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

#include <nlp_derivative_approximation.hpp>

Inheritance diagram for NLPderivativeApproximation:

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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)

NLPderivativeApproximation &	operator= (const NLPderivativeApproximation &rhs)

virtual	~NLPderivativeApproximation ()

Public Member Functions inherited from AlgorithmicBase
int	addLogRecord (LogRecord &_record)

returnValue	addOption (OptionsName name, int value)

returnValue	addOption (OptionsName name, double value)

returnValue	addOption (uint idx, OptionsName name, int value)

returnValue	addOption (uint idx, OptionsName name, double value)

returnValue	addOptionsList ()

	AlgorithmicBase ()

	AlgorithmicBase (UserInteraction *_userInteraction)

	AlgorithmicBase (const AlgorithmicBase &rhs)

returnValue	get (OptionsName name, int &value) const

returnValue	get (OptionsName name, double &value) const

returnValue	get (OptionsName name, std::string &value) const

returnValue	get (uint idx, OptionsName name, int &value) const

returnValue	get (uint idx, OptionsName name, double &value) const

returnValue	getAll (LogName _name, MatrixVariablesGrid &values) const

returnValue	getFirst (LogName _name, DMatrix &firstValue) const

returnValue	getFirst (LogName _name, VariablesGrid &firstValue) const

returnValue	getLast (LogName _name, DMatrix &lastValue) const

returnValue	getLast (LogName _name, VariablesGrid &lastValue) const

Options	getOptions (uint idx) const

BooleanType	haveOptionsChanged () const

BooleanType	haveOptionsChanged (uint idx) const

AlgorithmicBase &	operator= (const AlgorithmicBase &rhs)

returnValue	plot (PlotFrequency _frequency=PLOT_IN_ANY_CASE)

returnValue	printLogRecord (std::ostream &_stream, int idx, LogPrintMode _mode=PRINT_ITEM_BY_ITEM) const

returnValue	replot (PlotFrequency _frequency=PLOT_IN_ANY_CASE)

returnValue	set (OptionsName name, int value)

returnValue	set (OptionsName name, double value)

returnValue	set (OptionsName name, const std::string &value)

returnValue	set (uint idx, OptionsName name, int value)

returnValue	set (uint idx, OptionsName name, double value)

returnValue	setAll (LogName _name, const MatrixVariablesGrid &values)

returnValue	setLast (LogName _name, int lastValue, double time=-INFTY)

returnValue	setLast (LogName _name, double lastValue, double time=-INFTY)

returnValue	setLast (LogName _name, const DVector &lastValue, double time=-INFTY)

returnValue	setLast (LogName _name, const DMatrix &lastValue, double time=-INFTY)

returnValue	setLast (LogName _name, const VariablesGrid &lastValue, double time=-INFTY)

returnValue	setOptions (const Options &arg)

returnValue	setOptions (uint idx, const Options &arg)

virtual	~AlgorithmicBase ()

Protected Member Functions
virtual returnValue	setupLogging ()

virtual returnValue	setupOptions ()

Protected Attributes
double	hessianScaling

Protected Attributes inherited from AlgorithmicBase
int	outputLoggingIdx

BooleanType	useModuleStandalone

UserInteraction *	userInteraction

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

BEGIN_NAMESPACE_ACADO NLPderivativeApproximation::NLPderivativeApproximation ( )

Default constructor.

Definition at line 45 of file nlp_derivative_approximation.cpp.

NLPderivativeApproximation::NLPderivativeApproximation ( UserInteraction * _userInteraction )

Definition at line 54 of file nlp_derivative_approximation.cpp.

NLPderivativeApproximation::NLPderivativeApproximation ( const NLPderivativeApproximation & rhs )

Copy constructor (deep copy).

Definition at line 67 of file nlp_derivative_approximation.cpp.

NLPderivativeApproximation::~NLPderivativeApproximation ( )

virtual

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

B	matrix to be updated
x	direction x
y	residuum

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

virtual NLPderivativeApproximation* NLPderivativeApproximation::clone ( ) const

pure virtual

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

double NLPderivativeApproximation::getHessianScaling ( ) const

inline

virtual returnValue NLPderivativeApproximation::initHessian	(	BlockMatrix &	B,
		uint	N,
		const OCPiterate &	iter
	)

pure virtual

Parameters

B	matrix to be initialised
N	number of intervals
iter	current iterate

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

virtual returnValue NLPderivativeApproximation::initScaling	(	BlockMatrix &	B,
		const BlockMatrix &	x,
		const BlockMatrix &	y
	)

pure virtual

Parameters

B	matrix to be updated
x	direction x
y	residuum

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.

returnValue NLPderivativeApproximation::setupLogging ( )

protectedvirtual

Definition at line 104 of file nlp_derivative_approximation.cpp.

returnValue NLPderivativeApproximation::setupOptions ( )

protectedvirtual

Definition at line 98 of file nlp_derivative_approximation.cpp.

Member Data Documentation

double NLPderivativeApproximation::hessianScaling

protected

Definition at line 123 of file nlp_derivative_approximation.hpp.

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

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation