7 #ifndef __IPORIGIPOPTNLP_HPP__ 8 #define __IPORIGIPOPTNLP_HPP__ 82 virtual bool Initialize(
85 const std::string& prefix
89 virtual bool InitializeStructures(
109 return nlp_->GetWarmStartIterate(warm_start_iterate);
288 virtual void GetSpaces(
307 virtual void AdjustVariableBounds(
315 virtual Index f_evals()
const 322 return grad_f_evals_;
347 void FinalizeSolution(
362 bool IntermediateCallBack(
370 Number regularization_size,
380 static void RegisterOptions(
522 Number bound_relax_factor,
CachedResults< SmartPtr< const Vector > > c_cache_
Equality constraint residuals.
SmartPtr< const Vector > orig_x_U_
Original unmodified upper bounds on x.
bool jac_d_constant_
Flag indicating if we need to ask for inequality constraint Jacobians only once.
Specialized CompoundVector class specifically for the algorithm iterates.
Class for all IPOPT specific calculated quantities.
CachedResults< SmartPtr< const Vector > > d_cache_
Inequality constraint residual (reformulated as equalities with slacks.
virtual SmartPtr< const Vector > d_L() const
Scaled lower bounds on d.
SmartPtr< const Matrix > Px_L_
Permutation matrix (x_L_ -> x)
bool jac_c_constant_
Flag indicating if we need to ask for equality constraint Jacobians only once.
virtual SmartPtr< const Vector > x_L() const
Scaled lower bounds on x.
SmartPtr< const MatrixSpace > jac_d_space_
virtual SmartPtr< const Vector > orig_x_L() const
Original unscaled lower bounds on x.
SmartPtr< const MatrixSpace > px_u_space_
virtual bool GetWarmStartIterate(IteratesVector &warm_start_iterate)
Method accessing the GetWarmStartIterate of the NLP.
AlgorithmMode
enum to indicate the mode in which the algorithm is
SmartPtr< const VectorSpace > d_l_space_
SmartPtr< const SymMatrixSpace > scaled_h_space_
SmartPtr< const VectorSpace > x_u_space_
SmartPtr< const MatrixSpace > scaled_jac_c_space_
bool initialized_
Flag indicating if initialization method has been called.
SmartPtr< const VectorSpace > x_l_space_
CachedResults< SmartPtr< const Vector > > unscaled_x_cache_
Unscaled version of x vector.
HessianApproximationType hessian_approximation_
Flag indicating what Hessian information is to be used.
ipindex Index
Type of all indices of vectors, matrices etc.
SmartPtr< const MatrixSpace > scaled_jac_d_space_
CachedResults< SmartPtr< const Matrix > > jac_d_cache_
Jacobian Matrix for inequality constraints (current iteration)
bool honor_original_bounds_
Flag indicating whether the primal variables should be projected back into original bounds are optimi...
This file contains a base class for all exceptions and a set of macros to help with exceptions...
virtual SmartPtr< const Matrix > Pd_L() const
Permutation matrix (d_L_ -> d)
bool grad_f_constant_
Flag indicating if we need to ask for objective Gradient only once.
This class collects all timing statistics for Ipopt.
Template class for Smart Pointers.
This class stores a list of user set options.
virtual SmartPtr< const Vector > orig_d_U() const
Original unscaled upper bounds on d.
SolverReturn
enum for the return from the optimize algorithm
SmartPtr< const Vector > x_L_
Lower bounds on x.
virtual SmartPtr< const Vector > x_U() const
Scaled upper bounds on x.
SmartPtr< const Vector > d_L_
Lower bounds on d.
SmartPtr< NLP > nlp_
Pointer to the NLP.
virtual SmartPtr< const SymMatrixSpace > HessianMatrixSpace() const
Accessor method to obtain the MatrixSpace for the Hessian matrix (or it's approximation) ...
HessianApproximationType
enumeration for the Hessian information type.
SmartPtr< const Matrix > Pd_L_
Permutation matrix (d_L_ -> d)
SmartPtr< const Journalist > jnlst_
Journalist.
SmartPtr< const Vector > d_U_
Upper bounds on d.
virtual Index h_evals() const
virtual SmartPtr< const Matrix > Px_L() const
Permutation matrix (x_L_ -> x)
HessianApproximationSpace
enumeration for the Hessian approximation space.
SmartPtr< const MatrixSpace > pd_u_space_
Class to organize all the data required by the algorithm.
SmartPtr< const SymMatrixSpace > h_space_
SmartPtr< NLP > nlp()
Accessor method to the underlying NLP.
virtual Index d_evals() const
ipnumber Number
Type of all numbers.
virtual SmartPtr< const Matrix > Px_U() const
Permutation matrix (x_U_ -> x)
SmartPtr< const VectorSpace > d_space_
CachedResults< SmartPtr< const Matrix > > jac_c_cache_
Jacobian Matrix for equality constraints (current iteration)
SmartPtr< const Matrix > Px_U_
Permutation matrix (x_U_ -> x)
virtual Index jac_d_evals() const
virtual SmartPtr< const Vector > d_U() const
Scaled upper bounds on d.
CachedResults< SmartPtr< const Vector > > grad_f_cache_
Gradient of the objective function.
CachedResults< Number > f_cache_
Objective function.
This class maps the traditional NLP into something that is more useful for Ipopt. ...
Class responsible for all message output.
SmartPtr< const Vector > orig_d_L_
Original unmodified lower bounds on d.
bool hessian_constant_
Flag indicating if we need to ask for Hessian only once.
SmartPtr< const Vector > orig_x_L_
Original unmodified lower bounds on x.
virtual Index grad_f_evals() const
virtual Index jac_c_evals() const
SmartPtr< const VectorSpace > d_u_space_
virtual SmartPtr< const Vector > orig_d_L() const
Original unscaled lower bounds on d.
bool check_derivatives_for_naninf_
Flag indicating whether it is desired to check if there are Nan or Inf entries in first and second de...
bool warm_start_same_structure_
Flag indicating whether the TNLP with identical structure has already been solved before...
CachedResults< SmartPtr< const SymMatrix > > h_cache_
Hessian of the lagrangian (current iteration)
SmartPtr< const MatrixSpace > px_l_space_
Number constr_viol_tol_
constraint violation tolerance (from OptimalityErrorConvergenceCheck)
SmartPtr< const Vector > orig_d_U_
Original unmodified upper bounds on d.
virtual SmartPtr< const Vector > orig_x_U() const
Original unscaled upper bounds on x.
virtual SmartPtr< const Matrix > Pd_U() const
Permutation matrix (d_U_ -> d)
SmartPtr< const MatrixSpace > pd_l_space_
SmartPtr< const MatrixSpace > jac_c_space_
virtual Index c_evals() const
This is the abstract base class for classes that map the traditional NLP into something that is more ...
Number bound_relax_factor_
relaxation factor for the bounds
SmartPtr< const VectorSpace > c_space_
virtual SmartPtr< const VectorSpace > x_space() const
x_space
HessianApproximationSpace hessian_approximation_space_
Flag indicating in which space Hessian is to be approximated.
SmartPtr< const Vector > x_U_
Upper bounds on x.
SmartPtr< const Matrix > Pd_U_
Permutation matrix (d_U_ -> d)