Implements a conjugate gradient method as sparse linear algebra solver. More...

#include <conjugate_gradient_method.hpp>

Inheritance diagram for ConjugateGradientMethod:

Public Member Functions
virtual SparseSolver *	clone () const =0
	ConjugateGradientMethod ()
	ConjugateGradientMethod (const ConjugateGradientMethod &arg)
virtual returnValue	getX (double *x_)
virtual returnValue	setDimension (const int &n)
virtual returnValue	setIndices (const int rowIdx_, const int colIdx_)=0
virtual returnValue	setMatrix (double *A_)
virtual returnValue	setNumberOfEntries (const int &nDense_)
virtual returnValue	setPrintLevel (PrintLevel printLevel_)
virtual returnValue	setTolerance (double TOL_)
virtual returnValue	solve (double *b)
virtual	~ConjugateGradientMethod ()
Protected Member Functions
virtual returnValue	applyInversePreconditioner (double *x_)=0
virtual returnValue	applyPreconditioner (double *b)=0
virtual returnValue	computePreconditioner (double *A_)=0
virtual void	multiply (double xx, double result)=0
double	scalarProduct (double aa, double bb)
Protected Attributes
double *	A
double *	condScale
int	dim
int	nDense
double *	norm2
double **	p
int	pCounter
PrintLevel	printLevel
double *	r
double	TOL
double *	x

Detailed Description

Implements a conjugate gradient method as sparse linear algebra solver.

The class ConjugateGradientMethod implements a special sparse
linear algebra solver. After the application of an preconditioner
an iterative conjugate basis of the sparse data matrix A is
computed. The algotithm stops if the required tolerance is achieved.

Author:: Boris Houska, Hans Joachim Ferreau

Definition at line 57 of file conjugate_gradient_method.hpp.

Constructor & Destructor Documentation

BEGIN_NAMESPACE_ACADO ConjugateGradientMethod::ConjugateGradientMethod ( )

Default constructor.

Definition at line 48 of file conjugate_gradient_method.cpp.

ConjugateGradientMethod::ConjugateGradientMethod ( const ConjugateGradientMethod & arg )

Copy constructor (deep copy).

Definition at line 64 of file conjugate_gradient_method.cpp.

ConjugateGradientMethod::~ConjugateGradientMethod ( ) [virtual]

Destructor.

Definition at line 126 of file conjugate_gradient_method.cpp.

Member Function Documentation

virtual returnValue ConjugateGradientMethod::applyInversePreconditioner ( double * x_ ) [protected, pure virtual]

Applies the inverse of the preconditioner to the vector x (only internal use)

Implemented in SymmetricConjugateGradientMethod, and NormalConjugateGradientMethod.

virtual returnValue ConjugateGradientMethod::applyPreconditioner ( double * b ) [protected, pure virtual]

Applies the preconditioner to the vector b (only internal use)

Implemented in SymmetricConjugateGradientMethod, and NormalConjugateGradientMethod.

virtual SparseSolver* ConjugateGradientMethod::clone ( ) const [pure virtual]

Clone operator (deep copy).

Implements SparseSolver.

Implemented in SymmetricConjugateGradientMethod, and NormalConjugateGradientMethod.

virtual returnValue ConjugateGradientMethod::computePreconditioner ( double * A_ ) [protected, pure virtual]

Computes the preconditioner and Applies it to the input matrix.

Implemented in SymmetricConjugateGradientMethod, and NormalConjugateGradientMethod.

returnValue ConjugateGradientMethod::getX ( double * x_ ) [virtual]

Returns the solution of the equation A*x = b if solved.

Returns:: SUCCESSFUL_RETURN

Implements SparseSolver.

Definition at line 329 of file conjugate_gradient_method.cpp.

virtual void ConjugateGradientMethod::multiply	(	double *	xx,
		double *	result
	)		`[protected, pure virtual]`

Evaluates the matrix-vector product result = A*xx efficiently. (only internal use)

Implemented in SymmetricConjugateGradientMethod, and NormalConjugateGradientMethod.

double ConjugateGradientMethod::scalarProduct	(	double *	aa,
		double *	bb
	)		`[protected]`

Returns the scalar product of aa and bb. (only internal use)

Definition at line 355 of file conjugate_gradient_method.cpp.

returnValue ConjugateGradientMethod::setDimension ( const int & n ) [virtual]

Defines the dimension n of A R^{n n}

Returns:: SUCCESSFUL_RETURN

Implements SparseSolver.

Definition at line 252 of file conjugate_gradient_method.cpp.

virtual returnValue ConjugateGradientMethod::setIndices	(	const int *	rowIdx_,
		const int *	colIdx_
	)		`[pure virtual]`

Sets an index list containing the positions of the
non-zero elements in the matrix A.

Implements SparseSolver.

Implemented in SymmetricConjugateGradientMethod, and NormalConjugateGradientMethod.

returnValue ConjugateGradientMethod::setMatrix ( double * A_ ) [virtual]

Sets the non-zero elements of the matrix A. The double* A
is assumed to contain nDense entries corresponding to
non-zero elements of A.

Implements SparseSolver.

Definition at line 315 of file conjugate_gradient_method.cpp.

returnValue ConjugateGradientMethod::setNumberOfEntries ( const int & nDense_ ) [virtual]

Defines the number of non-zero elements in the
matrix A

Returns:: SUCCESSFUL_RETURN

Implements SparseSolver.

Definition at line 300 of file conjugate_gradient_method.cpp.

returnValue ConjugateGradientMethod::setPrintLevel ( PrintLevel printLevel_ ) [virtual]

Sets the print level.

Returns:: SUCCESSFUL_RETURN

Implements SparseSolver.

Definition at line 342 of file conjugate_gradient_method.cpp.

returnValue ConjugateGradientMethod::setTolerance ( double TOL_ ) [virtual]

Sets the required tolerance (accuracy) for the solution of
the linear equation. For large tolerances an iterative
algorithm might converge earlier.

Requires || A*x - b || <= TOL

The norm || . || is possibly scaled by a preconditioner.