Public Member Functions | Protected Member Functions | Protected Attributes

Base class for techniques of approximating second-order derivatives within NLPsolvers. More...

#include <nlp_derivative_approximation.hpp>

Inheritance diagram for NLPderivativeApproximation:
Inheritance graph
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List of all members.

Public Member Functions

virtual returnValue apply (BlockMatrix &B, const BlockMatrix &x, const BlockMatrix &y)=0
virtual
NLPderivativeApproximation
clone () const =0
double getHessianScaling () const
virtual returnValue initHessian (BlockMatrix &B, uint N, const OCPiterate &iter)=0
virtual returnValue initScaling (BlockMatrix &B, const BlockMatrix &x, const BlockMatrix &y)=0
 NLPderivativeApproximation ()
 NLPderivativeApproximation (UserInteraction *_userInteraction)
 NLPderivativeApproximation (const NLPderivativeApproximation &rhs)
NLPderivativeApproximationoperator= (const NLPderivativeApproximation &rhs)
virtual ~NLPderivativeApproximation ()

Protected Member Functions

virtual returnValue setupLogging ()
virtual returnValue setupOptions ()

Protected Attributes

double hessianScaling

Detailed Description

Base class for techniques of approximating second-order derivatives within NLPsolvers.

The class NLPderivativeApproximation serves as a base class for different techniques of approximating second-order derivative information within iterative NLPsolvers.

Author:
Boris Houska, Hans Joachim Ferreau

Definition at line 61 of file nlp_derivative_approximation.hpp.


Constructor & Destructor Documentation

Default constructor.

Definition at line 45 of file nlp_derivative_approximation.cpp.

Definition at line 54 of file nlp_derivative_approximation.cpp.

Copy constructor (deep copy).

Definition at line 67 of file nlp_derivative_approximation.cpp.

Destructor.

Definition at line 73 of file nlp_derivative_approximation.cpp.


Member Function Documentation

virtual returnValue NLPderivativeApproximation::apply ( BlockMatrix B,
const BlockMatrix x,
const BlockMatrix y 
) [pure virtual]
Parameters:
Bmatrix to be updated
xdirection x
yresiduum

Implemented in BFGSupdate, ExactHessian, GaussNewtonApproximationWithBFGS, ConstantHessian, and GaussNewtonApproximation.

Reimplemented in ExactHessian.

virtual returnValue NLPderivativeApproximation::initHessian ( BlockMatrix B,
uint  N,
const OCPiterate iter 
) [pure virtual]
Parameters:
Bmatrix to be initialised
Nnumber of intervals
itercurrent iterate

Implemented in BFGSupdate, GaussNewtonApproximationWithBFGS, ExactHessian, GaussNewtonApproximation, and ConstantHessian.

virtual returnValue NLPderivativeApproximation::initScaling ( BlockMatrix B,
const BlockMatrix x,
const BlockMatrix y 
) [pure virtual]
Parameters:
Bmatrix to be updated
xdirection x
yresiduum

Implemented in BFGSupdate, GaussNewtonApproximationWithBFGS, ExactHessian, GaussNewtonApproximation, and ConstantHessian.

NLPderivativeApproximation & NLPderivativeApproximation::operator= ( const NLPderivativeApproximation rhs)

Assignment operator (deep copy).

Definition at line 78 of file nlp_derivative_approximation.cpp.

Definition at line 104 of file nlp_derivative_approximation.cpp.

Definition at line 98 of file nlp_derivative_approximation.cpp.


Member Data Documentation

Definition at line 123 of file nlp_derivative_approximation.hpp.


The documentation for this class was generated from the following files:


acado
Author(s): Milan Vukov, Rien Quirynen
autogenerated on Sat Jun 8 2019 19:40:25