25 class IpoptCalculatedQuantities;
57 const std::string& prefix)
174 Number regularization_size,
198 "You have set options for user provided scaling, but have"
199 " not implemented GetScalingParameters in the NLP interface");
Number * x
Input: Starting point Output: Optimal solution.
virtual bool Eval_c(const Vector &x, Vector &c)=0
Specialized CompoundVector class specifically for the algorithm iterates.
Class for all IPOPT specific calculated quantities.
virtual bool Eval_h(const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd, SymMatrix &h)=0
void operator=(const NLP &)
Overloaded Equals Operator.
virtual bool GetWarmStartIterate(IteratesVector &warm_start_iterate)
Method for obtaining an entire iterate as a warmstart point.
AlgorithmMode
enum to indicate the mode in which the algorithm is
NLP()
Default constructor.
virtual bool Eval_jac_d(const Vector &x, Matrix &jac_d)=0
double Number
Type of all numbers.
DECLARE_STD_EXCEPTION(USER_SCALING_NOT_IMPLEMENTED)
Exceptions.
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)=0
Method for obtaining the bounds information.
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...
virtual ~NLP()
Default destructor.
This is the base class for all derived symmetric matrix types.
Template class for Smart Pointers.
This class stores a list of user set options.
SolverReturn
enum for the return from the optimize algorithm (obviously we need to add more)
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 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.
Class to organize all the data required by the algorithm.
virtual bool Eval_f(const Vector &x, Number &f)=0
Number * x_L
Lower bounds on variables.
int Index
Type of all indices of vectors, matrices etc.
Number Number * x_U
Upper bounds on variables.
Number Number * x_scaling
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)=0
Method for creating the derived vector / matrix types.
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 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)=0
Method for obtaining the starting point for all the iterates.
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...
virtual bool Eval_jac_c(const Vector &x, Matrix &jac_c)=0
virtual bool Eval_grad_f(const Vector &x, Vector &g_f)=0
virtual bool Eval_d(const Vector &x, Vector &d)=0
#define THROW_EXCEPTION(__except_type, __msg)