This is an adapter class to convert an NLP to a Feasibility Pump NLP by changing the objective function to the (2-norm) distance to a point. More...

#include <BonTNLP2FPNLP.hpp>

Public Member Functions
void	use (Ipopt::SmartPtr< TNLP > tnlp)
virtual bool	get_variables_linearity (Ipopt::Index n, LinearityType *var_types)
virtual bool	get_constraints_linearity (Ipopt::Index m, LinearityType *const_types)
	overload this method to return the constraint linearity.
Constructors/Destructors
	TNLP2FPNLP (const Ipopt::SmartPtr< Ipopt::TNLP > tnlp, double objectiveScalingFactor=100)
	Build using tnlp as source problem.
	TNLP2FPNLP (const Ipopt::SmartPtr< TNLP > tnlp, const Ipopt::SmartPtr< TNLP2FPNLP > other)
	Build using tnlp as source problem and using other for all other parameters.
virtual	~TNLP2FPNLP ()
	Default destructor.
Methods to select the objective function and extra constraints
void	set_use_feasibility_pump_objective (bool use_feasibility_pump_objective)
	Flag to indicate that we want to use the feasibility pump objective.
void	set_use_cutoff_constraint (bool use_cutoff_constraint)
	Flag to indicate that we want to use a cutoff constraint This constraint has the form f(x) <= (1-epsilon) f(x')
void	set_use_local_branching_constraint (bool use_local_branching_constraint)
	Flag to indicate that we want to use a local branching constraint.
Methods to provide the rhs of the extra constraints
void	set_cutoff (Ipopt::Number cutoff)
	Set the cutoff value to use in the cutoff constraint.
void	set_rhs_local_branching_constraint (double rhs_local_branching_constraint)
	Set the rhs of the local branching constraint.
Methods to change the objective function
void	set_dist_to_point_obj (size_t n, const Ipopt::Number vals, const Ipopt::Index inds)
	Set the point to which distance is minimized.
void	setSigma (double sigma)
	Set the value for sigma.
void	setLambda (double lambda)
	Set the value for lambda.
void	setNorm (int norm)
	Set the value for simgma.
methods to gather information about the NLP
virtual bool	get_nlp_info (Ipopt::Index &n, Ipopt::Index &m, Ipopt::Index &nnz_jac_g, Ipopt::Index &nnz_h_lag, Ipopt::TNLP::IndexStyleEnum &index_style)
	get info from nlp_ and add hessian information
virtual bool	get_bounds_info (Ipopt::Index n, Ipopt::Number x_l, Ipopt::Number x_u, Ipopt::Index m, Ipopt::Number g_l, Ipopt::Number g_u)
	This call is just passed onto tnlp_.
virtual bool	get_starting_point (Ipopt::Index n, bool init_x, Ipopt::Number x, bool init_z, Ipopt::Number z_L, Ipopt::Number z_U, Ipopt::Index m, bool init_lambda, Ipopt::Number lambda)
	Passed onto tnlp_.
virtual bool	eval_f (Ipopt::Index n, const Ipopt::Number *x, bool new_x, Ipopt::Number &obj_value)
	overloaded to return the value of the objective function
virtual bool	eval_grad_f (Ipopt::Index n, const Ipopt::Number x, bool new_x, Ipopt::Number grad_f)
	overload this method to return the vector of the gradient of the objective w.r.t.
virtual bool	eval_g (Ipopt::Index n, const Ipopt::Number x, bool new_x, Ipopt::Index m, Ipopt::Number g)
	overload to return the values of the left-hand side of the constraints
virtual bool	eval_jac_g (Ipopt::Index n, const Ipopt::Number x, bool new_x, Ipopt::Index m, Ipopt::Index nele_jac, Ipopt::Index iRow, Ipopt::Index jCol, Ipopt::Number values)
	overload to return the jacobian of g
virtual bool	eval_h (Ipopt::Index n, const Ipopt::Number x, bool new_x, Ipopt::Number obj_factor, Ipopt::Index m, const Ipopt::Number lambda, bool new_lambda, Ipopt::Index nele_hess, Ipopt::Index iRow, Ipopt::Index jCol, Ipopt::Number *values)
	Evaluate the modified Hessian of the Lagrangian.
Solution Methods
virtual void	finalize_solution (Ipopt::SolverReturn status, Ipopt::Index n, const Ipopt::Number x, const Ipopt::Number z_L, const Ipopt::Number z_U, Ipopt::Index m, const Ipopt::Number g, const Ipopt::Number lambda, Ipopt::Number obj_value, const Ipopt::IpoptData ip_data, Ipopt::IpoptCalculatedQuantities *ip_cq)
	This method is called when the algorithm is complete so the TNLP can store/write the solution.
Scaling of the objective function
void	setObjectiveScaling (double value)
double	getObjectiveScaling () const
Private Member Functions
Internal methods to help compute the distance, its gradient and hessian
double	dist_to_point (const Ipopt::Number *x)
	Compute the norm-2 distance to the current point to which distance is minimized.
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.
	TNLP2FPNLP ()
	Default Constructor.
	TNLP2FPNLP (const TNLP2FPNLP &)
	Copy Constructor.
void	operator= (const TNLP2FPNLP &)
	Overloaded Equals Operator.
Private Attributes
Ipopt::SmartPtr< TNLP >	tnlp_
	pointer to the tminlp that is being adapted
double	objectiveScalingFactor_
	Scaling factor for the objective.
Ipopt::TNLP::IndexStyleEnum	index_style_
	Ipopt::Index style (C++ or Fortran)
Data for storing the point the distance to which is minimized
vector< Ipopt::Index >	inds_
	Indices of the variables for which distance is minimized (i.e. indices of integer variables in a feasibility pump setting)
vector< Ipopt::Number >	vals_
	Values of the point to which we separate (if x is the point vals_[i] should be x[inds_[i]] )
double	lambda_
	value for the convex combination to take between original objective and distance function.
double	sigma_
	Scaling for the original objective.
int	norm_
	Norm to use (L_1 or L_2).
Flags to select the objective function and extra constraints
bool	use_feasibility_pump_objective_
	Flag to indicate that we want to use the feasibility pump objective.
bool	use_cutoff_constraint_
	Flag to indicate that we want to use a cutoff constraint This constraint has the form f(x) <= (1-epsilon) f(x')
bool	use_local_branching_constraint_
	Flag to indicate that we want to use a local branching constraint.
Data for storing the rhs of the extra constraints
double	cutoff_
	Value of best solution known.
double	rhs_local_branching_constraint_
	RHS of local branching constraint.

Detailed Description

This is an adapter class to convert an NLP to a Feasibility Pump NLP by changing the objective function to the (2-norm) distance to a point.

The extra function is set_dist_to_point_obj(size_t n, const double *, const int *)

Definition at line 22 of file BonTNLP2FPNLP.hpp.

Constructor & Destructor Documentation

Bonmin::TNLP2FPNLP::TNLP2FPNLP	(	const Ipopt::SmartPtr< Ipopt::TNLP >	tnlp,
		double	objectiveScalingFactor = `100`
	)

Build using tnlp as source problem.

Bonmin::TNLP2FPNLP::TNLP2FPNLP	(	const Ipopt::SmartPtr< TNLP >	tnlp,
		const Ipopt::SmartPtr< TNLP2FPNLP >	other
	)

Build using tnlp as source problem and using other for all other parameters.

virtual Bonmin::TNLP2FPNLP::~TNLP2FPNLP ( ) [virtual]

Default destructor.

Bonmin::TNLP2FPNLP::TNLP2FPNLP ( ) [private]

Default Constructor.

Bonmin::TNLP2FPNLP::TNLP2FPNLP ( const TNLP2FPNLP & ) [private]

Copy Constructor.

Member Function Documentation

void Bonmin::TNLP2FPNLP::use ( Ipopt::SmartPtr< TNLP > tnlp ) [inline]

Definition at line 36 of file BonTNLP2FPNLP.hpp.

References tnlp_.

void Bonmin::TNLP2FPNLP::set_use_feasibility_pump_objective ( bool use_feasibility_pump_objective ) [inline]

Flag to indicate that we want to use the feasibility pump objective.

Definition at line 41 of file BonTNLP2FPNLP.hpp.

References use_feasibility_pump_objective_.

void Bonmin::TNLP2FPNLP::set_use_cutoff_constraint ( bool use_cutoff_constraint ) [inline]

Flag to indicate that we want to use a cutoff constraint This constraint has the form f(x) <= (1-epsilon) f(x')

Definition at line 46 of file BonTNLP2FPNLP.hpp.

References use_cutoff_constraint_.

void Bonmin::TNLP2FPNLP::set_use_local_branching_constraint ( bool use_local_branching_constraint ) [inline]

Flag to indicate that we want to use a local branching constraint.

Definition at line 50 of file BonTNLP2FPNLP.hpp.

References use_local_branching_constraint_.

void Bonmin::TNLP2FPNLP::set_cutoff ( Ipopt::Number cutoff )

Set the cutoff value to use in the cutoff constraint.

void Bonmin::TNLP2FPNLP::set_rhs_local_branching_constraint ( double rhs_local_branching_constraint ) [inline]

Set the rhs of the local branching constraint.

Definition at line 60 of file BonTNLP2FPNLP.hpp.

References rhs_local_branching_constraint_.

void Bonmin::TNLP2FPNLP::set_dist_to_point_obj	(	size_t	n,
		const Ipopt::Number *	vals,
		const Ipopt::Index *	inds
	)

Set the point to which distance is minimized.

The distance is minimize in a subspace define by a subset of coordinates

Parameters:

n	number of coordinates on which distance is minimized
inds	indices of the coordinates on which distance is minimized
vals	values of the point for coordinates in ind

void Bonmin::TNLP2FPNLP::setSigma ( double sigma ) [inline]

Set the value for sigma.

Definition at line 76 of file BonTNLP2FPNLP.hpp.

References sigma_.

void Bonmin::TNLP2FPNLP::setLambda ( double lambda ) [inline]

Set the value for lambda.

Definition at line 80 of file BonTNLP2FPNLP.hpp.

References lambda_.

void Bonmin::TNLP2FPNLP::setNorm ( int norm ) [inline]

Set the value for simgma.

Definition at line 84 of file BonTNLP2FPNLP.hpp.

References norm_.

virtual bool Bonmin::TNLP2FPNLP::get_nlp_info	(	Ipopt::Index &	n,
		Ipopt::Index &	m,
		Ipopt::Index &	nnz_jac_g,
		Ipopt::Index &	nnz_h_lag,
		Ipopt::TNLP::IndexStyleEnum &	index_style
	)		`[virtual]`

get info from nlp_ and add hessian information

virtual bool Bonmin::TNLP2FPNLP::get_bounds_info	(	Ipopt::Index	n,
		Ipopt::Number *	x_l,
		Ipopt::Number *	x_u,
		Ipopt::Index	m,
		Ipopt::Number *	g_l,
		Ipopt::Number *	g_u
	)		`[virtual]`

This call is just passed onto tnlp_.

virtual bool Bonmin::TNLP2FPNLP::get_starting_point	(	Ipopt::Index	n,
		bool	init_x,
		Ipopt::Number *	x,
		bool	init_z,
		Ipopt::Number *	z_L,
		Ipopt::Number *	z_U,
		Ipopt::Index	m,
		bool	init_lambda,
		Ipopt::Number *	lambda
	)		`[inline, virtual]`

Passed onto tnlp_.

Definition at line 102 of file BonTNLP2FPNLP.hpp.

References tnlp_, use_cutoff_constraint_, and use_local_branching_constraint_.

virtual bool Bonmin::TNLP2FPNLP::eval_f	(	Ipopt::Index	n,
		const Ipopt::Number *	x,
		bool	new_x,
		Ipopt::Number &	obj_value
	)		`[virtual]`

overloaded to return the value of the objective function

virtual bool Bonmin::TNLP2FPNLP::eval_grad_f	(	Ipopt::Index	n,
		const Ipopt::Number *	x,
		bool	new_x,
		Ipopt::Number *	grad_f
	)		`[virtual]`

overload this method to return the vector of the gradient of the objective w.r.t.

x

virtual bool Bonmin::TNLP2FPNLP::eval_g	(	Ipopt::Index	n,
		const Ipopt::Number *	x,
		bool	new_x,
		Ipopt::Index	m,
		Ipopt::Number *	g
	)		`[virtual]`

overload to return the values of the left-hand side of the constraints

virtual bool Bonmin::TNLP2FPNLP::eval_jac_g	(	Ipopt::Index	n,
		const Ipopt::Number *	x,
		bool	new_x,
		Ipopt::Index	m,
		Ipopt::Index	nele_jac,
		Ipopt::Index *	iRow,
		Ipopt::Index *	jCol,
		Ipopt::Number *	values
	)		`[virtual]`

overload to return the jacobian of g

virtual bool Bonmin::TNLP2FPNLP::eval_h	(	Ipopt::Index	n,
		const Ipopt::Number *	x,
		bool	new_x,
		Ipopt::Number	obj_factor,
		Ipopt::Index	m,
		const Ipopt::Number *	lambda,
		bool	new_lambda,
		Ipopt::Index	nele_hess,
		Ipopt::Index *	iRow,
		Ipopt::Index *	jCol,
		Ipopt::Number *	values
	)		`[virtual]`

Evaluate the modified Hessian of the Lagrangian.

virtual void Bonmin::TNLP2FPNLP::finalize_solution	(	Ipopt::SolverReturn	status,
		Ipopt::Index	n,
		const Ipopt::Number *	x,
		const Ipopt::Number *	z_L,
		const Ipopt::Number *	z_U,
		Ipopt::Index	m,
		const Ipopt::Number *	g,
		const Ipopt::Number *	lambda,
		Ipopt::Number	obj_value,
		const Ipopt::IpoptData *	ip_data,
		Ipopt::IpoptCalculatedQuantities *	ip_cq
	)		`[virtual]`