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IpNLPBoundsRemover.hpp
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1 // Copyright (C) 2008, 2010 International Business Machines and others.
2 // All Rights Reserved.
3 // This code is published under the Eclipse Public License.
4 //
5 // $Id: IpNLP.hpp 949 2007-03-27 00:41:26Z andreasw $
6 //
7 // Authors: Andreas Waechter IBM 2008-08-25
8 
9 #ifndef __IPNLPBOUNDSREMOVER_HPP__
10 #define __IPNLPBOUNDSREMOVER_HPP__
11 
12 #include "IpNLP.hpp"
13 
14 namespace Ipopt
15 {
24  class NLPBoundsRemover : public NLP
25  {
26  public:
31  NLPBoundsRemover(NLP& nlp, bool allow_twosided_inequalities = false);
32 
35  {}
37 
43  virtual bool ProcessOptions(const OptionsList& options,
44  const std::string& prefix)
45  {
46  return nlp_->ProcessOptions(options, prefix);
47  }
48 
52  virtual bool GetSpaces(SmartPtr<const VectorSpace>& x_space,
55  SmartPtr<const VectorSpace>& x_l_space,
56  SmartPtr<const MatrixSpace>& px_l_space,
57  SmartPtr<const VectorSpace>& x_u_space,
58  SmartPtr<const MatrixSpace>& px_u_space,
59  SmartPtr<const VectorSpace>& d_l_space,
60  SmartPtr<const MatrixSpace>& pd_l_space,
61  SmartPtr<const VectorSpace>& d_u_space,
62  SmartPtr<const MatrixSpace>& pd_u_space,
63  SmartPtr<const MatrixSpace>& Jac_c_space,
64  SmartPtr<const MatrixSpace>& Jac_d_space,
65  SmartPtr<const SymMatrixSpace>& Hess_lagrangian_space);
66 
68  virtual bool GetBoundsInformation(const Matrix& Px_L,
69  Vector& x_L,
70  const Matrix& Px_U,
71  Vector& x_U,
72  const Matrix& Pd_L,
73  Vector& d_L,
74  const Matrix& Pd_U,
75  Vector& d_U);
76 
80  virtual bool GetStartingPoint(SmartPtr<Vector> x,
81  bool need_x,
82  SmartPtr<Vector> y_c,
83  bool need_y_c,
84  SmartPtr<Vector> y_d,
85  bool need_y_d,
86  SmartPtr<Vector> z_L,
87  bool need_z_L,
88  SmartPtr<Vector> z_U,
89  bool need_z_U);
90 
94  virtual bool GetWarmStartIterate(IteratesVector& warm_start_iterate)
95  {
96  return nlp_->GetWarmStartIterate(warm_start_iterate);
97  }
99 
103  virtual bool Eval_f(const Vector& x, Number& f)
104  {
105  return nlp_->Eval_f(x, f);
106  }
107 
108  virtual bool Eval_grad_f(const Vector& x, Vector& g_f)
109  {
110  return nlp_->Eval_grad_f(x, g_f);
111  }
112 
113  virtual bool Eval_c(const Vector& x, Vector& c)
114  {
115  return nlp_->Eval_c(x, c);
116  }
117 
118  virtual bool Eval_jac_c(const Vector& x, Matrix& jac_c)
119  {
120  return nlp_->Eval_jac_c(x, jac_c);
121  }
122 
123  virtual bool Eval_d(const Vector& x, Vector& d);
124 
125  virtual bool Eval_jac_d(const Vector& x, Matrix& jac_d);
126 
127  virtual bool Eval_h(const Vector& x,
128  Number obj_factor,
129  const Vector& yc,
130  const Vector& yd,
131  SymMatrix& h);
133 
142  virtual void FinalizeSolution(SolverReturn status,
143  const Vector& x, const Vector& z_L,
144  const Vector& z_U,
145  const Vector& c, const Vector& d,
146  const Vector& y_c, const Vector& y_d,
147  Number obj_value,
148  const IpoptData* ip_data,
150 
167  Index iter, Number obj_value,
168  Number inf_pr, Number inf_du,
169  Number mu, Number d_norm,
170  Number regularization_size,
171  Number alpha_du, Number alpha_pr,
172  Index ls_trials,
173  const IpoptData* ip_data,
175  {
176  return nlp_->IntermediateCallBack(mode,iter, obj_value, inf_pr, inf_du,
177  mu, d_norm, regularization_size,
178  alpha_du, alpha_pr, ls_trials,
179  ip_data, ip_cq);
180  }
182 
187  virtual void GetScalingParameters(
188  const SmartPtr<const VectorSpace> x_space,
189  const SmartPtr<const VectorSpace> c_space,
190  const SmartPtr<const VectorSpace> d_space,
193  SmartPtr<Vector>& c_scaling,
194  SmartPtr<Vector>& d_scaling) const;
196 
210  virtual void
212  SmartPtr<Matrix>& P_approx)
213  {
214  nlp_->GetQuasiNewtonApproximationSpaces(approx_space, P_approx);
215  }
216 
219  {
220  return nlp_;
221  }
222 
223  private:
236 
238  void operator=(const NLPBoundsRemover&);
240 
243 
246 
249 
252 
256  };
257 
258 } // namespace Ipopt
259 
260 #endif
Number * x
Input: Starting point Output: Optimal solution.
virtual ~NLPBoundsRemover()
Default destructor.
Specialized CompoundVector class specifically for the algorithm iterates.
