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23 class IpoptCalculatedQuantities;
240 "You have set options for user provided scaling, but have not implemented GetScalingParameters in the NLP interface");
#define THROW_EXCEPTION(__except_type, __msg)
Class to organize all the data required by the algorithm.
virtual bool ProcessOptions(const OptionsList &, const std::string &)
Overload if you want the chance to process options or parameters that may be specific to the NLP.
virtual void GetScalingParameters(const SmartPtr< const VectorSpace >, const SmartPtr< const VectorSpace >, const SmartPtr< const VectorSpace >, Number &, SmartPtr< Vector > &, SmartPtr< Vector > &, SmartPtr< Vector > &) const
Routines to get the scaling parameters.
virtual bool Eval_f(const Vector &x, Number &f)=0
void operator=(const NLP &)
Default Assignment Operator.
DECLARE_STD_EXCEPTION(INVALID_NLP)
Class for all IPOPT specific calculated quantities.
This file contains a base class for all exceptions and a set of macros to help with exceptions.
double Number
Type of all numbers.
virtual bool Eval_h(const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd, SymMatrix &h)=0
virtual bool Eval_grad_f(const Vector &x, Vector &g_f)=0
int Index
Type of all indices of vectors, matrices etc.
virtual bool GetWarmStartIterate(IteratesVector &)
Method for obtaining an entire iterate as a warmstart point.
virtual bool Eval_d(const Vector &x, Vector &d)=0
virtual bool Eval_jac_c(const Vector &x, Matrix &jac_c)=0
Template class for Smart Pointers.
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 void FinalizeSolution(SolverReturn, const Vector &, const Vector &, const Vector &, const Vector &, const Vector &, const Vector &, const Vector &, Number, const IpoptData *, IpoptCalculatedQuantities *)
This method is called at the very end of the optimization.
NLP(const NLP &)
Copy Constructor.
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.
NLP()
Default constructor.
This is the base class for all derived symmetric matrix types.
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 bool Eval_jac_d(const Vector &x, Matrix &jac_d)=0
DECLARE_STD_EXCEPTION(USER_SCALING_NOT_IMPLEMENTED)
Exceptions.
SolverReturn
enum for the return from the optimize algorithm
This class stores a list of user set options.
virtual bool IntermediateCallBack(AlgorithmMode, Index, Number, Number, Number, Number, Number, Number, Number, Number, Index, const IpoptData *, IpoptCalculatedQuantities *)
This method is called once per iteration, after the iteration summary output has been printed.
Storing the reference count of all the smart pointers that currently reference it.
virtual ~NLP()
Default destructor.
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 Eval_c(const Vector &x, Vector &c)=0
AlgorithmMode
enum to indicate the mode in which the algorithm is
Specialized CompoundVector class specifically for the algorithm iterates.