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213 lines
7.0 KiB
213 lines
7.0 KiB
/*
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* This file is part of ACADO Toolkit.
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*
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* ACADO Toolkit -- A Toolkit for Automatic Control and Dynamic Optimization.
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* Copyright (C) 2008-2014 by Boris Houska, Hans Joachim Ferreau,
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* Milan Vukov, Rien Quirynen, KU Leuven.
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* Developed within the Optimization in Engineering Center (OPTEC)
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* under supervision of Moritz Diehl. All rights reserved.
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*
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* ACADO Toolkit is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 3 of the License, or (at your option) any later version.
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*
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* ACADO Toolkit is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with ACADO Toolkit; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*
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*/
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/**
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* \file include/acado/sparse_solver/conjugate_gradient_method.hpp
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* \author Boris Houska, Hans Joachim Ferreau
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*/
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#ifndef ACADO_TOOLKIT_CONJUGATE_GRADIENT_MEHTOD_HPP
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#define ACADO_TOOLKIT_CONJUGATE_GRADIENT_MEHTOD_HPP
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#include <acado/utils/acado_utils.hpp>
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BEGIN_NAMESPACE_ACADO
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/**
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* \brief Implements a conjugate gradient method as sparse linear algebra solver.
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*
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* \ingroup NumericalAlgorithms
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*
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* The class ConjugateGradientMethod implements a special sparse \n
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* linear algebra solver. After the application of an preconditioner \n
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* an iterative conjugate basis of the sparse data matrix A is \n
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* computed. The algotithm stops if the required tolerance is achieved.\n
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*
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* \author Boris Houska, Hans Joachim Ferreau
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*/
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class ConjugateGradientMethod : public SparseSolver{
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//
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// PUBLIC MEMBER FUNCTIONS:
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//
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public:
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/** Default constructor. */
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ConjugateGradientMethod( );
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/** Copy constructor (deep copy). */
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ConjugateGradientMethod( const ConjugateGradientMethod &arg );
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/** Destructor. */
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virtual ~ConjugateGradientMethod( );
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/** Clone operator (deep copy). */
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virtual SparseSolver* clone() const = 0;
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/** Defines the dimension n of A \in R^{n \times n} \n
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* \n
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* \return SUCCESSFUL_RETURN \n
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*/
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virtual returnValue setDimension( const int &n );
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/** Defines the number of non-zero elements in the \n
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* matrix A \n
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* \n
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* \return SUCCESSFUL_RETURN \n
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*/
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virtual returnValue setNumberOfEntries( const int &nDense_ );
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/** Sets an index list containing the positions of the \n
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* non-zero elements in the matrix A.
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*/
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virtual returnValue setIndices( const int *rowIdx_,
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const int *colIdx_ ) = 0;
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/** Sets the non-zero elements of the matrix A. The double* A \n
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* is assumed to contain nDense entries corresponding to \n
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* non-zero elements of A. \n
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*/
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virtual returnValue setMatrix( double *A_ );
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/** Solves the system A*x = b for the specified data. \n
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* \n
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* \return SUCCESSFUL_RETURN \n
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* RET_LINEAR_SYSTEM_NUMERICALLY_SINGULAR \n
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*/
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virtual returnValue solve( double *b );
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/** Returns the solution of the equation A*x = b if solved. \n
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* \n
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* \return SUCCESSFUL_RETURN \n
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*/
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virtual returnValue getX( double *x_ );
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/** Sets the required tolerance (accuracy) for the solution of \n
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* the linear equation. For large tolerances an iterative \n
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* algorithm might converge earlier. \n
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* \n
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* Requires || A*x - b || <= TOL \n
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* \n
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* The norm || . || is possibly scaled by a preconditioner. \n
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* \n
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* \return SUCCESSFUL_RETURN \n
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*/
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virtual returnValue setTolerance( double TOL_ );
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/** Sets the print level. \n
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* \n
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* \return SUCCESSFUL_RETURN \n
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*/
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virtual returnValue setPrintLevel( PrintLevel printLevel_ );
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//
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// PROTECTED MEMBER FUNCTIONS:
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//
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protected:
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/** Returns the scalar product of aa and bb. (only internal use)*/
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double scalarProduct( double *aa, double *bb );
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/** Evaluates the matrix-vector product result = A*xx efficiently. (only internal use)*/
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virtual void multiply( double *xx , double *result ) = 0;
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/** Applies the preconditioner to the vector b (only internal use) */
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virtual returnValue applyPreconditioner( double *b ) = 0;
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/** Applies the inverse of the preconditioner to the vector x (only internal use) */
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virtual returnValue applyInversePreconditioner( double *x_ ) = 0;
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/** Computes the preconditioner and Applies it to the input matrix. */
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virtual returnValue computePreconditioner( double* A_ ) = 0;
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//
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// DATA MEMBERS:
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//
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protected:
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// DIMENSIONS:
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// --------------------
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int dim; // dimension of the matrix A
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int nDense; // number of non-zero entries in A
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// DATA:
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// --------------------
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double *A; // The (sparse) matrix A
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double *x; // The result vector x
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// AUXILIARY VARIABLES:
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// --------------------
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double *norm2; // Auxiliary variables
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double **p; // conjugate basis vectors
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double *r; // the actual residuum
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int pCounter; // a counter for the iterates
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double *condScale; // scaling factors to improve
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// the conditioning of the system
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double TOL; // The required tolerance. (default 10^(-10))
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PrintLevel printLevel; // The PrintLevel.
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};
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CLOSE_NAMESPACE_ACADO
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#include <acado/sparse_solver/conjugate_gradient_method.ipp>
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#endif // ACADO_TOOLKIT_CONJUGATE_GRADIENT_METHOD_HPP
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/*
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* end of file
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*/
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