9 #ifndef __IPTNLPREDUCER_HPP__
10 #define __IPTNLPREDUCER_HPP__
48 bool& use_x_scaling,
Index n,
50 bool& use_g_scaling,
Index m,
93 Number regularization_size,
102 Index* pos_nonlin_vars);
AlgorithmMode
enum to indicate the mode in which the algorithm is
Number * x
Input: Starting point Output: Optimal solution.
Number Number Index Number Number Index Index nele_hess
Number of non-zero elements in Hessian of Lagrangian.
Number Number * g
Values of constraint at final point (output only - ignored if set to NULL)
Number Number Index Number Number Index nele_jac
Number of non-zero elements in constraint Jacobian.
Number Number * x_scaling
Number Number Number * g_scaling
Number Number Index m
Number of constraints.
Number Number Index Number Number Index Index Index index_style
indexing style for iRow & jCol, 0 for C style, 1 for Fortran style
Class for all IPOPT specific calculated quantities.
Class to organize all the data required by the algorithm.
Specialized CompoundVector class specifically for the algorithm iterates.
Template class for Smart Pointers.
This is a wrapper around a given TNLP class that takes out a list of constraints that are given to th...
Index n_x_fix_
Number of variables that are to be fixed to initial value.
virtual bool get_warm_start_iterate(IteratesVector &warm_start_iterate)
overload this method to provide an Ipopt iterate (already in the form Ipopt requires it internally) f...
TNLPReducer()
Default Constructor.
void operator=(const TNLPReducer &)
Overloaded Equals Operator.
virtual bool get_list_of_nonlinear_variables(Index num_nonlin_vars, Index *pos_nonlin_vars)
Index * index_g_skip_
Array of indices of the constraints that are to be skipped.
Index n_g_skip_
Number of constraints to be skipped.
virtual bool get_starting_point(Index n, bool init_x, Number *x, bool init_z, Number *z_L, Number *z_U, Index m, bool init_lambda, Number *lambda)
overload this method to return the starting point.
Index nnz_jac_g_reduced_
Number of Jacobian nonzeros in the reduced NLP.
virtual Index get_number_of_nonlinear_variables()
virtual bool eval_f(Index n, const Number *x, bool new_x, Number &obj_value)
overload this method to return the value of the objective function
virtual bool get_constraints_linearity(Index m, LinearityType *const_types)
overload this method to return the constraint linearity.
virtual bool eval_g(Index n, const Number *x, bool new_x, Index m, Number *g)
overload this method to return the vector of constraint values
IndexStyleEnum index_style_orig_
Index style for original problem.
Index * index_xL_skip_
Array of indices of the lower variable bounds to be skipped.
virtual bool eval_jac_g(Index n, const Number *x, bool new_x, Index m, Index nele_jac, Index *iRow, Index *jCol, Number *values)
overload this method to return the jacobian of the constraints.
Index * index_xU_skip_
Array of indices of the upper variable bounds to be skipped.
Index * index_x_fix_
Array of indices of the variables that are to be fixed.
virtual void finalize_solution(SolverReturn status, Index n, const Number *x, const Number *z_L, const Number *z_U, Index m, const Number *g, const Number *lambda, Number obj_value, const IpoptData *ip_data, IpoptCalculatedQuantities *ip_cq)
This method is called when the algorithm is complete so the TNLP can store/write the solution.
virtual bool get_scaling_parameters(Number &obj_scaling, bool &use_x_scaling, Index n, Number *x_scaling, bool &use_g_scaling, Index m, Number *g_scaling)
overload this method to return scaling parameters.
Index n_xL_skip_
Number of lower variable bounds to be skipped.
virtual bool eval_h(Index n, const Number *x, bool new_x, Number obj_factor, Index m, const Number *lambda, bool new_lambda, Index nele_hess, Index *iRow, Index *jCol, Number *values)
overload this method to return the hessian of the lagrangian.
virtual ~TNLPReducer()
Default destructor.
virtual bool eval_grad_f(Index n, const Number *x, bool new_x, Number *grad_f)
overload this method to return the vector of the gradient of the objective w.r.t.
TNLPReducer(const TNLPReducer &)
Copy Constructor.
Index m_reduced_
Number of constraints in reduced NLP.
virtual bool intermediate_callback(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)
Intermediate Callback method for the user.
virtual bool get_variables_linearity(Index n, LinearityType *var_types)
overload this method to return the variables linearity (TNLP::LINEAR or TNLP::NON_LINEAR).
Index nnz_jac_g_skipped_
Number of Jacobian nonzeros that are skipped.
TNLPReducer(TNLP &tnlp, Index n_g_skip, const Index *index_g_skip, Index n_xL_skip, const Index *index_xL_skip, Index n_xU_skip, const Index *index_xU_skip, Index n_x_fix, const Index *index_f_fix)
Constructor is given the indices of the constraints that should be taken out of the problem statement...
virtual bool get_bounds_info(Index n, Number *x_l, Number *x_u, Index m, Number *g_l, Number *g_u)
overload this method to return the information about the bound on the variables and constraints.
Index * g_keep_map_
Map from original constraints to new constraints.
virtual bool get_nlp_info(Index &n, Index &m, Index &nnz_jac_g, Index &nnz_h_lag, IndexStyleEnum &index_style)
Index n_xU_skip_
Number of upper variable bounds to be skipped.
Index * jac_g_skipped_
Array of Jacobian elements that are to be skipped.
Base class for all NLP's that use standard triplet matrix form and dense vectors.
LinearityType
Type of the constraints.
IndexStyleEnum
overload this method to return the number of variables and constraints, and the number of non-zeros i...
SolverReturn
enum for the return from the optimize algorithm (obviously we need to add more)
int Index
Type of all indices of vectors, matrices etc.
double Number
Type of all numbers.