Reference documentation for deal.II version Git f982bbb689 2019-09-17 15:49:08 -0400
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Classes | Public Types | Public Member Functions | Private Member Functions | Private Attributes | List of all members
PreconditionChebyshev< MatrixType, VectorType, PreconditionerType > Class Template Reference

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

Inheritance diagram for PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >:
[legend]

Classes

struct  AdditionalData
 

Public Types

using size_type = types::global_dof_index
 

Public Member Functions

void initialize (const MatrixType &matrix, const AdditionalData &additional_data=AdditionalData())
 
void vmult (VectorType &dst, const VectorType &src) const
 
void Tvmult (VectorType &dst, const VectorType &src) const
 
void step (VectorType &dst, const VectorType &src) const
 
void Tstep (VectorType &dst, const VectorType &src) const
 
void clear ()
 
size_type m () const
 
size_type n () const
 
- Public Member Functions inherited from Subscriptor
 Subscriptor ()
 
 Subscriptor (const Subscriptor &)
 
 Subscriptor (Subscriptor &&) noexcept
 
virtual ~Subscriptor ()
 
Subscriptoroperator= (const Subscriptor &)
 
Subscriptoroperator= (Subscriptor &&) noexcept
 
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)
 

Private Member Functions

void estimate_eigenvalues (const VectorType &src) const
 

Private Attributes

SmartPointer< const MatrixType, PreconditionChebyshev< MatrixType, VectorType, PreconditionerType > > matrix_ptr
 
VectorType solution_old
 
VectorType temp_vector1
 
VectorType temp_vector2
 
AdditionalData data
 
double theta
 
double delta
 
bool eigenvalues_are_initialized
 
Threads::Mutex mutex
 

Additional Inherited Members

- Static Public Member Functions inherited from Subscriptor
static ::ExceptionBaseExcInUse (int arg1, std::string arg2, std::string arg3)
 
static ::ExceptionBaseExcNoSubscriber (std::string arg1, std::string arg2)
 

Detailed Description

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
class PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >

Preconditioning with a Chebyshev polynomial for symmetric positive definite matrices. This preconditioner is based on an iteration of an inner preconditioner of type PreconditionerType with coefficients that are adapted to optimally cover an eigenvalue range between the largest eigenvalue \(\lambda_{\max{}}\) down to a given lower eigenvalue \(\lambda_{\min{}}\) specified by the optional parameter smoothing_range. The algorithm is based on the following three-term recurrence:

\[ x^{n+1} = x^{n} + \rho_n \rho_{n-1} (x^{n} - x^{n-1}) + \frac{\rho_n}{\lambda_{\max{}}-\lambda_{\min{}}} P^{-1} (b-Ax^n). \]

where the parameter \(\rho_0\) is set to \(\rho_0 = 2 \frac{\lambda_{\max{}}-\lambda_{\min{}}}{\lambda_{\max{}}+\lambda_{\min{}}}\) for the maximal eigenvalue \(\lambda_{\max{}}\) and updated via \(\rho_n = \left(2\frac{\lambda_{\max{}}+\lambda_{\min{}}} {\lambda_{\max{}}-\lambda_{\min{}}} - \rho_{n-1}\right)^{-1}\). The Chebyshev polynomial is constructed to strongly damp the eigenvalue range between \(\lambda_{\min{}}\) and \(\lambda_{\max{}}\) and is visualized e.g. in Utilities::LinearAlgebra::chebyshev_filter().

The typical use case for the preconditioner is a Jacobi preconditioner specified through DiagonalMatrix, which is also the default value for the preconditioner. Note that if the degree variable is set to one, the Chebyshev iteration corresponds to a Jacobi preconditioner (or the underlying preconditioner type) with relaxation parameter according to the specified smoothing range.

Besides the default choice of a pointwise Jacobi preconditioner, this class also allows for more advanced types of preconditioners, for example iterating block-Jacobi preconditioners in DG methods.

Apart from the inner preconditioner object, this iteration does not need access to matrix entries, which makes it an ideal ingredient for matrix-free computations. In that context, this class can be used as a multigrid smoother that is trivially parallel (assuming that matrix-vector products are parallel and the inner preconditioner is parallel). Its use is demonstrated in the step-37 and step-59 tutorial programs.

Estimation of the eigenvalues

The Chebyshev method relies on an estimate of the eigenvalues of the matrix which are computed during the first invocation of vmult(). The algorithm invokes a conjugate gradient solver (i.e., Lanczos iteration) so symmetry and positive definiteness of the (preconditioned) matrix system are requirements. The eigenvalue algorithm can be controlled by PreconditionChebyshev::AdditionalData::eig_cg_n_iterations specifying how many iterations should be performed. The iterations are started from an initial vector that depends on the vector type. For the classes Vector or LinearAlgebra::distributed::Vector, which have fast element access, it is a vector with entries (-5.5, -4.5, -3.5, -2.5, ..., 3.5, 4.5, 5.5) with appropriate epilogue and adjusted such that its mean is always zero, which works well for the Laplacian. This setup is stable in parallel in the sense that for a different number of processors but the same ordering of unknowns, the same initial vector and thus eigenvalue distribution will be computed, apart from roundoff errors. For other vector types, the initial vector contains all ones, scaled by the length of the vector, except for the very first entry that is zero, triggering high-frequency content again.

The computation of eigenvalues happens the first time one of the vmult(), Tvmult(), step() or Tstep() functions is called. This is because temporary vectors of the same layout as the source and destination vectors are necessary for these computations and this information gets only available through vmult().

