A C++ example for interfacing an MINLP with bonmin. More...

#include <MyTMINLP.hpp>

Inheritance diagram for MyTMINLP:

Collaboration diagram for MyTMINLP:

Public Member Functions
	MyTMINLP ()
	Default constructor. More...

virtual	~MyTMINLP ()
	virtual destructor. More...

	MyTMINLP (const MyTMINLP &other)
	Copy constructor. More...

virtual const SosInfo *	sosConstraints () const

virtual const BranchingInfo *	branchingInfo () const

void	printSolutionAtEndOfAlgorithm ()

	MyTMINLP ()
	Default constructor. More...

virtual	~MyTMINLP ()
	virtual destructor. More...

	MyTMINLP (const MyTMINLP &other)
	Copy constructor. More...

virtual const SosInfo *	sosConstraints () const

virtual const BranchingInfo *	branchingInfo () const

void	printSolutionAtEndOfAlgorithm ()

Overloaded functions specific to a TMINLP.
Assignment operator. no data = nothing to assign
virtual bool	get_variables_types (Index n, VariableType *var_types)
	Pass the type of the variables (INTEGER, BINARY, CONTINUOUS) to the optimizer. More...

virtual bool	get_variables_linearity (Index n, Ipopt::TNLP::LinearityType *var_types)
	Pass info about linear and nonlinear variables. More...

virtual bool	get_constraints_linearity (Index m, Ipopt::TNLP::LinearityType *const_types)
	Pass the type of the constraints (LINEAR, NON_LINEAR) to the optimizer. More...

virtual bool	get_variables_types (Index n, VariableType *var_types)
	Pass the type of the variables (INTEGER, BINARY, CONTINUOUS) to the optimizer. More...

virtual bool	get_variables_linearity (Index n, Ipopt::TNLP::LinearityType *var_types)
	Pass info about linear and nonlinear variables. More...

virtual bool	get_constraints_linearity (Index m, Ipopt::TNLP::LinearityType *const_types)
	Pass the type of the constraints (LINEAR, NON_LINEAR) to the optimizer. More...

Overloaded functions defining a TNLP.
This group of function implement the various elements needed to define and solve a TNLP. They are the same as those in a standard Ipopt NLP problem
virtual bool	get_nlp_info (Index &n, Index &m, Index &nnz_jac_g, Index &nnz_h_lag, TNLP::IndexStyleEnum &index_style)
	Method to pass the main dimensions of the problem to Ipopt. More...

virtual bool	get_bounds_info (Index n, Number x_l, Number x_u, Index m, Number g_l, Number g_u)
	Method to pass the bounds on variables and constraints to Ipopt. More...

virtual bool	get_starting_point (Index n, bool init_x, Number x, bool init_z, Number z_L, Number z_U, Index m, bool init_lambda, Number lambda)
	Method to to pass the starting point for optimization to Ipopt. More...

virtual bool	eval_f (Index n, const Number *x, bool new_x, Number &obj_value)
	Method which compute the value of the objective function at point x. More...

virtual bool	eval_grad_f (Index n, const Number x, bool new_x, Number grad_f)
	Method which compute the gradient of the objective at a point x. More...

virtual bool	eval_g (Index n, const Number x, bool new_x, Index m, Number g)
	Method which compute the value of the functions defining the constraints at a point x. More...

virtual bool	eval_jac_g (Index n, const Number x, bool new_x, Index m, Index nele_jac, Index iRow, Index jCol, Number values)
	Method to compute the Jacobian of the functions defining the constraints. More...

virtual bool	eval_h (Index n, const Number x, bool new_x, Number obj_factor, Index m, const Number lambda, bool new_lambda, Index nele_hess, Index iRow, Index jCol, Number *values)
	Method to compute the Jacobian of the functions defining the constraints. More...

virtual void	finalize_solution (TMINLP::SolverReturn status, Index n, const Number *x, Number obj_value)
	Method called by Ipopt at the end of optimization. More...

virtual bool	get_nlp_info (Index &n, Index &m, Index &nnz_jac_g, Index &nnz_h_lag, TNLP::IndexStyleEnum &index_style)
	Method to pass the main dimensions of the problem to Ipopt. More...

virtual bool	get_bounds_info (Index n, Number x_l, Number x_u, Index m, Number g_l, Number g_u)
	Method to pass the bounds on variables and constraints to Ipopt. More...

virtual bool	get_starting_point (Index n, bool init_x, Number x, bool init_z, Number z_L, Number z_U, Index m, bool init_lambda, Number lambda)
	Method to to pass the starting point for optimization to Ipopt. More...

virtual bool	eval_f (Index n, const Number *x, bool new_x, Number &obj_value)
	Method which compute the value of the objective function at point x. More...

virtual bool	eval_grad_f (Index n, const Number x, bool new_x, Number grad_f)
	Method which compute the gradient of the objective at a point x. More...

virtual bool	eval_g (Index n, const Number x, bool new_x, Index m, Number g)
	Method which compute the value of the functions defining the constraints at a point x. More...

virtual bool	eval_jac_g (Index n, const Number x, bool new_x, Index m, Index nele_jac, Index iRow, Index jCol, Number values)
	Method to compute the Jacobian of the functions defining the constraints. More...

virtual bool	eval_h (Index n, const Number x, bool new_x, Number obj_factor, Index m, const Number lambda, bool new_lambda, Index nele_hess, Index iRow, Index jCol, Number *values)
	Method to compute the Jacobian of the functions defining the constraints. More...

virtual void	finalize_solution (TMINLP::SolverReturn status, Index n, const Number *x, Number obj_value)
	Method called by Ipopt at the end of optimization. More...

Public Member Functions inherited from Bonmin::TMINLP
virtual const PerturbInfo *	perturbInfo () const

virtual bool	hasUpperBoundingObjective ()
	Say if has a specific function to compute upper bounds. More...

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). More...

virtual bool	get_constraint_convexities (int m, TMINLP::Convexity *constraints_convexities) const
	Get accest to constraint convexities. More...

virtual bool	get_number_nonconvex (int &number_non_conv, int &number_concave) const
	Get dimension information on nonconvex constraints. More...

virtual bool	get_constraint_convexities (int number_non_conv, MarkedNonConvex *non_convs) const
	Get array describing the constraints marked nonconvex in the model. More...

virtual bool	get_simple_concave_constraints (int number_concave, SimpleConcaveConstraint *simple_concave) const
	Fill array containing indices of simple concave constraints. More...

virtual bool	hasLinearObjective ()
	Say if problem has a linear objective (for OA) More...

bool	hasGeneralInteger ()
	Say if problem has general integer variables. More...

virtual const int *	get_const_xtra_id () const
	Access array describing constraint to which perspectives should be applied. More...

	TMINLP ()
	Default destructor. More...

virtual	~TMINLP ()
	Default destructor. More...

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. More...

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. More...

virtual bool	get_variables_types (Ipopt::Index n, VariableType *var_types)=0
	overload this method to provide the variables types. More...

virtual bool	get_variables_linearity (Ipopt::Index n, Ipopt::TNLP::LinearityType *var_types)=0
	overload this method to provide the variables linearity. More...

virtual bool	get_constraints_linearity (Ipopt::Index m, Ipopt::TNLP::LinearityType *const_types)=0
	overload this method to provide the constraint linearity. More...

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. More...

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. More...

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 More...

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. More...

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 More...

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. More...

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. More...

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. More...

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. More...

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. More...

Private Attributes
bool	printSol_

Additional Inherited Members
Public Types inherited from Bonmin::TMINLP
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...

Protected Member Functions inherited from Bonmin::TMINLP
	TMINLP (const TMINLP &)
	Copy constructor. More...

void	operator= (const TMINLP &)
	Overloaded Equals Operator. More...

Detailed Description

A C++ example for interfacing an MINLP with bonmin.

This class implements the following NLP :

$\begin{array}{l} \min - x_0 - x_1 - x_2 \\ \mbox{s.t}\\ (x_1 - \frac{1}{2})^2 + (x_2 - \frac{1}{2})^2 \leq \frac{1}{4} \\ x_0 - x_1 \leq 0 \\ x_0 + x_2 + x_3 \leq 2\\ x_0 \in \{0,1\}^n \; (x_1, x_2) \in R^2 \; x_3 \in N \end{array}$

This class implements the following NLP :

$\begin{array}{l} \min x_0 - x_1 - x_2 \\ \mbox{s.t}\\ (x_1 - \frac{1}{2})^2 + (x_2 - \frac{1}{2})^2 \leq \frac{1}{4} \\ x_0 - x_1 \leq 0 \\ x_1 + x_2 + x_3 \leq 2\\ x_0 \in \{0,1\}^n \; (x_1, x_2) \in R^2 \; x_3 \in N \end{array}$

Definition at line 28 of file MyTMINLP.hpp.

Constructor & Destructor Documentation

MyTMINLP::MyTMINLP ( )

inline

Default constructor.

Definition at line 32 of file MyTMINLP.hpp.

virtual MyTMINLP::~MyTMINLP ( )

inlinevirtual

virtual destructor.

Definition at line 36 of file MyTMINLP.hpp.

MyTMINLP::MyTMINLP ( const MyTMINLP & other )

inline

Copy constructor.

Definition at line 40 of file MyTMINLP.hpp.

MyTMINLP::MyTMINLP ( )

inline

Default constructor.

Definition at line 32 of file MyTMINLP.hpp.

virtual MyTMINLP::~MyTMINLP ( )

inlinevirtual

virtual destructor.

Definition at line 36 of file MyTMINLP.hpp.

MyTMINLP::MyTMINLP ( const MyTMINLP & other )

inline

Copy constructor.

Definition at line 40 of file MyTMINLP.hpp.

Member Function Documentation

bool MyTMINLP::get_variables_types	(	Index	n,
		VariableType *	var_types
	)

virtual

Pass the type of the variables (INTEGER, BINARY, CONTINUOUS) to the optimizer.

Parameters

n	size of var_types (has to be equal to the number of variables in the problem)
var_types	types of the variables (has to be filled by function).

Definition at line 13 of file MyTMINLP.cpp.

bool MyTMINLP::get_variables_linearity	(	Index	n,
		Ipopt::TNLP::LinearityType *	var_types
	)

virtual

Pass info about linear and nonlinear variables.

Definition at line 24 of file MyTMINLP.cpp.

bool MyTMINLP::get_constraints_linearity	(	Index	m,
		Ipopt::TNLP::LinearityType *	const_types
	)

virtual

Pass the type of the constraints (LINEAR, NON_LINEAR) to the optimizer.

Parameters

m	size of const_types (has to be equal to the number of constraints in the problem)
const_types	types of the constraints (has to be filled by function).

Definition at line 35 of file MyTMINLP.cpp.

bool MyTMINLP::get_nlp_info	(	Index &	n,
		Index &	m,
		Index &	nnz_jac_g,
		Index &	nnz_h_lag,
		TNLP::IndexStyleEnum &	index_style
	)

virtual

Method to pass the main dimensions of the problem to Ipopt.

Parameters

n	number of variables in problem.
m	number of constraints.
nnz_jac_g	number of nonzeroes in Jacobian of constraints system.
nnz_h_lag	number of nonzeroes in Hessian of the Lagrangean.
index_style	indicate wether arrays are numbered from 0 (C-style) or from 1 (Fortran).

Returns: true in case of success.

Definition at line 44 of file MyTMINLP.cpp.

bool MyTMINLP::get_bounds_info	(	Index	n,
		Number *	x_l,
		Number *	x_u,
		Index	m,
		Number *	g_l,
		Number *	g_u
	)

virtual

Method to pass the bounds on variables and constraints to Ipopt.

 \param n size of x_l and x_u (has to be equal to the number of variables in the problem)
 \param x_l lower bounds on variables (function should fill it).
 \param x_u upper bounds on the variables (function should fill it).
 \param m size of g_l and g_u (has to be equal to the number of constraints in the problem).
 \param g_l lower bounds of the constraints (function should fill it).
 \param g_u upper bounds of the constraints (function should fill it).

Returns: true in case of success.

Definition at line 56 of file MyTMINLP.cpp.

bool MyTMINLP::get_starting_point	(	Index	n,
		bool	init_x,
		Number *	x,
		bool	init_z,
		Number *	z_L,
		Number *	z_U,
		Index	m,
		bool	init_lambda,
		Number *	lambda
	)

virtual

Method to to pass the starting point for optimization to Ipopt.

Parameters

init_x	do we initialize primals?
x	pass starting primal points (function should fill it if init_x is 1).
m	size of lambda (has to be equal to the number of constraints in the problem).
init_lambda	do we initialize duals of constraints?
lambda	lower bounds of the constraints (function should fill it).

Returns: true in case of success.

Definition at line 85 of file MyTMINLP.cpp.

bool MyTMINLP::eval_f	(	Index	n,
		const Number *	x,
		bool	new_x,
		Number &	obj_value
	)

virtual

Method which compute the value of the objective function at point x.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
obj_value	value of objective in x (has to be computed by the function).

Returns: true in case of success.

Definition at line 103 of file MyTMINLP.cpp.

bool MyTMINLP::eval_grad_f	(	Index	n,
		const Number *	x,
		bool	new_x,
		Number *	grad_f
	)

virtual

Method which compute the gradient of the objective at a point x.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
grad_f	gradient of objective taken in x (function has to fill it).

Returns: true in case of success.

