file IpTNLPAdapter.hpp.

Number Ipopt::TNLPAdapter::findiff_perturbation_ [private]

Size of the perturbation for the derivative approximation.

Definition at line 279 of file IpTNLPAdapter.hpp.

Number Ipopt::TNLPAdapter::point_perturbation_radius_ [private]

Maximal perturbation of the initial point.

Definition at line 281 of file IpTNLPAdapter.hpp.

bool Ipopt::TNLPAdapter::dependency_detection_with_rhs_ [private]

Flag indicating if rhs should be considered during dependency detection.

Definition at line 284 of file IpTNLPAdapter.hpp.

Number Ipopt::TNLPAdapter::tol_ [private]

Overall convergence tolerance.

Definition at line 287 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::n_full_x_ [private]

full dimension of x (fixed + non-fixed)

Definition at line 293 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::n_full_g_ [private]

full dimension of g (c + d)

Definition at line 295 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::nz_jac_c_ [private]

non-zeros of the jacobian of c

Definition at line 297 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::nz_jac_c_no_extra_ [private]

non-zeros of the jacobian of c without added constraints for fixed variables.

Definition at line 300 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::nz_jac_d_ [private]

non-zeros of the jacobian of d

Definition at line 302 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::nz_full_jac_g_ [private]

number of non-zeros in full-size Jacobian of g

Definition at line 304 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::nz_full_h_ [private]

number of non-zeros in full-size Hessian

Definition at line 306 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::nz_h_ [private]

number of non-zeros in the non-fixed-size Hessian

Definition at line 308 of file IpTNLPAdapter.hpp.

Index Ipopt::TNLPAdapter::n_x_fixed_ [private]

Number of fixed variables.

Definition at line 310 of file IpTNLPAdapter.hpp.

TNLP::IndexStyleEnum Ipopt::TNLPAdapter::index_style_ [private]

Numbering style of variables and constraints.

Definition at line 314 of file IpTNLPAdapter.hpp.

SmartPtr<const VectorSpace> Ipopt::TNLPAdapter::x_space_ [private]

Definition at line 318 of file IpTNLPAdapter.hpp.

SmartPtr<const VectorSpace> Ipopt::TNLPAdapter::c_space_ [private]

Definition at line 319 of file IpTNLPAdapter.hpp.

SmartPtr<const VectorSpace> Ipopt::TNLPAdapter::d_space_ [private]

Definition at line 320 of file IpTNLPAdapter.hpp.

SmartPtr<const VectorSpace> Ipopt::TNLPAdapter::x_l_space_ [private]

Definition at line 321 of file IpTNLPAdapter.hpp.

SmartPtr<const MatrixSpace> Ipopt::TNLPAdapter::px_l_space_ [private]

Definition at line 322 of file IpTNLPAdapter.hpp.

SmartPtr<const VectorSpace> Ipopt::TNLPAdapter::x_u_space_ [private]

Definition at line 323 of file IpTNLPAdapter.hpp.

SmartPtr<const MatrixSpace> Ipopt::TNLPAdapter::px_u_space_ [private]

Definition at line 324 of file IpTNLPAdapter.hpp.

SmartPtr<const VectorSpace> Ipopt::TNLPAdapter::d_l_space_ [private]

Definition at line 325 of file IpTNLPAdapter.hpp.

SmartPtr<const MatrixSpace> Ipopt::TNLPAdapter::pd_l_space_ [private]

Definition at line 326 of file IpTNLPAdapter.hpp.

SmartPtr<const VectorSpace> Ipopt::TNLPAdapter::d_u_space_ [private]

Definition at line 327 of file IpTNLPAdapter.hpp.

SmartPtr<const MatrixSpace> Ipopt::TNLPAdapter::pd_u_space_ [private]

Definition at line 328 of file IpTNLPAdapter.hpp.

SmartPtr<const MatrixSpace> Ipopt::TNLPAdapter::Jac_c_space_ [private]

Definition at line 329 of file IpTNLPAdapter.hpp.

SmartPtr<const MatrixSpace> Ipopt::TNLPAdapter::Jac_d_space_ [private]

Definition at line 330 of file IpTNLPAdapter.hpp.

SmartPtr<const SymMatrixSpace> Ipopt::TNLPAdapter::Hess_lagrangian_space_ [private]

Definition at line 331 of file IpTNLPAdapter.hpp.

Number* Ipopt::TNLPAdapter::full_x_ [private]

Definition at line 336 of file IpTNLPAdapter.hpp.

Number* Ipopt::TNLPAdapter::full_lambda_ [private]

copy of the full x vector (fixed & non-fixed)

Definition at line 337 of file IpTNLPAdapter.hpp.

Number* Ipopt::TNLPAdapter::full_g_ [private]

copy of lambda (yc & yd)

Definition at line 338 of file IpTNLPAdapter.hpp.

Number* Ipopt::TNLPAdapter::jac_g_ [private]

copy of g (c & d)

Definition at line 339 of file IpTNLPAdapter.hpp.

Number* Ipopt::TNLPAdapter::c_rhs_ [private]

the values for the full jacobian of g

Definition at line 340 of file IpTNLPAdapter.hpp.

TaggedObject::Tag Ipopt::TNLPAdapter::x_tag_for_iterates_ [private]

Definition at line 345 of file IpTNLPAdapter.hpp.

TaggedObject::Tag Ipopt::TNLPAdapter::y_c_tag_for_iterates_ [private]

Definition at line 346 of file IpTNLPAdapter.hpp.

TaggedObject::Tag Ipopt::TNLPAdapter::y_d_tag_for_iterates_ [private]

Definition at line 347 of file IpTNLPAdapter.hpp.

TaggedObject::Tag Ipopt::TNLPAdapter::x_tag_for_g_ [private]

Definition at line 348 of file IpTNLPAdapter.hpp.

TaggedObject::Tag Ipopt::TNLPAdapter::x_tag_for_jac_g_ [private]

Definition at line 349 of file IpTNLPAdapter.hpp.

SmartPtr<ExpansionMatrix> Ipopt::TNLPAdapter::P_x_full_x_ [private]