This class maps the traditional NLP into something that is more useful for Ipopt. More...
#include <IpOrigIpoptNLP.hpp>
Public Member Functions | |
virtual bool | Initialize (const Journalist &jnlst, const OptionsList &options, const std::string &prefix) |
Initialization method. | |
virtual bool | InitializeStructures (SmartPtr< Vector > &x, bool init_x, SmartPtr< Vector > &y_c, bool init_y_c, SmartPtr< Vector > &y_d, bool init_y_d, SmartPtr< Vector > &z_L, bool init_z_L, SmartPtr< Vector > &z_U, bool init_z_U, SmartPtr< Vector > &v_L, SmartPtr< Vector > &v_U) |
Initialize (create) structures for the iteration data. | |
virtual bool | GetWarmStartIterate (IteratesVector &warm_start_iterate) |
Method accessing the GetWarmStartIterate of the NLP. | |
virtual void | 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) |
Accessor method for vector/matrix spaces pointers. | |
virtual void | AdjustVariableBounds (const Vector &new_x_L, const Vector &new_x_U, const Vector &new_d_L, const Vector &new_d_U) |
Method for adapting the variable bounds. | |
SmartPtr< NLP > | nlp () |
Accessor method to the underlying NLP. | |
Constructors / Destructor | |
OrigIpoptNLP (const SmartPtr< const Journalist > &jnlst, const SmartPtr< NLP > &nlp, const SmartPtr< NLPScalingObject > &nlp_scaling, TimingStatistics &timing_statistics) | |
Constructor. | |
virtual | ~OrigIpoptNLP () |
Destructor. | |
virtual Number | f (const Vector &x) |
Accessor methods for model data. | |
virtual Number | f (const Vector &x, Number mu) |
Objective value (depending in mu) - incorrect version for OrigIpoptNLP. | |
virtual SmartPtr< const Vector > | grad_f (const Vector &x) |
Gradient of the objective. | |
virtual SmartPtr< const Vector > | grad_f (const Vector &x, Number mu) |
Gradient of the objective (depending in mu) - incorrect version for OrigIpoptNLP. | |
virtual SmartPtr< const Vector > | c (const Vector &x) |
Equality constraint residual. | |
virtual SmartPtr< const Matrix > | jac_c (const Vector &x) |
Jacobian Matrix for equality constraints. | |
virtual SmartPtr< const Vector > | d (const Vector &x) |
Inequality constraint residual (reformulated as equalities with slacks) | |
virtual SmartPtr< const Matrix > | jac_d (const Vector &x) |
Jacobian Matrix for inequality constraints. | |
virtual SmartPtr< const SymMatrix > | h (const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd) |
Hessian of the Lagrangian. | |
virtual SmartPtr< const SymMatrix > | h (const Vector &x, Number obj_factor, const Vector &yc, const Vector &yd, Number mu) |
Hessian of the Lagrangian (depending in mu) - incorrect version for OrigIpoptNLP. | |
virtual SmartPtr< const SymMatrix > | uninitialized_h () |
Provides a Hessian matrix from the correct matrix space with uninitialized values. | |
virtual SmartPtr< const Vector > | x_L () const |
Lower bounds on x. | |
virtual SmartPtr< const Matrix > | Px_L () const |
Permutation matrix (x_L_ -> x) | |
virtual SmartPtr< const Vector > | orig_x_L () const |
Original lower bounds on x. | |
virtual SmartPtr< const Vector > | x_U () const |
Upper bounds on x. | |
virtual SmartPtr< const Matrix > | Px_U () const |
Permutation matrix (x_U_ -> x) | |
virtual SmartPtr< const Vector > | orig_x_U () const |
Original upper bounds on x. | |
virtual SmartPtr< const Vector > | d_L () const |
Lower bounds on d. | |
virtual SmartPtr< const Matrix > | Pd_L () const |
Permutation matrix (d_L_ -> d) | |
virtual SmartPtr< const Vector > | d_U () const |
Upper bounds on d. | |
virtual SmartPtr< const Matrix > | Pd_U () const |
Permutation matrix (d_U_ -> d) | |