Definition at line 111 of file MyTMINLP.cpp.

bool MyTMINLP::eval_g	(	Index	n,
		const Number *	x,
		bool	new_x,
		Index	m,
		Number *	g
	)

virtual

Method which compute the value of the functions defining the constraints at a point x.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
m	size of array g (has to be equal to the number of constraints in the problem)
grad_f	values of the constraints (function has to fill it).

Returns: true in case of success.

Definition at line 122 of file MyTMINLP.cpp.

bool MyTMINLP::eval_jac_g	(	Index	n,
		const Number *	x,
		bool	new_x,
		Index	m,
		Index	nele_jac,
		Index *	iRow,
		Index *	jCol,
		Number *	values
	)

virtual

Method to compute the Jacobian of the functions defining the constraints.

If the parameter values==NULL fill the arrays iCol and jRow which store the position of the non-zero element of the Jacobian. If the paramenter values!=NULL fill values with the non-zero elements of the Jacobian.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
m	size of array g (has to be equal to the number of constraints in the problem)
grad_f	values of the constraints (function has to fill it).

Returns: true in case of success.

Definition at line 135 of file MyTMINLP.cpp.

bool MyTMINLP::eval_h	(	Index	n,
		const Number *	x,
		bool	new_x,
		Number	obj_factor,
		Index	m,
		const Number *	lambda,
		bool	new_lambda,
		Index	nele_hess,
		Index *	iRow,
		Index *	jCol,
		Number *	values
	)

virtual

Method to compute the Jacobian of the functions defining the constraints.

If the parameter values==NULL fill the arrays iCol and jRow which store the position of the non-zero element of the Jacobian. If the paramenter values!=NULL fill values with the non-zero elements of the Jacobian.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
m	size of array g (has to be equal to the number of constraints in the problem)
grad_f	values of the constraints (function has to fill it).

Returns: true in case of success.

Definition at line 181 of file MyTMINLP.cpp.

void MyTMINLP::finalize_solution	(	TMINLP::SolverReturn	status,
		Index	n,
		const Number *	x,
		Number	obj_value
	)

virtual

Method called by Ipopt at the end of optimization.

Definition at line 205 of file MyTMINLP.cpp.

virtual const SosInfo* MyTMINLP::sosConstraints ( ) const

inlinevirtual

Implements Bonmin::TMINLP.

Definition at line 169 of file MyTMINLP.hpp.

virtual const BranchingInfo* MyTMINLP::branchingInfo ( ) const

inlinevirtual

Implements Bonmin::TMINLP.

Definition at line 170 of file MyTMINLP.hpp.

void MyTMINLP::printSolutionAtEndOfAlgorithm ( )

inline

Definition at line 173 of file MyTMINLP.hpp.

virtual bool MyTMINLP::get_variables_types	(	Index	n,
		VariableType *	var_types
	)

virtual

Pass the type of the variables (INTEGER, BINARY, CONTINUOUS) to the optimizer.

Parameters

n	size of var_types (has to be equal to the number of variables in the problem)
var_types	types of the variables (has to be filled by function).

virtual bool MyTMINLP::get_variables_linearity	(	Index	n,
		Ipopt::TNLP::LinearityType *	var_types
	)

virtual

Pass info about linear and nonlinear variables.

virtual bool MyTMINLP::get_constraints_linearity	(	Index	m,
		Ipopt::TNLP::LinearityType *	const_types
	)

virtual

Pass the type of the constraints (LINEAR, NON_LINEAR) to the optimizer.

Parameters

m	size of const_types (has to be equal to the number of constraints in the problem)
const_types	types of the constraints (has to be filled by function).

virtual bool MyTMINLP::get_nlp_info	(	Index &	n,
		Index &	m,
		Index &	nnz_jac_g,
		Index &	nnz_h_lag,
		TNLP::IndexStyleEnum &	index_style
	)

virtual

Method to pass the main dimensions of the problem to Ipopt.

Parameters

n	number of variables in problem.
m	number of constraints.
nnz_jac_g	number of nonzeroes in Jacobian of constraints system.
nnz_h_lag	number of nonzeroes in Hessian of the Lagrangean.
index_style	indicate wether arrays are numbered from 0 (C-style) or from 1 (Fortran).

Returns: true in case of success.

virtual bool MyTMINLP::get_bounds_info	(	Index	n,
		Number *	x_l,
		Number *	x_u,
		Index	m,
		Number *	g_l,
		Number *	g_u
	)

virtual

Method to pass the bounds on variables and constraints to Ipopt.

 \param n size of x_l and x_u (has to be equal to the number of variables in the problem)
 \param x_l lower bounds on variables (function should fill it).
 \param x_u upper bounds on the variables (function should fill it).
 \param m size of g_l and g_u (has to be equal to the number of constraints in the problem).
 \param g_l lower bounds of the constraints (function should fill it).
 \param g_u upper bounds of the constraints (function should fill it).

