Class for handling NLPs using CouenneProblem. More...

#include <CouenneTNLP.hpp>

Inheritance diagram for Couenne::CouenneTNLP:

Collaboration diagram for Couenne::CouenneTNLP:

Public Member Functions
	CouenneTNLP ()
	Empty constructor. More...

	CouenneTNLP (CouenneProblem *)
	Constructor. More...

	CouenneTNLP (const CouenneTNLP &)
	Copy constructor. More...

CouenneTNLP &	operator= (const CouenneTNLP &rhs)
	Assignment. More...

CouenneTNLP *	clone ()
	Clone. More...

virtual	~CouenneTNLP ()
	Destructor. More...

void	setInitSol (const double *sol)
	set initial solution More...

CouNumber *	getSolution ()
	returns best solution (if it exists) More...

CouNumber	getSolValue ()
	returns value of the best solution More...

virtual bool	get_nlp_info (Ipopt::Index &n, Ipopt::Index &m, Ipopt::Index &nnz_jac_g, Ipopt::Index &nnz_h_lag, enum Ipopt::TNLP::IndexStyleEnum &index_style)
	return the number of variables and constraints, and the number of non-zeros in the jacobian and the hessian. 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)
	return the information about the bound on the variables and constraints. More...

virtual bool	get_variables_linearity (Ipopt::Index n, Ipopt::TNLP::LinearityType *var_types)
	return the variables linearity (TNLP::Linear or TNLP::NonLinear). More...

virtual bool	get_constraints_linearity (Ipopt::Index m, Ipopt::TNLP::LinearityType *const_types)
	return the constraint linearity. 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)
	return the starting point. More...

virtual bool	eval_f (Ipopt::Index n, const Ipopt::Number *x, bool new_x, Ipopt::Number &obj_value)
	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)
	return the vector of the gradient of the objective w.r.t. x More...

virtual bool	eval_g (Ipopt::Index n, const Ipopt::Number x, bool new_x, Ipopt::Index m, Ipopt::Number g)
	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)
	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)
	return the hessian of the lagrangian. More...

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. More...

virtual bool	intermediate_callback (Ipopt::AlgorithmMode mode, Ipopt::Index iter, Ipopt::Number obj_value, Ipopt::Number inf_pr, Ipopt::Number inf_du, Ipopt::Number mu, Ipopt::Number d_norm, Ipopt::Number regularization_size, Ipopt::Number alpha_du, Ipopt::Number alpha_pr, Ipopt::Index ls_trials, const Ipopt::IpoptData ip_data, Ipopt::IpoptCalculatedQuantities ip_cq)
	Intermediate Callback method for the user. More...

Methods for quasi-Newton approximation. If the second
derivatives are approximated by Ipopt, it is better to do this only in the space of nonlinear variables. The following methods are call by Ipopt if the quasi-Newton approximation is selected. If -1 is returned as number of nonlinear variables, Ipopt assumes that all variables are nonlinear. Otherwise, it calls get_list_of_nonlinear_variables with an array into which the indices of the nonlinear variables should be written - the array has the lengths num_nonlin_vars, which is identical with the return value of get_number_of_nonlinear_variables (). It is assumed that the indices are counted starting with 1 in the FORTRAN_STYLE, and 0 for the C_STYLE.
CouenneProblem *	problem_
	Pointer to the object containing all info. More...

CouNumber *	sol0_
	Initial solution. More...

CouNumber *	sol_
	Optimal solution. More...

CouNumber	bestZ_
	Value of the optimal solution. More...

std::vector< std::pair< int, expression * > >	gradient_
	expression gradient (packed sparse vector) More...

std::set< int >	nonLinVars_
	list of nonlinear variables More...

ExprJac	Jac_
	Jacobian. More...

ExprHess *	HLa_
	Hessian — there are 1+m of them, but all are squeezed in a single object. More...

CouenneSparseMatrix *	optHessian_
	Stores the values of the Hessian of the Lagrangian at optimum for later use. More...

bool	saveOptHessian_
	Flag to be set to save this solution's Lagrangian Hessian in above structure. More...

virtual Ipopt::Index	get_number_of_nonlinear_variables ()
	Pointer to the object containing all info. More...

virtual bool	get_list_of_nonlinear_variables (Ipopt::Index num_nonlin_vars, Ipopt::Index *pos_nonlin_vars)
	get real list More...

virtual void	setObjective (expression *newObj)
	Change objective function and modify gradient expressions accordingly. More...

CouenneSparseMatrix *&	optHessian ()
	Get methods. More...

bool &	getSaveOptHessian ()
	set and get saveOptHessian_ More...

Detailed Description

Class for handling NLPs using CouenneProblem.

Definition at line 27 of file CouenneTNLP.hpp.

Constructor & Destructor Documentation

CouenneTNLP::CouenneTNLP ( )

Empty constructor.

Definition at line 32 of file CouenneTNLP.cpp.

CouenneTNLP::CouenneTNLP ( CouenneProblem * p )

Constructor.

Definition at line 61 of file CouenneTNLP.cpp.

CouenneTNLP::CouenneTNLP ( const CouenneTNLP & rhs )

Copy constructor.

Definition at line 127 of file CouenneTNLP.cpp.

CouenneTNLP::~CouenneTNLP ( )

virtual

Destructor.

Empty constructor.

Definition at line 44 of file CouenneTNLP.cpp.

Member Function Documentation

CouenneTNLP & CouenneTNLP::operator= ( const CouenneTNLP & rhs )

Assignment.

Definition at line 132 of file CouenneTNLP.cpp.

CouenneTNLP * CouenneTNLP::clone ( )

Clone.

Definition at line 153 of file CouenneTNLP.cpp.

void CouenneTNLP::setInitSol ( const double * sol )

set initial solution

Definition at line 180 of file CouenneTNLP.cpp.

CouNumber* Couenne::CouenneTNLP::getSolution ( )

inline

returns best solution (if it exists)

Definition at line 53 of file CouenneTNLP.hpp.

CouNumber Couenne::CouenneTNLP::getSolValue ( )

inline

returns value of the best solution

Definition at line 57 of file CouenneTNLP.hpp.

bool CouenneTNLP::get_nlp_info	(	Ipopt::Index &	n,
		Ipopt::Index &	m,
		Ipopt::Index &	nnz_jac_g,
		Ipopt::Index &	nnz_h_lag,
		enum Ipopt::TNLP::IndexStyleEnum &	index_style
	)

virtual

return the number of variables and constraints, and the number of non-zeros in the jacobian and the hessian.

The index_style parameter lets you specify C or Fortran style indexing for the sparse matrix iRow and jCol parameters. C_STYLE is 0-based, and FORTRAN_STYLE is 1-based.

Definition at line 162 of file CouenneTNLP.cpp.

bool CouenneTNLP::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

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. (see TNLPAdapter)

Definition at line 195 of file CouenneTNLP.cpp.

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

virtual

return the variables linearity (TNLP::Linear or TNLP::NonLinear).

The var_types array should be allocated with length at least n. (default implementation just return false and does not fill the array).

Definition at line 259 of file CouenneTNLP.cpp.

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

virtual

return the constraint linearity.

array should be alocated with length at least n. (default implementation just return false and does not fill the array).

Definition at line 272 of file CouenneTNLP.cpp.

bool CouenneTNLP::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
	)

virtual

return the starting point.

The bool variables indicate whether the algorithm wants you to initialize x, z_L/z_u, and lambda, respectively. If, for some reason, the algorithm wants you to initialize these and you cannot, return false, which will cause Ipopt to stop. You will have to run Ipopt with different options then.

Definition at line 316 of file CouenneTNLP.cpp.

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

virtual

return the value of the objective function

Definition at line 332 of file CouenneTNLP.cpp.

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

virtual

return the vector of the gradient of the objective w.r.t. x

Definition at line 345 of file CouenneTNLP.cpp.

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

virtual

return the vector of constraint values

Definition at line 376 of file CouenneTNLP.cpp.

bool CouenneTNLP::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

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.

