Bonmin
1.7
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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. |
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.
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.
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
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] |
void Bonmin::TNLP2FPNLP::setLambda | ( | double | lambda | ) | [inline] |
void Bonmin::TNLP2FPNLP::setNorm | ( | int | norm | ) | [inline] |
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] |
This method is called when the algorithm is complete so the TNLP can store/write the solution.
virtual bool Bonmin::TNLP2FPNLP::get_variables_linearity | ( | Ipopt::Index | n, |
LinearityType * | var_types | ||
) | [inline, virtual] |
Definition at line 158 of file BonTNLP2FPNLP.hpp.
References tnlp_.
virtual bool Bonmin::TNLP2FPNLP::get_constraints_linearity | ( | Ipopt::Index | m, |
LinearityType * | const_types | ||
) | [inline, virtual] |
overload this method to 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 166 of file BonTNLP2FPNLP.hpp.
References tnlp_, use_cutoff_constraint_, and use_local_branching_constraint_.
void Bonmin::TNLP2FPNLP::setObjectiveScaling | ( | double | value | ) | [inline] |
Definition at line 181 of file BonTNLP2FPNLP.hpp.
References objectiveScalingFactor_.
double Bonmin::TNLP2FPNLP::getObjectiveScaling | ( | ) | const [inline] |
Definition at line 185 of file BonTNLP2FPNLP.hpp.
References objectiveScalingFactor_.
double Bonmin::TNLP2FPNLP::dist_to_point | ( | const Ipopt::Number * | x | ) | [private] |
Compute the norm-2 distance to the current point to which distance is minimized.
void Bonmin::TNLP2FPNLP::operator= | ( | const TNLP2FPNLP & | ) | [private] |
Overloaded Equals Operator.
Ipopt::SmartPtr<TNLP> Bonmin::TNLP2FPNLP::tnlp_ [private] |
pointer to the tminlp that is being adapted
Definition at line 215 of file BonTNLP2FPNLP.hpp.
Referenced by get_constraints_linearity(), get_starting_point(), get_variables_linearity(), and use().
vector<Ipopt::Index> Bonmin::TNLP2FPNLP::inds_ [private] |
Indices of the variables for which distance is minimized (i.e. indices of integer variables in a feasibility pump setting)
Definition at line 220 of file BonTNLP2FPNLP.hpp.
vector<Ipopt::Number> Bonmin::TNLP2FPNLP::vals_ [private] |
Values of the point to which we separate (if x is the point vals_[i] should be x[inds_[i]] )
Definition at line 222 of file BonTNLP2FPNLP.hpp.
double Bonmin::TNLP2FPNLP::lambda_ [private] |
value for the convex combination to take between original objective and distance function.
( take lambda_ * distance + (1-lambda) sigma f(x).
Definition at line 225 of file BonTNLP2FPNLP.hpp.
Referenced by setLambda().
double Bonmin::TNLP2FPNLP::sigma_ [private] |
Scaling for the original objective.
Definition at line 227 of file BonTNLP2FPNLP.hpp.
Referenced by setSigma().
int Bonmin::TNLP2FPNLP::norm_ [private] |
Norm to use (L_1 or L_2).
Definition at line 229 of file BonTNLP2FPNLP.hpp.
Referenced by setNorm().
double Bonmin::TNLP2FPNLP::objectiveScalingFactor_ [private] |
Scaling factor for the objective.
Definition at line 233 of file BonTNLP2FPNLP.hpp.
Referenced by getObjectiveScaling(), and setObjectiveScaling().
bool Bonmin::TNLP2FPNLP::use_feasibility_pump_objective_ [private] |
Flag to indicate that we want to use the feasibility pump objective.
Definition at line 238 of file BonTNLP2FPNLP.hpp.
Referenced by set_use_feasibility_pump_objective().
bool Bonmin::TNLP2FPNLP::use_cutoff_constraint_ [private] |
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 242 of file BonTNLP2FPNLP.hpp.
Referenced by get_constraints_linearity(), get_starting_point(), and set_use_cutoff_constraint().
bool Bonmin::TNLP2FPNLP::use_local_branching_constraint_ [private] |
Flag to indicate that we want to use a local branching constraint.
Definition at line 245 of file BonTNLP2FPNLP.hpp.
Referenced by get_constraints_linearity(), get_starting_point(), and set_use_local_branching_constraint().
double Bonmin::TNLP2FPNLP::cutoff_ [private] |
Value of best solution known.
Definition at line 251 of file BonTNLP2FPNLP.hpp.
double Bonmin::TNLP2FPNLP::rhs_local_branching_constraint_ [private] |
RHS of local branching constraint.
Definition at line 254 of file BonTNLP2FPNLP.hpp.
Referenced by set_rhs_local_branching_constraint().
Ipopt::TNLP::IndexStyleEnum Bonmin::TNLP2FPNLP::index_style_ [private] |
Ipopt::Index style (C++ or Fortran)
Definition at line 258 of file BonTNLP2FPNLP.hpp.