Public Member Functions
TutorialCpp_NLP Class Reference

#include <TutorialCpp_nlp.hpp>

Inheritance diagram for TutorialCpp_NLP:
Ipopt::TNLP Ipopt::TNLP Ipopt::TNLP Ipopt::ReferencedObject Ipopt::ReferencedObject Ipopt::ReferencedObject

List of all members.

Public Member Functions

 TutorialCpp_NLP (Index N, const Number *a)
 constructor that takes in problem data
virtual ~TutorialCpp_NLP ()
 default destructor
 TutorialCpp_NLP (Index N, const Number *a)
 constructor that takes in problem data
virtual ~TutorialCpp_NLP ()
 default destructor
 TutorialCpp_NLP (Index N, const Number *a)
 constructor that takes in problem data
virtual ~TutorialCpp_NLP ()
 default destructor
Overloaded from TNLP
virtual bool get_nlp_info (Index &n, Index &m, Index &nnz_jac_g, Index &nnz_h_lag, IndexStyleEnum &index_style)
 Method to return some info about the nlp.
virtual bool get_bounds_info (Index n, Number *x_l, Number *x_u, Index m, Number *g_l, Number *g_u)
 Method to return the bounds for my problem.
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 return the starting point for the algorithm.
virtual bool eval_f (Index n, const Number *x, bool new_x, Number &obj_value)
 Method to return the objective value.
virtual bool eval_grad_f (Index n, const Number *x, bool new_x, Number *grad_f)
 Method to return the gradient of the objective.
virtual bool eval_g (Index n, const Number *x, bool new_x, Index m, Number *g)
 Method to return the constraint residuals.
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 return: 1) The structure of the jacobian (if "values" is NULL) 2) The values of the jacobian (if "values" is not NULL)
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 return: 1) The structure of the hessian of the lagrangian (if "values" is NULL) 2) The values of the hessian of the lagrangian (if "values" is not NULL)
virtual bool get_nlp_info (Index &n, Index &m, Index &nnz_jac_g, Index &nnz_h_lag, IndexStyleEnum &index_style)
 Method to return some info about the nlp.
virtual bool get_bounds_info (Index n, Number *x_l, Number *x_u, Index m, Number *g_l, Number *g_u)
 Method to return the bounds for my problem.
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 return the starting point for the algorithm.
virtual bool eval_f (Index n, const Number *x, bool new_x, Number &obj_value)
 Method to return the objective value.
virtual bool eval_grad_f (Index n, const Number *x, bool new_x, Number *grad_f)
 Method to return the gradient of the objective.
virtual bool eval_g (Index n, const Number *x, bool new_x, Index m, Number *g)
 Method to return the constraint residuals.
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 return: 1) The structure of the jacobian (if "values" is NULL) 2) The values of the jacobian (if "values" is not NULL)
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 return: 1) The structure of the hessian of the lagrangian (if "values" is NULL) 2) The values of the hessian of the lagrangian (if "values" is not NULL)
virtual bool get_nlp_info (Index &n, Index &m, Index &nnz_jac_g, Index &nnz_h_lag, IndexStyleEnum &index_style)
 Method to return some info about the nlp.
virtual bool get_bounds_info (Index n, Number *x_l, Number *x_u, Index m, Number *g_l, Number *g_u)
 Method to return the bounds for my problem.
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 return the starting point for the algorithm.
virtual bool eval_f (Index n, const Number *x, bool new_x, Number &obj_value)
 Method to return the objective value.
virtual bool eval_grad_f (Index n, const Number *x, bool new_x, Number *grad_f)
 Method to return the gradient of the objective.
virtual bool eval_g (Index n, const Number *x, bool new_x, Index m, Number *g)
 Method to return the constraint residuals.
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 return: 1) The structure of the jacobian (if "values" is NULL) 2) The values of the jacobian (if "values" is not NULL)
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 return: 1) The structure of the hessian of the lagrangian (if "values" is NULL) 2) The values of the hessian of the lagrangian (if "values" is not NULL)
Solution Methods
virtual void finalize_solution (SolverReturn status, Index n, const Number *x, const Number *z_L, const Number *z_U, Index m, const Number *g, const Number *lambda, Number obj_value, const IpoptData *ip_data, IpoptCalculatedQuantities *ip_cq)
 This method is called when the algorithm is complete so the TNLP can store/write the solution.
virtual void finalize_solution (SolverReturn status, Index n, const Number *x, const Number *z_L, const Number *z_U, Index m, const Number *g, const Number *lambda, Number obj_value, const IpoptData *ip_data, IpoptCalculatedQuantities *ip_cq)
 This method is called when the algorithm is complete so the TNLP can store/write the solution.
virtual void finalize_solution (SolverReturn status, Index n, const Number *x, const Number *z_L, const Number *z_U, Index m, const Number *g, const Number *lambda, Number obj_value, const IpoptData *ip_data, IpoptCalculatedQuantities *ip_cq)
 This method is called when the algorithm is complete so the TNLP can store/write the solution.

Private Member Functions

Methods to block default compiler methods.

The compiler automatically generates the following three methods.

Since the default compiler implementation is generally not what you want (for all but the most simple classes), we usually put the declarations of these methods in the private section and never implement them. This prevents the compiler from implementing an incorrect "default" behavior without us knowing. (See Scott Meyers book, "Effective C++")

 TutorialCpp_NLP ()
 TutorialCpp_NLP (const TutorialCpp_NLP &)
TutorialCpp_NLPoperator= (const TutorialCpp_NLP &)
 TutorialCpp_NLP ()
 TutorialCpp_NLP (const TutorialCpp_NLP &)
TutorialCpp_NLPoperator= (const TutorialCpp_NLP &)
 TutorialCpp_NLP ()
 TutorialCpp_NLP (const TutorialCpp_NLP &)
TutorialCpp_NLPoperator= (const TutorialCpp_NLP &)

Private Attributes

NLP data
Index N_
 Number of variables.
Numbera_
 Value of constants in constraints.

Detailed Description

Definition at line 34 of file TutorialCpp_nlp.hpp.


Constructor & Destructor Documentation

TutorialCpp_NLP::TutorialCpp_NLP ( Index  N,
const Number a 
)

constructor that takes in problem data

virtual TutorialCpp_NLP::~TutorialCpp_NLP ( ) [virtual]

default destructor

TutorialCpp_NLP::TutorialCpp_NLP ( ) [private]
TutorialCpp_NLP::TutorialCpp_NLP ( const TutorialCpp_NLP ) [private]
TutorialCpp_NLP::TutorialCpp_NLP ( Index  N,
const Number a 
)

constructor that takes in problem data

virtual TutorialCpp_NLP::~TutorialCpp_NLP ( ) [virtual]

default destructor

TutorialCpp_NLP::TutorialCpp_NLP ( ) [private]
TutorialCpp_NLP::TutorialCpp_NLP ( const TutorialCpp_NLP ) [private]
TutorialCpp_NLP::TutorialCpp_NLP ( Index  N,
const Number a 
)

constructor that takes in problem data

virtual TutorialCpp_NLP::~TutorialCpp_NLP ( ) [virtual]

default destructor

TutorialCpp_NLP::TutorialCpp_NLP ( ) [private]
TutorialCpp_NLP::TutorialCpp_NLP ( const TutorialCpp_NLP ) [private]

Member Function Documentation

virtual bool TutorialCpp_NLP::get_nlp_info ( Index n,
Index m,
Index nnz_jac_g,
Index nnz_h_lag,
IndexStyleEnum index_style 
) [virtual]

Method to return some info about the nlp.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::get_bounds_info ( Index  n,
Number x_l,
Number x_u,
Index  m,
Number g_l,
Number g_u 
) [virtual]

Method to return the bounds for my problem.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::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 return the starting point for the algorithm.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::eval_f ( Index  n,
const Number x,
bool  new_x,
Number obj_value 
) [virtual]

Method to return the objective value.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::eval_grad_f ( Index  n,
const Number x,
bool  new_x,
Number grad_f 
) [virtual]

Method to return the gradient of the objective.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::eval_g ( Index  n,
const Number x,
bool  new_x,
Index  m,
Number g 
) [virtual]

Method to return the constraint residuals.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::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 return: 1) The structure of the jacobian (if "values" is NULL) 2) The values of the jacobian (if "values" is not NULL)

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::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 return: 1) The structure of the hessian of the lagrangian (if "values" is NULL) 2) The values of the hessian of the lagrangian (if "values" is not NULL)

Reimplemented from Ipopt::TNLP.

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

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

Implements Ipopt::TNLP.

TutorialCpp_NLP& TutorialCpp_NLP::operator= ( const TutorialCpp_NLP ) [private]
virtual bool TutorialCpp_NLP::get_nlp_info ( Index n,
Index m,
Index nnz_jac_g,
Index nnz_h_lag,
IndexStyleEnum index_style 
) [virtual]

Method to return some info about the nlp.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::get_bounds_info ( Index  n,
Number x_l,
Number x_u,
Index  m,
Number g_l,
Number g_u 
) [virtual]

Method to return the bounds for my problem.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::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 return the starting point for the algorithm.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::eval_f ( Index  n,
const Number x,
bool  new_x,
Number obj_value 
) [virtual]

Method to return the objective value.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::eval_grad_f ( Index  n,
const Number x,
bool  new_x,
Number grad_f 
) [virtual]

Method to return the gradient of the objective.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::eval_g ( Index  n,
const Number x,
bool  new_x,
Index  m,
Number g 
) [virtual]

Method to return the constraint residuals.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::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 return: 1) The structure of the jacobian (if "values" is NULL) 2) The values of the jacobian (if "values" is not NULL)

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::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 return: 1) The structure of the hessian of the lagrangian (if "values" is NULL) 2) The values of the hessian of the lagrangian (if "values" is not NULL)

Reimplemented from Ipopt::TNLP.

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

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

Implements Ipopt::TNLP.

TutorialCpp_NLP& TutorialCpp_NLP::operator= ( const TutorialCpp_NLP ) [private]
virtual bool TutorialCpp_NLP::get_nlp_info ( Index n,
Index m,
Index nnz_jac_g,
Index nnz_h_lag,
IndexStyleEnum index_style 
) [virtual]

Method to return some info about the nlp.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::get_bounds_info ( Index  n,
Number x_l,
Number x_u,
Index  m,
Number g_l,
Number g_u 
) [virtual]

Method to return the bounds for my problem.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::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 return the starting point for the algorithm.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::eval_f ( Index  n,
const Number x,
bool  new_x,
Number obj_value 
) [virtual]

Method to return the objective value.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::eval_grad_f ( Index  n,
const Number x,
bool  new_x,
Number grad_f 
) [virtual]

Method to return the gradient of the objective.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::eval_g ( Index  n,
const Number x,
bool  new_x,
Index  m,
Number g 
) [virtual]

Method to return the constraint residuals.

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::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 return: 1) The structure of the jacobian (if "values" is NULL) 2) The values of the jacobian (if "values" is not NULL)

Implements Ipopt::TNLP.

virtual bool TutorialCpp_NLP::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 return: 1) The structure of the hessian of the lagrangian (if "values" is NULL) 2) The values of the hessian of the lagrangian (if "values" is not NULL)

Reimplemented from Ipopt::TNLP.

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

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

Implements Ipopt::TNLP.

TutorialCpp_NLP& TutorialCpp_NLP::operator= ( const TutorialCpp_NLP ) [private]

Member Data Documentation

Number of variables.

Definition at line 118 of file TutorialCpp_nlp.hpp.

Value of constants in constraints.

Definition at line 120 of file TutorialCpp_nlp.hpp.


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