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// (C) Copyright International Business Machines Corporation 2007
// All Rights Reserved.
// This code is published under the Eclipse Public License.
//
// Authors :
// Pierre Bonami, International Business Machines Corporation
//
// Date : 10/06/2007
#ifndef __TMINLPQuad_HPP__
#define __TMINLPQuad_HPP__
#include "BonTMINLP2TNLP.hpp"
#include "BonQuadRow.hpp"
namespace Bonmin
{
/** This is a derived class fro TMINLP2TNLP to handle adding quadratic cuts.
*/
class TMINLP2TNLPQuadCuts : public Bonmin::TMINLP2TNLP
{
public:
/**@name Constructors/Destructors */
//@{
TMINLP2TNLPQuadCuts(const Ipopt::SmartPtr<Bonmin::TMINLP> tminlp
#ifdef WARM_STARTER
,
const OptionsList& options
#endif
);
/** Copy Constructor
* \warning source and copy point to the same tminlp_.
*/
TMINLP2TNLPQuadCuts(const TMINLP2TNLPQuadCuts&);
/** Virtual copy.*/
virtual Bonmin::TMINLP2TNLP * clone() const{
printf("Cloning TMINLP2TNLPQuadCuts.\n");
return new TMINLP2TNLPQuadCuts(*this);}
/** Destructor */
virtual ~TMINLP2TNLPQuadCuts();
//@}
/**@name methods to gather information about the NLP */
//@{
/** This call is just passed onto parent class and add number of quadratic
cuts*/
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);
/** This call is just passed onto parent class and add bounds of quadratic
cuts*/
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);
virtual bool get_constraints_linearity(Ipopt::Index m, Ipopt::TNLP::LinearityType* const_types);
/** This call is just passed onto parent class and add
lambda for quadratic cuts*/
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);
/** Method that returns scaling parameters (passed to parent all quadratic
not scaled).
*/
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);
/** Returns the value of the objective function in x*/
virtual bool eval_f(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
Ipopt::Number& obj_value);
/** Returns the vector of the gradient of
* the objective w.r.t. x */
virtual bool eval_grad_f(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
Ipopt::Number* grad_f);
/** Returns the vector of constraint values in x (appends constraint values for quadratics).*/
virtual bool eval_g(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
Ipopt::Index m, Ipopt::Number* g);
/** Returns 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. */
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);
/** compute the value of a single constraint */
virtual bool eval_gi(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
Ipopt::Index i, Ipopt::Number& gi);
/** compute the structure or values of the gradient for one
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);
/** 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 */
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);
//@}
/** \name Cuts management. */
//@{
/** Add some linear or quadratic cuts to the problem formulation
if some of the OsiRowCuts are quadratic they will be well understood as long as safe is true.*/
void addCuts(const Cuts& cuts, bool safe);
/** Add some cuts to the problem formulaiton (handles Quadratics).*/
void addCuts(const OsiCuts &cuts);
/** Add some linear cuts to the problem formulation.*/
virtual void addCuts(unsigned int numberCuts, const OsiRowCut ** cuts);
/** Remove some cuts from the formulation */
void removeCuts(unsigned int number ,const int * toRemove);
//@}
//
/** Change objective to a linear one whith given objective function.*/
void set_linear_objective(int n_var, const double * obj, double c_0);
/** Reset objective to original one */
void reset_objective(){
obj_.clear();
}
protected:
/** Add some cuts to the problem formulaiton (handles Quadratics).*/
void addRowCuts(const OsiCuts &cuts, bool safe);
/**@name 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. */
//@{
/** Default Constructor */
TMINLP2TNLPQuadCuts();
/** Overloaded Equals Operator */
TMINLP2TNLPQuadCuts& operator=(const TMINLP2TNLP&);
//@}
private:
/** Some storage for quadratic cuts.*/
vector<QuadRow *> quadRows_;
/** Storage for the original hessian of the problem.*/
AdjustableMat H_;
/** print H_ for debug.*/
void printH();
/** Current umber of entries in the jacobian.*/
int curr_nnz_jac_;
/** Store user passed linear objective.*/
vector<double> obj_;
/** constant term in objective function.*/
double c_;
};
} // namespace Ipopt
#endif
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