This class maps the traditional NLP into something that is more useful for Ipopt. More...

#include <IpOrigIpoptNLP.hpp>

Inheritance diagram for Ipopt::OrigIpoptNLP:

Public Member Functions
virtual bool	Initialize (const Journalist &jnlst, const OptionsList &options, const std::string &prefix)
	Initialization method.

virtual bool	InitializeStructures (SmartPtr< Vector > &x, bool init_x, SmartPtr< Vector > &y_c, bool init_y_c, SmartPtr< Vector > &y_d, bool init_y_d, SmartPtr< Vector > &z_L, bool init_z_L, SmartPtr< Vector > &z_U, bool init_z_U, SmartPtr< Vector > &v_L, SmartPtr< Vector > &v_U)
	Initialize (create) structures for the iteration data.

virtual bool	GetWarmStartIterate (IteratesVector &warm_start_iterate)
	Method accessing the GetWarmStartIterate of the NLP.

virtual void	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)
	Accessor method for vector/matrix spaces pointers.

virtual void	AdjustVariableBounds (const Vector &new_x_L, const Vector &new_x_U, const Vector &new_d_L, const Vector &new_d_U)
	Method for adapting the variable bounds.

SmartPtr< NLP >	nlp ()
	Accessor method to the underlying NLP.

Constructors / Destructor
	OrigIpoptNLP (const SmartPtr< const Journalist > &jnlst, const SmartPtr< NLP > &nlp, const SmartPtr< NLPScalingObject > &nlp_scaling, TimingStatistics &timing_statistics)
	Constructor.

virtual	~OrigIpoptNLP ()
	Destructor.


virtual Number	f (const Vector &x)
	Accessor methods for model data.

virtual Number	f (const Vector &x, Number mu)
	Objective value (depending in mu) - incorrect version for OrigIpoptNLP.

virtual SmartPtr< const Vector >	grad_f (const Vector &x)
	Gradient of the objective.

virtual SmartPtr< const Vector >	grad_f (const Vector &x, Number mu)
	Gradient of the objective (depending in mu) - incorrect version for OrigIpoptNLP.

virtual SmartPtr< const Vector >	c (const Vector &x)
	Equality constraint residual.

virtual SmartPtr< const Matrix >	jac_c (const Vector &x)
	Jacobian Matrix for equality constraints.

virtual SmartPtr< const Vector >	d (const Vector &x)
	Inequality constraint residual (reformulated as equalities with slacks)

virtual SmartPtr< const Matrix >	jac_d (const Vector &x)
	Jacobian Matrix for inequality constraints.

virtual SmartPtr< const SymMatrix >	h (const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd)
	Hessian of the Lagrangian.

virtual SmartPtr< const SymMatrix >	h (const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd, Number mu)
	Hessian of the Lagrangian (depending in mu) - incorrect version for OrigIpoptNLP.

virtual SmartPtr< const SymMatrix >	uninitialized_h ()
	Provides a Hessian matrix from the correct matrix space with uninitialized values.

virtual SmartPtr< const Vector >	x_L () const
	Scaled lower bounds on x.

virtual SmartPtr< const Matrix >	Px_L () const
	Permutation matrix (x_L_ -> x)

virtual SmartPtr< const Vector >	orig_x_L () const
	Original unscaled lower bounds on x.

virtual SmartPtr< const Vector >	x_U () const
	Scaled upper bounds on x.

virtual SmartPtr< const Matrix >	Px_U () const
	Permutation matrix (x_U_ -> x)

virtual SmartPtr< const Vector >	orig_x_U () const
	Original unscaled upper bounds on x.

virtual SmartPtr< const Vector >	d_L () const
	Scaled lower bounds on d.

virtual SmartPtr< const Matrix >	Pd_L () const
	Permutation matrix (d_L_ -> d)

virtual SmartPtr< const Vector >	orig_d_L () const
	Original unscaled lower bounds on d.

virtual SmartPtr< const Vector >	d_U () const
	Scaled upper bounds on d.

virtual SmartPtr< const Matrix >	Pd_U () const
	Permutation matrix (d_U_ -> d)

virtual SmartPtr< const Vector >	orig_d_U () const
	Original unscaled upper bounds on d.

virtual SmartPtr< const SymMatrixSpace >	HessianMatrixSpace () const
	Accessor method to obtain the MatrixSpace for the Hessian matrix (or it's approximation)

virtual SmartPtr< const VectorSpace >	x_space () const
	x_space

Counters for the number of function evaluations.
virtual Index	f_evals () const

virtual Index	grad_f_evals () const

virtual Index	c_evals () const

virtual Index	jac_c_evals () const

virtual Index	d_evals () const

virtual Index	jac_d_evals () const

virtual Index	h_evals () const


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)
	Solution Routines - overloaded from IpoptNLP.

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, SmartPtr< const IpoptData > ip_data, SmartPtr< IpoptCalculatedQuantities > ip_cq)

Public Member Functions inherited from Ipopt::IpoptNLP
SmartPtr< NLPScalingObject >	NLP_scaling () const
	Returns the scaling strategy object.

	IpoptNLP (const SmartPtr< NLPScalingObject > nlp_scaling)

virtual	~IpoptNLP ()
	Destructor.

	DECLARE_STD_EXCEPTION (Eval_Error)
	thrown if there is any error evaluating values from the nlp

virtual bool	objective_depends_on_mu () const
	Method for telling the IpoptCalculatedQuantities class whether the objective function depends on the barrier function.

Public Member Functions inherited from Ipopt::ReferencedObject
	ReferencedObject ()

virtual	~ReferencedObject ()

Index	ReferenceCount () const

void	AddRef (const Referencer *referencer) const

void	ReleaseRef (const Referencer *referencer) const

Static Public Member Functions
static void	RegisterOptions (SmartPtr< RegisteredOptions > roptions)
	Called to register the options.

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.
	OrigIpoptNLP ()
	Default Constructor.

	OrigIpoptNLP (const OrigIpoptNLP &)
	Copy Constructor.

void	operator= (const OrigIpoptNLP &)
	Overloaded Assignment Operator.

auxiliary functions
void	relax_bounds (Number bound_relax_factor, Vector &bounds)
	relax the bounds by a relative move of relax_bound_factor.

SmartPtr< const Vector >	get_unscaled_x (const Vector &x)
	Method for getting the unscaled version of the x vector.

Private Attributes
SmartPtr< const Journalist >	jnlst_
	Journalist.

SmartPtr< NLP >	nlp_
	Pointer to the NLP.

bool	initialized_
	Flag indicating if initialization method has been called.


SmartPtr< const VectorSpace >	x_space_
	Necessary Vector/Matrix spaces.

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 >	h_space_

SmartPtr< const MatrixSpace >	scaled_jac_c_space_

SmartPtr< const MatrixSpace >	scaled_jac_d_space_

SmartPtr< const SymMatrixSpace >	scaled_h_space_

Storage for Model Quantities
CachedResults< Number >	f_cache_
	Objective function.

CachedResults< SmartPtr< const Vector > >	grad_f_cache_
	Gradient of the objective function.

CachedResults< SmartPtr< const Vector > >	c_cache_
	Equality constraint residuals.

CachedResults< SmartPtr< const Matrix > >	jac_c_cache_
	Jacobian Matrix for equality constraints (current iteration)

CachedResults< SmartPtr< const Vector > >	d_cache_
	Inequality constraint residual (reformulated as equalities with slacks.

CachedResults< SmartPtr< const Matrix > >	jac_d_cache_
	Jacobian Matrix for inequality constraints (current iteration)

CachedResults< SmartPtr< const SymMatrix > >	h_cache_
	Hessian of the lagrangian (current iteration)

CachedResults< SmartPtr< const Vector > >	unscaled_x_cache_
	Unscaled version of x vector.

SmartPtr< const Vector >	x_L_
	Lower bounds on x.

SmartPtr< const Matrix >	Px_L_
	Permutation matrix (x_L_ -> x)

SmartPtr< const Vector >	x_U_
	Upper bounds on x.