Returns: true in case of success.

virtual bool MyTMINLP::get_starting_point	(	Index	n,
		bool	init_x,
		Number *	x,
		bool	init_z,
		Number *	z_L,
		Number *	z_U,
		Index	m,
		bool	init_lambda,
		Number *	lambda
	)

virtual

Method to to pass the starting point for optimization to Ipopt.

Parameters

init_x	do we initialize primals?
x	pass starting primal points (function should fill it if init_x is 1).
m	size of lambda (has to be equal to the number of constraints in the problem).
init_lambda	do we initialize duals of constraints?
lambda	lower bounds of the constraints (function should fill it).

Returns: true in case of success.

virtual bool MyTMINLP::eval_f	(	Index	n,
		const Number *	x,
		bool	new_x,
		Number &	obj_value
	)

virtual

Method which compute the value of the objective function at point x.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
obj_value	value of objective in x (has to be computed by the function).

Returns: true in case of success.

virtual bool MyTMINLP::eval_grad_f	(	Index	n,
		const Number *	x,
		bool	new_x,
		Number *	grad_f
	)

virtual

Method which compute the gradient of the objective at a point x.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
grad_f	gradient of objective taken in x (function has to fill it).

Returns: true in case of success.

virtual bool MyTMINLP::eval_g	(	Index	n,
		const Number *	x,
		bool	new_x,
		Index	m,
		Number *	g
	)

virtual

Method which compute the value of the functions defining the constraints at a point x.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
m	size of array g (has to be equal to the number of constraints in the problem)
grad_f	values of the constraints (function has to fill it).

Returns: true in case of success.

virtual bool MyTMINLP::eval_jac_g	(	Index	n,
		const Number *	x,
		bool	new_x,
		Index	m,
		Index	nele_jac,
		Index *	iRow,
		Index *	jCol,
		Number *	values
	)

virtual

Method to compute the Jacobian of the functions defining the constraints.

If the parameter values==NULL fill the arrays iCol and jRow which store the position of the non-zero element of the Jacobian. If the paramenter values!=NULL fill values with the non-zero elements of the Jacobian.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
m	size of array g (has to be equal to the number of constraints in the problem)
grad_f	values of the constraints (function has to fill it).

Returns: true in case of success.

virtual bool MyTMINLP::eval_h	(	Index	n,
		const Number *	x,
		bool	new_x,
		Number	obj_factor,
		Index	m,
		const Number *	lambda,
		bool	new_lambda,
		Index	nele_hess,
		Index *	iRow,
		Index *	jCol,
		Number *	values
	)

virtual

Method to compute the Jacobian of the functions defining the constraints.

If the parameter values==NULL fill the arrays iCol and jRow which store the position of the non-zero element of the Jacobian. If the paramenter values!=NULL fill values with the non-zero elements of the Jacobian.

Parameters

n	size of array x (has to be the number of variables in the problem).
x	point where to evaluate.
new_x	Is this the first time we evaluate functions at this point? (in the present context we don't care).
m	size of array g (has to be equal to the number of constraints in the problem)
grad_f	values of the constraints (function has to fill it).

Returns: true in case of success.

virtual void MyTMINLP::finalize_solution	(	TMINLP::SolverReturn	status,
		Index	n,
		const Number *	x,
		Number	obj_value
	)

virtual

Method called by Ipopt at the end of optimization.

virtual const SosInfo* MyTMINLP::sosConstraints ( ) const

inlinevirtual

Implements Bonmin::TMINLP.

Definition at line 169 of file MyTMINLP.hpp.

virtual const BranchingInfo* MyTMINLP::branchingInfo ( ) const

inlinevirtual

Implements Bonmin::TMINLP.

Definition at line 170 of file MyTMINLP.hpp.

void MyTMINLP::printSolutionAtEndOfAlgorithm ( )

inline

Definition at line 173 of file MyTMINLP.hpp.

Member Data Documentation

bool MyTMINLP::printSol_

private

Definition at line 177 of file MyTMINLP.hpp.

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

/tmp/OS-2.10.2/Bonmin/examples/CppExample/MyTMINLP.hpp
/tmp/OS-2.10.2/Bonmin/examples/OptionDocGen/MyTMINLP.hpp
/tmp/OS-2.10.2/Bonmin/examples/CppExample/MyTMINLP.cpp
/tmp/OS-2.10.2/Bonmin/examples/OptionDocGen/MyTMINLP.cpp

Public Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation