NLPInterfacePack_ExampleNLPObjGrad.hpp

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#ifndef EXAMPLE_NLP_OBJ_GRADIENT_H
#define EXAMPLE_NLP_OBJ_GRADIENT_H

#include "NLPInterfacePack_NLPObjGrad.hpp"
#include "AbstractLinAlgPack_VectorMutable.hpp"
#include "AbstractLinAlgPack_VectorSpace.hpp"
#include "AbstractLinAlgPack_VectorSpaceBlocked.hpp"
#include "Teuchos_Assert.hpp"

namespace NLPInterfacePack {

class ExampleNLPObjGrad : virtual public NLPObjGrad {
public:

  ExampleNLPObjGrad(
    const VectorSpace::space_ptr_t&  vec_space
    ,value_type                      xo
    ,bool                            has_bounds
    ,bool                            dep_bounded
    );


  virtual Range1D var_dep() const;
  virtual Range1D var_indep() const;



  void initialize(bool test_setup);
  bool is_initialized() const;
  size_type n() const;
  size_type m() const;
  vec_space_ptr_t space_x() const;
  vec_space_ptr_t space_c() const;
    size_type num_bounded_x() const;
  void force_xinit_in_bounds(bool force_xinit_in_bounds);
  bool force_xinit_in_bounds() const;
  const Vector& xinit() const;
  const Vector& xl() const;
  const Vector& xu() const;
  value_type max_var_bounds_viol() const;
  void scale_f( value_type scale_f );
  value_type scale_f() const;
  void report_final_solution(
    const Vector&    x
    ,const Vector*   lambda
    ,const Vector*   nu
    ,bool            optimal
    );


protected:


  void imp_calc_f(
    const Vector& x, bool newx
    ,const ZeroOrderInfo& zero_order_info) const;
  void imp_calc_c(
    const Vector& x, bool newx
    ,const ZeroOrderInfo& zero_order_info) const;
  void imp_calc_h(const Vector& x, bool newx, const ZeroOrderInfo& zero_order_info) const;



  void imp_calc_Gf(
    const Vector& x, bool newx
    ,const ObjGradInfo& obj_grad_info) const;


private:

  // /////////////////////////////////////////
  // Private data members

  VectorSpace::space_ptr_t    vec_space_;       // The vector space for dependent and indepenent variables and c(x).
  VectorSpace::space_ptr_t    vec_space_comp_;  // Composite vector space for x = [ xD; xI ]
  Range1D                     var_dep_;         // Range for dependnet variables.
  Range1D                     var_indep_;       // Range for independent variables.

  bool         initialized_;            // flag for if initialized has been called.
  value_type   obj_scale_;              // default = 1.0;
  bool         has_bounds_;             // default = true
  bool         force_xinit_in_bounds_;  // default = true.

  size_type    n_;                      // Number of variables in the problem.
  VectorSpace::vec_mut_ptr_t  xinit_;   // Initial guess.
  VectorSpace::vec_mut_ptr_t  xl_;      // lower bounds.
  VectorSpace::vec_mut_ptr_t  xu_;      // upper bounds.

  // /////////////////////////////////////////
  // Private member functions

  void assert_is_initialized() const;

};  // end class ExampleNLPObjGrad

// ///////////////////////////////////////////////
// Inline member functions

inline
void ExampleNLPObjGrad::assert_is_initialized() const
{
  typedef NLPInterfacePack::NLP NLP;
  TEUCHOS_TEST_FOR_EXCEPTION(
    !is_initialized(), NLP::UnInitialized
    ,"ExampleNLPObjGrad::assert_is_initialized() : Error, "
    "ExampleNLPObjGrad::initialize() has not been called yet." );
}

} // end namespace NLPInterfacePack

#endif  // EXAMPLE_NLP_OBJ_GRADIENT_H