Class for all IPOPT specific calculated quantities.
SmartPtr< NLP > nlp()
Accessor method to the original NLP.
virtual bool IntermediateCallBack(AlgorithmMode mode, Index iter, Number obj_value, Number inf_pr, Number inf_du, Number mu, Number d_norm, Number regularization_size, Number alpha_du, Number alpha_pr, Index ls_trials, const IpoptData *ip_data, IpoptCalculatedQuantities *ip_cq)
This method is called once per iteration, after the iteration summary output has been printed...
AlgorithmMode
enum to indicate the mode in which the algorithm is
double Number
Type of all numbers.
Definition: IpTypes.hpp:17
Vector Base Class.
Definition: IpVector.hpp:47
virtual bool Eval_jac_c(const Vector &x, Matrix &jac_c)
virtual void GetQuasiNewtonApproximationSpaces(SmartPtr< VectorSpace > &approx_space, SmartPtr< Matrix > &P_approx)
Method for obtaining the subspace in which the limited-memory Hessian approximation should be done...
bool allow_twosided_inequalities_
Flag indicating whether twosided inequality constraints are allowed.
virtual bool Eval_jac_d(const Vector &x, Matrix &jac_d)
SmartPtr< const Matrix > Px_u_orig_
Pointer to the expansion matrix for the upper x bounds.
virtual bool Eval_h(const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd, SymMatrix &h)
virtual bool Eval_f(const Vector &x, Number &f)
This is the base class for all derived symmetric matrix types.
Definition: IpSymMatrix.hpp:23
Template class for Smart Pointers.
Definition: IpSmartPtr.hpp:172
This class stores a list of user set options.
SolverReturn
enum for the return from the optimize algorithm (obviously we need to add more)
Definition: IpAlgTypes.hpp:22
virtual void GetScalingParameters(const SmartPtr< const VectorSpace > x_space, const SmartPtr< const VectorSpace > c_space, const SmartPtr< const VectorSpace > d_space, Number &obj_scaling, SmartPtr< Vector > &x_scaling, SmartPtr< Vector > &c_scaling, SmartPtr< Vector > &d_scaling) const
Routines to get the scaling parameters.
virtual bool GetSpaces(SmartPtr< const VectorSpace > &x_space, SmartPtr< const VectorSpace > &c_space, SmartPtr< const VectorSpace > &d_space, SmartPtr< const VectorSpace > &x_l_space, SmartPtr< const MatrixSpace > &px_l_space, SmartPtr< const VectorSpace > &x_u_space, SmartPtr< const MatrixSpace > &px_u_space, SmartPtr< const VectorSpace > &d_l_space, SmartPtr< const MatrixSpace > &pd_l_space, SmartPtr< const VectorSpace > &d_u_space, SmartPtr< const MatrixSpace > &pd_u_space, SmartPtr< const MatrixSpace > &Jac_c_space, SmartPtr< const MatrixSpace > &Jac_d_space, SmartPtr< const SymMatrixSpace > &Hess_lagrangian_space)
Method for creating the derived vector / matrix types.
virtual bool Eval_grad_f(const Vector &x, Vector &g_f)
Matrix Base Class.
Definition: IpMatrix.hpp:27
Class to organize all the data required by the algorithm.
Definition: IpIpoptData.hpp:83
NLPBoundsRemover()
Default Constructor.
Number * x_L
Lower bounds on variables.
int Index
Type of all indices of vectors, matrices etc.
Definition: IpTypes.hpp:19
SmartPtr< const VectorSpace > d_space_orig_
Pointer to the original d space.
virtual bool Eval_c(const Vector &x, Vector &c)
Number Number * x_U
Upper bounds on variables.
Number Number * x_scaling
virtual bool GetStartingPoint(SmartPtr< Vector > x, bool need_x, SmartPtr< Vector > y_c, bool need_y_c, SmartPtr< Vector > y_d, bool need_y_d, SmartPtr< Vector > z_L, bool need_z_L, SmartPtr< Vector > z_U, bool need_z_U)
Method for obtaining the starting point for all the iterates.
This is an adaper for an NLP that converts variable bound constraints to inequality constraints...
virtual bool GetBoundsInformation(const Matrix &Px_L, Vector &x_L, const Matrix &Px_U, Vector &x_U, const Matrix &Pd_L, Vector &d_L, const Matrix &Pd_U, Vector &d_U)
Method for obtaining the bounds information.
virtual bool GetWarmStartIterate(IteratesVector &warm_start_iterate)
Method for obtaining an entire iterate as a warmstart point.
Brief Class Description.
Definition: IpNLP.hpp:31
virtual bool ProcessOptions(const OptionsList &options, const std::string &prefix)
Overload if you want the chance to process options or parameters that may be specific to the NLP...
virtual bool Eval_d(const Vector &x, Vector &d)
void operator=(const NLPBoundsRemover &)
Overloaded Equals Operator.
virtual void FinalizeSolution(SolverReturn status, const Vector &x, const Vector &z_L, const Vector &z_U, const Vector &c, const Vector &d, const Vector &y_c, const Vector &y_d, Number obj_value, const IpoptData *ip_data, IpoptCalculatedQuantities *ip_cq)
This method is called at the very end of the optimization.
Number obj_scaling
SmartPtr< const Matrix > Px_l_orig_
Pointer to the expansion matrix for the lower x bounds.
SmartPtr< NLP > nlp_
Pointer to the original NLP.