Definition at line 441 of file CouenneTNLP.cpp.

bool CouenneTNLP::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

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. A default implementation is provided, in case the user wants to se quasi-Newton approximations to estimate the second derivatives and doesn't not neet to implement this method.

first call, must determine structure iRow/jCol

generic call, iRow/jCol are known and we should fill in the values

Definition at line 501 of file CouenneTNLP.cpp.

void CouenneTNLP::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

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

Definition at line 616 of file CouenneTNLP.cpp.

bool CouenneTNLP::intermediate_callback	(	Ipopt::AlgorithmMode	mode,
		Ipopt::Index	iter,
		Ipopt::Number	obj_value,
		Ipopt::Number	inf_pr,
		Ipopt::Number	inf_du,
		Ipopt::Number	mu,
		Ipopt::Number	d_norm,
		Ipopt::Number	regularization_size,
		Ipopt::Number	alpha_du,
		Ipopt::Number	alpha_pr,
		Ipopt::Index	ls_trials,
		const Ipopt::IpoptData *	ip_data,
		Ipopt::IpoptCalculatedQuantities *	ip_cq
	)

virtual

Intermediate Callback method for the user.

Providing dummy default implementation. For details see IntermediateCallBack in IpNLP.hpp.

Definition at line 733 of file CouenneTNLP.cpp.

Index CouenneTNLP::get_number_of_nonlinear_variables ( )

virtual

Pointer to the object containing all info.

Definition at line 754 of file CouenneTNLP.cpp.

bool CouenneTNLP::get_list_of_nonlinear_variables	(	Ipopt::Index	num_nonlin_vars,
		Ipopt::Index *	pos_nonlin_vars
	)

virtual

get real list

Definition at line 764 of file CouenneTNLP.cpp.

void CouenneTNLP::setObjective ( expression * newObj )

virtual

Change objective function and modify gradient expressions accordingly.

Definition at line 578 of file CouenneTNLP.cpp.

CouenneSparseMatrix*& Couenne::CouenneTNLP::optHessian ( )

inline

Get methods.

Definition at line 182 of file CouenneTNLP.hpp.

bool& Couenne::CouenneTNLP::getSaveOptHessian ( )

inline

set and get saveOptHessian_

Definition at line 186 of file CouenneTNLP.hpp.

Member Data Documentation

CouenneProblem* Couenne::CouenneTNLP::problem_

private

Pointer to the object containing all info.

Definition at line 192 of file CouenneTNLP.hpp.

CouNumber* Couenne::CouenneTNLP::sol0_

private

Initial solution.

Definition at line 195 of file CouenneTNLP.hpp.

CouNumber* Couenne::CouenneTNLP::sol_

private

Optimal solution.

Definition at line 198 of file CouenneTNLP.hpp.

CouNumber Couenne::CouenneTNLP::bestZ_

private

Value of the optimal solution.

Definition at line 201 of file CouenneTNLP.hpp.

std::vector<std::pair <int, expression *> > Couenne::CouenneTNLP::gradient_

private

expression gradient (packed sparse vector)

Definition at line 204 of file CouenneTNLP.hpp.

std::set<int> Couenne::CouenneTNLP::nonLinVars_

private

list of nonlinear variables

Definition at line 207 of file CouenneTNLP.hpp.

ExprJac Couenne::CouenneTNLP::Jac_

private

Jacobian.

Definition at line 210 of file CouenneTNLP.hpp.

ExprHess* Couenne::CouenneTNLP::HLa_

private

Hessian — there are 1+m of them, but all are squeezed in a single object.

Definition at line 214 of file CouenneTNLP.hpp.

CouenneSparseMatrix* Couenne::CouenneTNLP::optHessian_

private

Stores the values of the Hessian of the Lagrangian at optimum for later use.

Definition at line 217 of file CouenneTNLP.hpp.

bool Couenne::CouenneTNLP::saveOptHessian_

private

Flag to be set to save this solution's Lagrangian Hessian in above structure.

Definition at line 220 of file CouenneTNLP.hpp.

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

/tmp/OS-2.10.2/Couenne/src/interfaces/CouenneTNLP.hpp
/tmp/OS-2.10.2/Couenne/src/interfaces/CouenneTNLP.cpp

Public Member Functions

Methods for quasi-Newton approximation. If the second

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation