Base class for all MINLPs that use a standard triplet matrix form and dense vectors. More...

#include <BonTMINLP.hpp>

Inheritance diagram for Bonmin::TMINLP:

Classes
struct	BranchingInfo
	Stores branching priorities information. More...
struct	MarkedNonConvex
	Structure for marked non-convex constraints. More...
class	PerturbInfo
	Class to store perturbation radii for variables in the model. More...
struct	SimpleConcaveConstraint
	Structure which describes a constraints of the form $f[ y F(x) ] with a concave function. More...
struct	SosInfo
	Class to store sos constraints for model. More...
Public Types
enum	SolverReturn { SUCCESS, INFEASIBLE, CONTINUOUS_UNBOUNDED, LIMIT_EXCEEDED, USER_INTERRUPT, MINLP_ERROR }
	Return statuses of algorithm. More...
enum	VariableType { CONTINUOUS, BINARY, INTEGER }
	Type of the variables. More...
enum	Convexity { Convex, NonConvex, SimpleConcave }
	Used to mark constraints of the problem. More...
Public Member Functions
virtual const BranchingInfo *	branchingInfo () const =0
virtual const SosInfo *	sosConstraints () const =0
virtual const PerturbInfo *	perturbInfo () const
virtual bool	hasUpperBoundingObjective ()
	Say if has a specific function to compute upper bounds.
virtual bool	eval_upper_bound_f (Ipopt::Index n, const Ipopt::Number *x, Ipopt::Number &obj_value)
	overload this method to return the value of an alternative objective function for upper bounding (to use it hasUpperBoundingObjective should return true).
virtual bool	get_constraint_convexities (int m, TMINLP::Convexity *constraints_convexities) const
	Get accest to constraint convexities.
virtual bool	get_number_nonconvex (int &number_non_conv, int &number_concave) const
	Get dimension information on nonconvex constraints.
virtual bool	get_constraint_convexities (int number_non_conv, MarkedNonConvex *non_convs) const
	Get array describing the constraints marked nonconvex in the model.
virtual bool	get_simple_concave_constraints (int number_concave, SimpleConcaveConstraint *simple_concave) const
	Fill array containing indices of simple concave constraints.
virtual bool	hasLinearObjective ()
	Say if problem has a linear objective (for OA)
bool	hasGeneralInteger ()
	Say if problem has general integer variables.
virtual const int *	get_const_xtra_id () const
	Access array describing constraint to which perspectives should be applied.
Constructors/Destructors
	TMINLP ()
	Default destructor.
virtual	~TMINLP ()
	Default destructor.
methods to gather information about the MINLP
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)=0
	overload this method to return the number of variables and constraints, and the number of non-zeros in the jacobian and the hessian.
virtual bool	get_scaling_parameters (Ipopt::Number &obj_scaling, bool &use_x_scaling, Ipopt::Index n, Ipopt::Number x_scaling, bool &use_g_scaling, Ipopt::Index m, Ipopt::Number g_scaling)
	overload this method to return scaling parameters.
virtual bool	get_variables_types (Ipopt::Index n, VariableType *var_types)=0
	overload this method to provide the variables types.
virtual bool	get_variables_linearity (Ipopt::Index n, Ipopt::TNLP::LinearityType *var_types)=0
	overload this method to provide the variables linearity.
virtual bool	get_constraints_linearity (Ipopt::Index m, Ipopt::TNLP::LinearityType *const_types)=0
	overload this method to provide the constraint linearity.
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)=0
	overload this method to return the information about the bound on the variables and constraints.
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)=0
	overload this method to return the starting point.
virtual bool	eval_f (Ipopt::Index n, const Ipopt::Number *x, bool new_x, Ipopt::Number &obj_value)=0
	overload this method 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)=0
	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)=0
	overload this method to return the vector of constraint values
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)=0
	overload this method to return the jacobian of the constraints.
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)=0
	overload this method to return the hessian of the lagrangian.
virtual bool	eval_gi (Ipopt::Index n, const Ipopt::Number *x, bool new_x, Ipopt::Index i, Ipopt::Number &gi)
	Compute the value of a single constraint.
virtual bool	eval_grad_gi (Ipopt::Index n, const Ipopt::Number x, bool new_x, Ipopt::Index i, Ipopt::Index &nele_grad_gi, Ipopt::Index jCol, Ipopt::Number *values)
	Compute the structure or values of the gradient for one constraint.
Solution Methods
virtual void	finalize_solution (TMINLP::SolverReturn status, Ipopt::Index n, const Ipopt::Number *x, Ipopt::Number obj_value)=0
	This method is called when the algorithm is complete so the TNLP can store/write the solution.
Protected Member Functions

	TMINLP (const TMINLP &)
	Copy constructor.
void	operator= (const TMINLP &)
	Overloaded Equals Operator.
Friends
class	TMINLP2TNLP

Detailed Description

Base class for all MINLPs that use a standard triplet matrix form and dense vectors.

The class TMINLP2TNLP allows the caller to produce a viable TNLP from the MINLP (by relaxing binary and/or integers, or by fixing them), which can then be solved by Ipopt.

This interface presents the problem form:

$\begin{array}{rl} &min f(x)\\ \mbox{s.t.}&\\ & g^L <= g(x) <= g^U\\ & x^L <= x <= x^U\\ \end{array}$

Where each x_i is either a continuous, binary, or integer variable. If x_i is binary, the bounds [xL,xU] are assumed to be [0,1]. In order to specify an equality constraint, set gL_i = gU_i = rhs. The value that indicates "infinity" for the bounds (i.e. the variable or constraint has no lower bound (-infinity) or upper bound (+infinity)) is set through the option nlp_lower_bound_inf and nlp_upper_bound_inf. To indicate that a variable has no upper or lower bound, set the bound to -ipopt_inf or +ipopt_inf respectively

Definition at line 59 of file BonTMINLP.hpp.

Member Enumeration Documentation

enum Bonmin::TMINLP::SolverReturn

Return statuses of algorithm.

Enumerator:

SUCCESS
INFEASIBLE
CONTINUOUS_UNBOUNDED
LIMIT_EXCEEDED
USER_INTERRUPT
MINLP_ERROR

Definition at line 64 of file BonTMINLP.hpp.

enum Bonmin::TMINLP::VariableType

Type of the variables.

Enumerator:

CONTINUOUS
BINARY
INTEGER

Definition at line 192 of file BonTMINLP.hpp.

enum Bonmin::TMINLP::Convexity

Used to mark constraints of the problem.

Enumerator:

Convex	Constraint is convex.
NonConvex	Constraint is non-convex.
SimpleConcave	Constraint is concave of the simple form y >= F(x).

Definition at line 348 of file BonTMINLP.hpp.

Constructor & Destructor Documentation

Bonmin::TMINLP::TMINLP ( )

Default destructor.

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

Default destructor.

Bonmin::TMINLP::TMINLP ( const TMINLP & ) [protected]

Copy constructor.

Copy Constructor

Member Function Documentation

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

overload this method to return the number of variables and constraints, and the number of non-zeros in the jacobian and the hessian.

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

virtual bool Bonmin::TMINLP::get_scaling_parameters	(	Ipopt::Number &	obj_scaling,
		bool &	use_x_scaling,
		Ipopt::Index	n,
		Ipopt::Number *	x_scaling,
		bool &	use_g_scaling,
		Ipopt::Index	m,
		Ipopt::Number *	g_scaling
	)		`[inline, virtual]`

overload this method to return scaling parameters.

This is only called if the options are set to retrieve user scaling. There, use_x_scaling (or use_g_scaling) should get set to true only if the variables (or constraints) are to be scaled. This method should return true only if the scaling parameters could be provided.

Reimplemented in Bonmin::TMINLPLinObj.

Definition at line 222 of file BonTMINLP.hpp.

virtual bool Bonmin::TMINLP::get_variables_types	(	Ipopt::Index	n,
		VariableType *	var_types
	)		`[pure virtual]`

overload this method to provide the variables types.

The var_types array will be allocated with length n.

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

virtual bool Bonmin::TMINLP::get_variables_linearity	(	Ipopt::Index	n,
		Ipopt::TNLP::LinearityType *	var_types
	)		`[pure virtual]`

overload this method to provide the variables linearity.

array should be allocated with length at least n.

Implemented in Bonmin::TMINLPLinObj, and Bonmin::AmplTMINLP.

virtual bool Bonmin::TMINLP::get_constraints_linearity	(	Ipopt::Index	m,
		Ipopt::TNLP::LinearityType *	const_types
	)		`[pure virtual]`

overload this method to provide the constraint linearity.

array should be allocated with length at least m.

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

virtual bool Bonmin::TMINLP::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
	)		`[pure virtual]`

overload this method to return the information about the bound on the variables and constraints.

The value that indicates that a bound does not exist is specified in the parameters nlp_lower_bound_inf and nlp_upper_bound_inf. By default, nlp_lower_bound_inf is -1e19 and nlp_upper_bound_inf is 1e19. An exception will be thrown if x_l and x_u are not 0,1 for binary variables

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

virtual bool Bonmin::TMINLP::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
	)		`[pure virtual]`

overload this method to return the starting point.

The bools init_x and init_lambda are both inputs and outputs. As inputs, they indicate whether or not the algorithm wants you to initialize x and lambda respectively. If, for some reason, the algorithm wants you to initialize these and you cannot, set the respective bool to false.

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

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

overload this method to return the value of the objective function

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

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

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

x

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

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

overload this method to return the vector of constraint values

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

virtual bool Bonmin::TMINLP::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
	)		`[pure virtual]`

overload this method to return the jacobian of the constraints.

The vectors iRow and jCol only need to be set once. The first call is used to set the structure only (iRow and jCol will be non-NULL, and values will be NULL) For subsequent calls, iRow and jCol will be NULL.

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

virtual bool Bonmin::TMINLP::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
	)		`[pure virtual]`

overload this method to return the hessian of the lagrangian.

The vectors iRow and jCol only need to be set once (during the first call). The first call is used to set the structure only (iRow and jCol will be non-NULL, and values will be NULL) For subsequent calls, iRow and jCol will be NULL. This matrix is symmetric - specify the lower diagonal only

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

virtual bool Bonmin::TMINLP::eval_gi	(	Ipopt::Index	n,
		const Ipopt::Number *	x,
		bool	new_x,
		Ipopt::Index	i,
		Ipopt::Number &	gi
	)		`[inline, virtual]`

Compute the value of a single constraint.

The constraint number is i (starting counting from 0.

Reimplemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

Definition at line 304 of file BonTMINLP.hpp.

virtual bool Bonmin::TMINLP::eval_grad_gi	(	Ipopt::Index	n,
		const Ipopt::Number *	x,
		bool	new_x,
		Ipopt::Index	i,
		Ipopt::Index &	nele_grad_gi,
		Ipopt::Index *	jCol,
		Ipopt::Number *	values
	)		`[inline, virtual]`

Compute the structure or values of the gradient for one constraint.

The constraint * number is i (starting counting from 0. Other things are like with eval_jac_g.

Reimplemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

Definition at line 313 of file BonTMINLP.hpp.

virtual void Bonmin::TMINLP::finalize_solution	(	TMINLP::SolverReturn	status,
		Ipopt::Index	n,
		const Ipopt::Number *	x,
		Ipopt::Number	obj_value
	)		`[pure virtual]`

This method is called when the algorithm is complete so the TNLP can store/write the solution.

Implemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

virtual const BranchingInfo* Bonmin::TMINLP::branchingInfo ( ) const [pure virtual]

Implemented in Bonmin::AmplTMINLP, MyTMINLP, and Bonmin::TMINLPLinObj.

virtual const SosInfo* Bonmin::TMINLP::sosConstraints ( ) const [pure virtual]

Implemented in Bonmin::AmplTMINLP, Bonmin::TMINLPLinObj, and MyTMINLP.

virtual const PerturbInfo* Bonmin::TMINLP::perturbInfo ( ) const [inline, virtual]

Reimplemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

Definition at line 333 of file BonTMINLP.hpp.

virtual bool Bonmin::TMINLP::hasUpperBoundingObjective ( ) [inline, virtual]

Say if has a specific function to compute upper bounds.

Reimplemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

Definition at line 339 of file BonTMINLP.hpp.

virtual bool Bonmin::TMINLP::eval_upper_bound_f	(	Ipopt::Index	n,
		const Ipopt::Number *	x,
		Ipopt::Number &	obj_value
	)		`[inline, virtual]`

overload this method to return the value of an alternative objective function for upper bounding (to use it hasUpperBoundingObjective should return true).

Reimplemented in Bonmin::AmplTMINLP, and Bonmin::TMINLPLinObj.

Definition at line 344 of file BonTMINLP.hpp.