SmartPtr< const Matrix >	Px_U_
	Permutation matrix (x_U_ -> x)

SmartPtr< const Vector >	d_L_
	Lower bounds on d.

SmartPtr< const Matrix >	Pd_L_
	Permutation matrix (d_L_ -> d)

SmartPtr< const Vector >	d_U_
	Upper bounds on d.

SmartPtr< const Matrix >	Pd_U_
	Permutation matrix (d_U_ -> d)

SmartPtr< const Vector >	orig_x_L_
	Original unmodified lower bounds on x.

SmartPtr< const Vector >	orig_x_U_
	Original unmodified upper bounds on x.

SmartPtr< const Vector >	orig_d_L_
	Original unmodified lower bounds on d.

SmartPtr< const Vector >	orig_d_U_
	Original unmodified upper bounds on d.

Algorithmic parameters
Number	bound_relax_factor_
	relaxation factor for the bounds

Number	constr_viol_tol_
	constraint violation tolerance (from OptimalityErrorConvergenceCheck)

bool	honor_original_bounds_
	Flag indicating whether the primal variables should be projected back into original bounds are optimization.

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.

HessianApproximationSpace	hessian_approximation_space_
	Flag indicating in which space Hessian is to be approximated.

bool	check_derivatives_for_naninf_
	Flag indicating whether it is desired to check if there are Nan or Inf entries in first and second derivative matrices.

bool	grad_f_constant_
	Flag indicating if we need to ask for objective Gradient only once.

bool	jac_c_constant_
	Flag indicating if we need to ask for equality constraint Jacobians only once.

bool	jac_d_constant_
	Flag indicating if we need to ask for inequality constraint Jacobians only once.

bool	hessian_constant_
	Flag indicating if we need to ask for Hessian only once.

Counters for the function evaluations
Index	f_evals_

Index	grad_f_evals_

Index	c_evals_

Index	jac_c_evals_

Index	d_evals_

Index	jac_d_evals_

Index	h_evals_

Timing statistics for the function evaluations.
TimingStatistics &	timing_statistics_

Detailed Description

This class maps the traditional NLP into something that is more useful for Ipopt.

This class takes care of storing the calculated model results, handles caching, scaling, and (some day) takes care of addition of slacks.

Given a NLP

\begin{eqnarray*} \mathrm{min} && f(x), \\ \mathrm{s.t.} && c(x) = 0, &\qquad y_c\\ && d_L \leq d(x) \leq d_U, &\qquad y_d \\ && x_L \leq x \leq x_U, &\qquad z_L, z_U \end{eqnarray*}

and (invertible diagonal) scaling matrices \(s_o\), \(s_c\), \(s_d\), \(s_x\), this class represents the NLP

\begin{eqnarray*} \mathrm{min} && s_o f(s_x^{-1} \tilde x), \\ \mathrm{s.t.} && s_c c(s_x^{-1} \tilde x) = 0, &\qquad \tilde y_c \\ && s_d d_L \leq s_d d(s_x^{-1} \tilde x) \leq s_d d_U, &\qquad \tilde y_d \\ && s_x x_L \leq \tilde x \leq s_x x_U, &\qquad \tilde z_L, z_U \end{eqnarray*}

where \(\tilde x\), \(\tilde y_c\), \(\tilde y_d\), \(\tilde z_L\), \(\tilde z_U\), are the primal and dual variables of the scaled problem (though, Ipopt adds slack variables additionally).

The correspondence between a scaled and its corresponding unscaled solution is

\begin{eqnarray*} x && = s_x^{-1} \tilde x \\ y_c && = s_o^{-1} s_c \tilde y_c \\ y_d && = s_o^{-1} s_d \tilde y_d \\ z_L && = s_o^{-1} s_x \tilde z_L \\ z_U && = s_o^{-1} s_x \tilde z_U \end{eqnarray*}

Definition at line 65 of file IpOrigIpoptNLP.hpp.

Constructor & Destructor Documentation

◆ OrigIpoptNLP() [1/3]

Ipopt::OrigIpoptNLP::OrigIpoptNLP	(	const SmartPtr< const Journalist > &	jnlst,
		const SmartPtr< NLP > &	nlp,
		const SmartPtr< NLPScalingObject > &	nlp_scaling,
		TimingStatistics &	timing_statistics
	)

Constructor.

Parameters

jnlst	Journalist
nlp	NLP
nlp_scaling	NLP scaling
timing_statistics	Timing statistics

Since: 3.14.0

◆ ~OrigIpoptNLP()

virtual Ipopt::OrigIpoptNLP::~OrigIpoptNLP ( )

virtual

Destructor.

◆ OrigIpoptNLP() [2/3]

Ipopt::OrigIpoptNLP::OrigIpoptNLP ( )

private

Default Constructor.

◆ OrigIpoptNLP() [3/3]

Ipopt::OrigIpoptNLP::OrigIpoptNLP ( const OrigIpoptNLP & )

private

Copy Constructor.

Member Function Documentation

◆ Initialize()

virtual bool Ipopt::OrigIpoptNLP::Initialize	(	const Journalist &	jnlst,
		const OptionsList &	options,
		const std::string &	prefix
	)

virtual

Initialization method.

Set the internal options and initialize internal data structures.

Reimplemented from Ipopt::IpoptNLP.

◆ InitializeStructures()

virtual bool Ipopt::OrigIpoptNLP::InitializeStructures	(	SmartPtr< Vector > &	x,
		bool	init_x,
		SmartPtr< Vector > &	y_c,
		bool	init_y_c,
		SmartPtr< Vector > &	y_d,
		bool	init_y_d,
		SmartPtr< Vector > &	z_L,
		bool	init_z_L,
		SmartPtr< Vector > &	z_U,
		bool	init_z_U,
		SmartPtr< Vector > &	v_L,
		SmartPtr< Vector > &	v_U
	)

virtual

Initialize (create) structures for the iteration data.

Public Member Functions

Static Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ OrigIpoptNLP() [1/3]

◆ ~OrigIpoptNLP()

◆ OrigIpoptNLP() [2/3]

◆ OrigIpoptNLP() [3/3]

Member Function Documentation

◆ Initialize()

◆ InitializeStructures()

◆ GetWarmStartIterate()

◆ f() [1/2]

◆ f() [2/2]

◆ grad_f() [1/2]

◆ grad_f() [2/2]

◆ c()

◆ jac_c()

◆ d()

◆ jac_d()

◆ h() [1/2]

◆ h() [2/2]

◆ uninitialized_h()

◆ x_L()

◆ Px_L()

◆ orig_x_L()

◆ x_U()

◆ Px_U()

◆ orig_x_U()

◆ d_L()

◆ Pd_L()

◆ orig_d_L()

◆ d_U()

◆ Pd_U()

◆ orig_d_U()

◆ HessianMatrixSpace()

◆ x_space()

◆ GetSpaces()

◆ AdjustVariableBounds()

◆ f_evals()

◆ grad_f_evals()

◆ c_evals()

◆ jac_c_evals()

◆ d_evals()

◆ jac_d_evals()

◆ h_evals()

◆ FinalizeSolution()

◆ IntermediateCallBack()

◆ RegisterOptions()

◆ nlp()

◆ operator=()

◆ relax_bounds()

◆ get_unscaled_x()

Member Data Documentation

◆ jnlst_

◆ nlp_

◆ x_space_

◆ c_space_

◆ d_space_

◆ x_l_space_

◆ px_l_space_

◆ x_u_space_

◆ px_u_space_

◆ d_l_space_

◆ pd_l_space_

◆ d_u_space_

◆ pd_u_space_

◆ jac_c_space_

◆ jac_d_space_

◆ h_space_

◆ scaled_jac_c_space_

◆ scaled_jac_d_space_

◆ scaled_h_space_

◆ f_cache_

◆ grad_f_cache_

◆ c_cache_

◆ jac_c_cache_

◆ d_cache_