Reference documentation for deal.II version GIT 6da2e5d553 2022-07-01 18:55:02+00:00

#include <deal.II/lac/trilinos_precondition.h>

Inheritance diagram for TrilinosWrappers::PreconditionAMG:
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## Public Types

using size_type = ::types::global_dof_index

## Public Member Functions

~PreconditionAMG () override

void initialize (const SparseMatrix &matrix, const Teuchos::ParameterList &ml_parameters)

void initialize (const Epetra_RowMatrix &matrix, const Teuchos::ParameterList &ml_parameters)

template<typename number >

void reinit ()

void clear ()

size_type memory_consumption () const

MPI_Comm get_mpi_communicator () const

void transpose ()

virtual void vmult (MPI::Vector &dst, const MPI::Vector &src) const

virtual void vmult (::Vector< double > &dst, const ::Vector< double > &src) const

virtual void vmult (::LinearAlgebra::distributed::Vector< double > &dst, const ::LinearAlgebra::distributed::Vector< double > &src) const

virtual void Tvmult (MPI::Vector &dst, const MPI::Vector &src) const

virtual void Tvmult (::Vector< double > &dst, const ::Vector< double > &src) const

virtual void Tvmult (::LinearAlgebra::distributed::Vector< double > &dst, const ::LinearAlgebra::distributed::Vector< double > &src) const

Epetra_Operatortrilinos_operator () const

## Private Attributes

std::shared_ptr< SparseMatrixtrilinos_matrix

## Subscriptor functionality

Classes derived from Subscriptor provide a facility to subscribe to this object. This is mostly used by the SmartPointer class.

std::atomic< unsigned int > counter

std::map< std::string, unsigned int > counter_map

std::vector< std::atomic< bool > * > validity_pointers

const std::type_info * object_info

void subscribe (std::atomic< bool > *const validity, const std::string &identifier="") const

void unsubscribe (std::atomic< bool > *const validity, const std::string &identifier="") const

unsigned int n_subscriptions () const

template<typename StreamType >
void list_subscribers (StreamType &stream) const

void list_subscribers () const

template<class Archive >
void serialize (Archive &ar, const unsigned int version)

static ::ExceptionBaseExcInUse (int arg1, std::string arg2, std::string arg3)

static ::ExceptionBaseExcNoSubscriber (std::string arg1, std::string arg2)

using map_value_type = decltype(counter_map)::value_type

using map_iterator = decltype(counter_map)::iterator

static std::mutex mutex

void check_no_subscribers () const noexcept

## Partitioners

IndexSet locally_owned_domain_indices () const

IndexSet locally_owned_range_indices () const

Teuchos::RCP< Epetra_Operatorpreconditioner

Epetra_MpiComm communicator

std::shared_ptr< Epetra_Map > vector_distributor

static ::ExceptionBaseExcNonMatchingMaps (std::string arg1)

## Detailed Description

This class implements an algebraic multigrid (AMG) preconditioner based on the Trilinos ML implementation, which is a black-box preconditioner that works well for many PDE-based linear problems. What this class does is twofold. When the initialize() function is invoked, a ML preconditioner object is created based on the matrix that we want the preconditioner to be based on. A call of the respective vmult function does call the respective operation in the Trilinos package, where it is called ApplyInverse. Use of this class is explained in the step-31 tutorial program.

Since the Trilinos objects we want to use are heavily dependent on Epetra objects, we recommend using this class in conjunction with Trilinos (Epetra) sparse matrices and vectors. There is support for use with matrices of the SparseMatrix class and corresponding vectors, too, but this requires generating a copy of the matrix, which is slower and takes (much) more memory. When doing such a copy operation, we can still profit from the fact that some of the entries in the preconditioner matrix are zero and hence can be neglected.

The implementation is able to distinguish between matrices from elliptic problems and convection dominated problems. We use the standard options provided by Trilinos ML for elliptic problems, except that we use a Chebyshev smoother instead of a symmetric Gauss-Seidel smoother. For most elliptic problems, Chebyshev provides a better damping of high frequencies (in the algebraic sense) than Gauss-Seidel (SSOR), and is faster (Chebyshev requires only some matrix-vector products, whereas SSOR requires substitutions which are more expensive). Moreover, Chebyshev is perfectly parallel in the sense that it does not degenerate when used on many processors. SSOR, on the other hand, gets more Jacobi-like on many processors.

For proper functionality of this class we recommend using Trilinos v9.0 and higher. Older versions may have problems with generating the coarse- matrix structure when using matrices with many nonzero entries per row (i.e., matrices stemming from higher order finite element discretizations).

Definition at line 1336 of file trilinos_precondition.h.

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