virtual SmartPtr< const SymMatrixSpace > | HessianMatrixSpace () const |
Accessor method to obtain the MatrixSpace for the Hessian matrix (or it's approximation) | |
virtual SmartPtr< const VectorSpace > | x_space () const |
x_space | |
Counters for the number of function evaluations. | |
virtual Index | f_evals () const |
virtual Index | grad_f_evals () const |
virtual Index | c_evals () const |
virtual Index | jac_c_evals () const |
virtual Index | d_evals () const |
virtual Index | jac_d_evals () const |
virtual Index | h_evals () const |
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) |
Solution Routines - overloaded from IpoptNLP. | |
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, SmartPtr< const IpoptData > ip_data, SmartPtr< IpoptCalculatedQuantities > ip_cq) |
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virtual bool | Initialize (const Journalist &jnlst, const OptionsList &options, const std::string &prefix) |
Initialization method. | |
virtual bool | InitializeStructures (SmartPtr< Vector > &x, bool init_x, SmartPtr< Vector > &y_c, bool init_y_c, SmartPtr< Vector > &y_d, bool init_y_d, SmartPtr< Vector > &z_L, bool init_z_L, SmartPtr< Vector > &z_U, bool init_z_U, SmartPtr< Vector > &v_L, SmartPtr< Vector > &v_U)=0 |
Initialize (create) structures for the iteration data. | |
virtual bool | GetWarmStartIterate (IteratesVector &warm_start_iterate)=0 |
Method accessing the GetWarmStartIterate of the NLP. | |
virtual void | 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 |
Accessor method for vector/matrix spaces pointers. | |
virtual void | AdjustVariableBounds (const Vector &new_x_L, const Vector &new_x_U, const Vector &new_d_L, const Vector &new_d_U)=0 |
Method for adapting the variable bounds. | |
SmartPtr< NLPScalingObject > | NLP_scaling () const |
Returns the scaling strategy object. | |
IpoptNLP (const SmartPtr< NLPScalingObject > nlp_scaling) | |
virtual | ~IpoptNLP () |
Destructor. | |
DECLARE_STD_EXCEPTION (Eval_Error) | |
thrown if there is any error evaluating values from the nlp | |
virtual bool | objective_depends_on_mu () const |
Method for telling the IpoptCalculatedQuantities class whether the objective function depends on the barrier function. | |
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ReferencedObject () | |
virtual | ~ReferencedObject () |
Index | ReferenceCount () const |
void | AddRef (const Referencer *referencer) const |
void | ReleaseRef (const Referencer *referencer) const |
Static Public Member Functions | |
static void | RegisterOptions (SmartPtr< RegisteredOptions > roptions) |
Called to register the options. | |
Private Member Functions | |
Default Compiler Generated Methods | |
(Hidden to avoid implicit creation/calling). These methods are not implemented and we do not want the compiler to implement them for us, so we declare them private and do not define them. This ensures that they will not be implicitly created/called. | |
OrigIpoptNLP () | |
Default Constructor. | |
OrigIpoptNLP (const OrigIpoptNLP &) | |
Copy Constructor. | |
void | operator= (const OrigIpoptNLP &) |
Overloaded Assignment Operator. | |
auxiliary functions | |
void | relax_bounds (Number bound_relax_factor, Vector &bounds) |
relax the bounds by a relative move of relax_bound_factor. | |
SmartPtr< const Vector > | get_unscaled_x (const Vector &x) |
Method for getting the unscaled version of the x vector. | |
Private Attributes | |
SmartPtr< const Journalist > | jnlst_ |
Journalist. | |
SmartPtr< NLP > | nlp_ |
Pointer to the NLP. | |
bool | initialized_ |
Flag indicating if initialization method has been called. | |
SmartPtr< const VectorSpace > | x_space_ |
Necessary Vector/Matrix spaces. | |
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 > | h_space_ |
SmartPtr< const MatrixSpace > | scaled_jac_c_space_ |
SmartPtr< const MatrixSpace > | scaled_jac_d_space_ |
SmartPtr< const SymMatrixSpace > | scaled_h_space_ |
Storage for Model Quantities | |
CachedResults< Number > | f_cache_ |
Objective function. | |
CachedResults< SmartPtr< const Vector > > | grad_f_cache_ |
Gradient of the objective function. | |
CachedResults< SmartPtr< const Vector > > | c_cache_ |
Equality constraint residuals. | |
CachedResults< SmartPtr< const Matrix > > | jac_c_cache_ |
Jacobian Matrix for equality constraints (current iteration) | |
CachedResults< SmartPtr< const Vector > > | d_cache_ |
Inequality constraint residual (reformulated as equalities with slacks. | |
CachedResults< SmartPtr< const Matrix > > | jac_d_cache_ |
Jacobian Matrix for inequality constraints (current iteration) | |
CachedResults< SmartPtr< const SymMatrix > > | h_cache_ |
Hessian of the lagrangian (current iteration) | |
CachedResults< SmartPtr< const Vector > > | unscaled_x_cache_ |
Unscaled version of x vector. | |
SmartPtr< const Vector > | x_L_ |
Lower bounds on x. | |
SmartPtr< const Matrix > | Px_L_ |
Permutation matrix (x_L_ -> x) | |
SmartPtr< const Vector > | x_U_ |
Upper bounds on x. | |
SmartPtr< const Matrix > | Px_U_ |
Permutation matrix (x_U_ -> x) | |
SmartPtr< const Vector > | d_L_ |
Lower bounds on d. | |
SmartPtr< const Matrix > | Pd_L_ |
Permutation matrix (d_L_ -> d) | |
SmartPtr< const Vector > | d_U_ |
Upper bounds on d. | |
SmartPtr< const Matrix > | Pd_U_ |
Permutation matrix (d_U_ -> d) | |
SmartPtr< const Vector > | orig_x_L_ |
Original unmodified lower bounds on x. | |
SmartPtr< const Vector > | orig_x_U_ |
Original unmodified upper bounds on x. | |
Algorithmic parameters | |
Number | bound_relax_factor_ |
relaxation factor for the bounds | |
Number | constr_viol_tol_ |
constraint violation tolerance (from OptimalityErrorConvergenceCheck) | |
bool | honor_original_bounds_ |
Flag indicating whether the primal variables should be projected back into original bounds are optimization. | |
bool | warm_start_same_structure_ |
Flag indicating whether the TNLP with identical structure has already been solved before. | |
HessianApproximationType | hessian_approximation_ |
Flag indicating what Hessian information is to be used. | |
HessianApproximationSpace | hessian_approximation_space_ |
Flag indicating in which space Hessian is to be approximated. | |
bool | check_derivatives_for_naninf_ |
Flag indicating whether it is desired to check if there are Nan or Inf entries in first and second derivative matrices. | |
bool | jac_c_constant_ |
Flag indicating if we need to ask for equality constraint Jacobians only once. | |
bool | jac_d_constant_ |
Flag indicating if we need to ask for inequality constraint Jacobians only once. | |
bool | hessian_constant_ |
Flag indicating if we need to ask for Hessian only once. | |
Counters for the function evaluations | |
Index | f_evals_ |
Index | grad_f_evals_ |
Index | c_evals_ |
Index | jac_c_evals_ |
Index | d_evals_ |
Index | jac_d_evals_ |
Index | h_evals_ |
Timing statistics for the function evaluations. | |
TimingStatistics & | timing_statistics_ |
This class maps the traditional NLP into something that is more useful for Ipopt.
This class takes care of storing the calculated model results, handles caching, scaling, and (some day) takes care of addition of slacks.
Given a NLP
\begin{eqnarray*} \mathrm{min} && f(x), \\ \mathrm{s.t.} && c(x) = 0, &\qquad y_c\\ && d_L \leq d(x) \leq d_U, &\qquad y_d \\ && x_L \leq x \leq x_U, &\qquad z_L, z_U \end{eqnarray*}
and (invertible diagonal) scaling matrices \(s_o\), \(s_c\), \(s_d\), \(s_x\), this class represents the NLP
\begin{eqnarray*} \mathrm{min} && s_o f(s_x^{-1} \tilde x), \\ \mathrm{s.t.} && s_c c(s_x^{-1} \tilde x) = 0, &\qquad \tilde y_c \\ && s_d d_L \leq s_d d(s_x^{-1} \tilde x) \leq s_d d_U, &\qquad \tilde y_d \\ && s_x x_L \leq \tilde x \leq s_x x_U, &\qquad \tilde z_L, z_U \end{eqnarray*}
where \(\tilde x\), \(\tilde y_c\), \(\tilde y_d\), \(\tilde z_L\), \(\tilde z_U\), are the primal and dual variables of the scaled problem (though, Ipopt adds slack variables additionally).
The correspondence between a scaled and its corresponding unscaled solution is
\begin{eqnarray*} x && = s_x^{-1} \tilde x \\ y_c && = s_o^{-1} s_c \tilde y_c \\ y_d && = s_o^{-1} s_d \tilde y_d \\ z_L && = s_o^{-1} s_x \tilde z_L \\ z_U && = s_o^{-1} s_x \tilde z_U \end{eqnarray*}
Definition at line 65 of file IpOrigIpoptNLP.hpp.
Ipopt::OrigIpoptNLP::OrigIpoptNLP | ( | const SmartPtr< const Journalist > & | jnlst, |
const SmartPtr< NLP > & | nlp, | ||
const SmartPtr< NLPScalingObject > & | nlp_scaling, | ||
TimingStatistics & | timing_statistics | ||
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Constructor.
jnlst | Journalist |
nlp | NLP |
nlp_scaling | NLP scaling |
timing_statistics | Timing statistics |
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Destructor.
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Default Constructor.
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Copy Constructor.
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Initialization method.
Set the internal options and initialize internal data structures.
Reimplemented from Ipopt::IpoptNLP.
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Initialize (create) structures for the iteration data.
Implements Ipopt::IpoptNLP.
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Method accessing the GetWarmStartIterate of the NLP.
Implements Ipopt::IpoptNLP.
Definition at line 105 of file IpOrigIpoptNLP.hpp.
Objective value (depending in mu) - incorrect version for OrigIpoptNLP.
Implements Ipopt::IpoptNLP.
Gradient of the objective.
Implements Ipopt::IpoptNLP.
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Gradient of the objective (depending in mu) - incorrect version for OrigIpoptNLP.
Implements Ipopt::IpoptNLP.
Equality constraint residual.
Implements Ipopt::IpoptNLP.
Jacobian Matrix for equality constraints.
Implements Ipopt::IpoptNLP.
Inequality constraint residual (reformulated as equalities with slacks)
Implements Ipopt::IpoptNLP.
Jacobian Matrix for inequality constraints.
Implements Ipopt::IpoptNLP.
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Hessian of the Lagrangian.
Implements Ipopt::IpoptNLP.
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Hessian of the Lagrangian (depending in mu) - incorrect version for OrigIpoptNLP.
Implements Ipopt::IpoptNLP.
Provides a Hessian matrix from the correct matrix space with uninitialized values.
This can be used in LeastSquareMults to obtain a "zero Hessian".
Implements Ipopt::IpoptNLP.
Permutation matrix (x_L_ -> x)
Implements Ipopt::IpoptNLP.
Definition at line 195 of file IpOrigIpoptNLP.hpp.
Original lower bounds on x.
Returns NULL if bounds are not relaxed.
Definition at line 205 of file IpOrigIpoptNLP.hpp.
Permutation matrix (x_U_ -> x)
Implements Ipopt::IpoptNLP.
Definition at line 217 of file IpOrigIpoptNLP.hpp.
Original upper bounds on x.
Returns NULL if bounds are not relaxed.
Definition at line 227 of file IpOrigIpoptNLP.hpp.
Permutation matrix (d_L_ -> d)
Implements Ipopt::IpoptNLP.
Definition at line 239 of file IpOrigIpoptNLP.hpp.
Permutation matrix (d_U_ -> d)
Implements Ipopt::IpoptNLP.
Definition at line 251 of file IpOrigIpoptNLP.hpp.
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Accessor method to obtain the MatrixSpace for the Hessian matrix (or it's approximation)
Implements Ipopt::IpoptNLP.
Definition at line 256 of file IpOrigIpoptNLP.hpp.
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Accessor method for vector/matrix spaces pointers.
Implements Ipopt::IpoptNLP.
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Method for adapting the variable bounds.
This is called if slacks are becoming too small
Implements Ipopt::IpoptNLP.
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Implements Ipopt::IpoptNLP.
Definition at line 296 of file IpOrigIpoptNLP.hpp.
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Implements Ipopt::IpoptNLP.
Definition at line 300 of file IpOrigIpoptNLP.hpp.
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Implements Ipopt::IpoptNLP.
Definition at line 304 of file IpOrigIpoptNLP.hpp.
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Implements Ipopt::IpoptNLP.
Definition at line 308 of file IpOrigIpoptNLP.hpp.
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Implements Ipopt::IpoptNLP.
Definition at line 312 of file IpOrigIpoptNLP.hpp.
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Implements Ipopt::IpoptNLP.
Definition at line 316 of file IpOrigIpoptNLP.hpp.
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Implements Ipopt::IpoptNLP.
Definition at line 320 of file IpOrigIpoptNLP.hpp.
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Solution Routines - overloaded from IpoptNLP.
Implements Ipopt::IpoptNLP.
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Implements Ipopt::IpoptNLP.
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Called to register the options.
Accessor method to the underlying NLP.
Definition at line 365 of file IpOrigIpoptNLP.hpp.
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Overloaded Assignment Operator.
relax the bounds by a relative move of relax_bound_factor.
Here, relax_bound_factor should be negative (or zero) for lower bounds, and positive (or zero) for upper bounds.
Method for getting the unscaled version of the x vector.
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Definition at line 372 of file IpOrigIpoptNLP.hpp.
Pointer to the NLP.
Definition at line 375 of file IpOrigIpoptNLP.hpp.
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Necessary Vector/Matrix spaces.
Definition at line 379 of file IpOrigIpoptNLP.hpp.
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Definition at line 380 of file IpOrigIpoptNLP.hpp.
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Definition at line 381 of file IpOrigIpoptNLP.hpp.
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Definition at line 382 of file IpOrigIpoptNLP.hpp.
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Definition at line 383 of file IpOrigIpoptNLP.hpp.
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Definition at line 384 of file IpOrigIpoptNLP.hpp.
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Definition at line 385 of file IpOrigIpoptNLP.hpp.
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Definition at line 386 of file IpOrigIpoptNLP.hpp.
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Definition at line 387 of file IpOrigIpoptNLP.hpp.
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Definition at line 388 of file IpOrigIpoptNLP.hpp.
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Definition at line 389 of file IpOrigIpoptNLP.hpp.
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Definition at line 390 of file IpOrigIpoptNLP.hpp.
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Definition at line 391 of file IpOrigIpoptNLP.hpp.
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Definition at line 392 of file IpOrigIpoptNLP.hpp.
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Definition at line 394 of file IpOrigIpoptNLP.hpp.
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Definition at line 395 of file IpOrigIpoptNLP.hpp.
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Definition at line 396 of file IpOrigIpoptNLP.hpp.
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Objective function.
Definition at line 402 of file IpOrigIpoptNLP.hpp.
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Gradient of the objective function.
Definition at line 405 of file IpOrigIpoptNLP.hpp.
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Equality constraint residuals.
Definition at line 408 of file IpOrigIpoptNLP.hpp.
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Jacobian Matrix for equality constraints (current iteration)
Definition at line 413 of file IpOrigIpoptNLP.hpp.
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Inequality constraint residual (reformulated as equalities with slacks.
Definition at line 418 of file IpOrigIpoptNLP.hpp.
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Jacobian Matrix for inequality constraints (current iteration)
Definition at line 423 of file IpOrigIpoptNLP.hpp.
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Hessian of the lagrangian (current iteration)
Definition at line 428 of file IpOrigIpoptNLP.hpp.
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Unscaled version of x vector.
Definition at line 431 of file IpOrigIpoptNLP.hpp.
Lower bounds on x.
Definition at line 434 of file IpOrigIpoptNLP.hpp.
Permutation matrix (x_L_ -> x)
Definition at line 437 of file IpOrigIpoptNLP.hpp.
Upper bounds on x.
Definition at line 440 of file IpOrigIpoptNLP.hpp.
Permutation matrix (x_U_ -> x)
Definition at line 443 of file IpOrigIpoptNLP.hpp.
Lower bounds on d.
Definition at line 446 of file IpOrigIpoptNLP.hpp.
Permutation matrix (d_L_ -> d)
Definition at line 449 of file IpOrigIpoptNLP.hpp.
Upper bounds on d.
Definition at line 452 of file IpOrigIpoptNLP.hpp.
Permutation matrix (d_U_ -> d)
Definition at line 455 of file IpOrigIpoptNLP.hpp.
Original unmodified lower bounds on x.
Definition at line 458 of file IpOrigIpoptNLP.hpp.
Original unmodified upper bounds on x.
Definition at line 461 of file IpOrigIpoptNLP.hpp.
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relaxation factor for the bounds
Definition at line 510 of file IpOrigIpoptNLP.hpp.
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constraint violation tolerance (from OptimalityErrorConvergenceCheck)
Definition at line 513 of file IpOrigIpoptNLP.hpp.
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Flag indicating whether the primal variables should be projected back into original bounds are optimization.
Definition at line 518 of file IpOrigIpoptNLP.hpp.
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Flag indicating whether the TNLP with identical structure has already been solved before.
Definition at line 523 of file IpOrigIpoptNLP.hpp.
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Flag indicating what Hessian information is to be used.
Definition at line 526 of file IpOrigIpoptNLP.hpp.
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Flag indicating in which space Hessian is to be approximated.
Definition at line 529 of file IpOrigIpoptNLP.hpp.
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Flag indicating whether it is desired to check if there are Nan or Inf entries in first and second derivative matrices.
Definition at line 534 of file IpOrigIpoptNLP.hpp.
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Flag indicating if we need to ask for equality constraint Jacobians only once.
Definition at line 539 of file IpOrigIpoptNLP.hpp.
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Flag indicating if we need to ask for inequality constraint Jacobians only once.
Definition at line 544 of file IpOrigIpoptNLP.hpp.
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Flag indicating if we need to ask for Hessian only once.
Definition at line 547 of file IpOrigIpoptNLP.hpp.
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Definition at line 552 of file IpOrigIpoptNLP.hpp.
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Definition at line 553 of file IpOrigIpoptNLP.hpp.
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Definition at line 554 of file IpOrigIpoptNLP.hpp.
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Definition at line 555 of file IpOrigIpoptNLP.hpp.
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Definition at line 556 of file IpOrigIpoptNLP.hpp.
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Definition at line 557 of file IpOrigIpoptNLP.hpp.
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Definition at line 558 of file IpOrigIpoptNLP.hpp.
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Flag indicating if initialization method has been called.
Definition at line 562 of file IpOrigIpoptNLP.hpp.
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Definition at line 566 of file IpOrigIpoptNLP.hpp.