Due to the cost of the eigenvalue estimate in the first vmult(), this class is most appropriate if it is applied repeatedly, e.g. in a smoother for a geometric multigrid solver, that can in turn be used to solve several linear systems.

Bypassing the eigenvalue computation

In some contexts, the automatic eigenvalue computation of this class may result in bad quality, or it may be unstable when used in parallel with different enumerations of the degrees of freedom, making computations strongly dependent on the parallel configuration. It is possible to bypass the automatic eigenvalue computation by setting AdditionalData::eig_cg_n_iterations to zero, and provide the variable AdditionalData::max_eigenvalue instead. The minimal eigenvalue is implicitly specified via max_eigenvalue/smoothing_range.

Using the PreconditionChebyshev as a solver

If the range [max_eigenvalue/smoothing_range, max_eigenvalue] contains all eigenvalues of the preconditioned matrix system and the degree (i.e., number of iterations) is high enough, this class can also be used as a direct solver. For an error estimation of the Chebyshev iteration that can be used to determine the number of iteration, see Varga (2009).

In order to use Chebyshev as a solver, set the degree to numbers::invalid_unsigned_int to force the automatic computation of the number of iterations needed to reach a given target tolerance. In this case, the target tolerance is read from the variable PreconditionChebyshev::AdditionalData::smoothing_range (it needs to be a number less than one to force any iterations obviously).

For details on the algorithm, see section 5.1 of

@book{Varga2009,
Title = {Matrix iterative analysis},
Author = {Varga, R. S.},
Publisher = {Springer},
Address = {Berlin},
Edition = {2nd},
Year = {2009},
}

Requirements on the templated classes

The class MatrixType must be derived from Subscriptor because a SmartPointer to MatrixType is held in the class. In particular, this means that the matrix object needs to persist during the lifetime of PreconditionChebyshev. The preconditioner is held in a shared_ptr that is copied into the AdditionalData member variable of the class, so the variable used for initialization can safely be discarded after calling initialize(). Both the matrix and the preconditioner need to provide vmult() functions for the matrix-vector product and m() functions for accessing the number of rows in the (square) matrix. Furthermore, the matrix must provide el(i,i) methods for accessing the matrix diagonal in case the preconditioner type is DiagonalMatrix. Even though it is highly recommended to pass the inverse diagonal entries inside a separate preconditioner object for implementing the Jacobi method (which is the only possible way to operate this class when computing in parallel with MPI because there is no knowledge about the locally stored range of entries that would be needed from the matrix alone), there is a backward compatibility function that can extract the diagonal in case of a serial computation.

Author
Martin Kronbichler, 2009, 2016; extension for full compatibility with LinearOperator class: Jean-Paul Pelteret, 2015

Definition at line 1008 of file precondition.h.

Member Typedef Documentation

◆ size_type

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
using PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::size_type = types::global_dof_index

Declare type for container size.

Definition at line 1014 of file precondition.h.

Member Function Documentation

◆ initialize()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::initialize ( const MatrixType &  matrix,
const AdditionalData additional_data = AdditionalData() 
)

Initialize function. Takes the matrix which is used to form the preconditioner, and additional flags if there are any. This function works only if the input matrix has an operator el(i,i) for accessing all the elements in the diagonal. Alternatively, the diagonal can be supplied with the help of the AdditionalData field.

This function calculates an estimate of the eigenvalue range of the matrix weighted by its diagonal using a modified CG iteration in case the given number of iterations is positive.

◆ vmult()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::vmult ( VectorType &  dst,
const VectorType &  src 
) const

Compute the action of the preconditioner on src, storing the result in dst.

◆ Tvmult()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::Tvmult ( VectorType &  dst,
const VectorType &  src 
) const

Compute the action of the transposed preconditioner on src, storing the result in dst.

◆ step()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::step ( VectorType &  dst,
const VectorType &  src 
) const

Perform one step of the preconditioned Richardson iteration.

◆ Tstep()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::Tstep ( VectorType &  dst,
const VectorType &  src 
) const

Perform one transposed step of the preconditioned Richardson iteration.

◆ clear()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::clear ( )

Resets the preconditioner.

◆ m()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
size_type PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::m ( ) const

Return the dimension of the codomain (or range) space. Note that the matrix is of dimension \(m \times n\).

◆ n()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
size_type PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::n ( ) const

Return the dimension of the domain space. Note that the matrix is of dimension \(m \times n\).

◆ estimate_eigenvalues()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::estimate_eigenvalues ( const VectorType &  src) const
private

Initializes the factors theta and delta based on an eigenvalue computation. If the user set provided values for the largest eigenvalue in AdditionalData, no computation is performed and the information given by the user is used.

Member Data Documentation

◆ matrix_ptr

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
SmartPointer< const MatrixType, PreconditionChebyshev<MatrixType, VectorType, PreconditionerType> > PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::matrix_ptr
private

A pointer to the underlying matrix.

Definition at line 1169 of file precondition.h.

◆ solution_old

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
VectorType PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::solution_old
mutableprivate

Internal vector used for the vmult operation.

Definition at line 1174 of file precondition.h.

◆ temp_vector1

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
VectorType PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::temp_vector1
mutableprivate

Internal vector used for the vmult operation.

Definition at line 1179 of file precondition.h.

◆ temp_vector2

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
VectorType PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::temp_vector2
mutableprivate

Internal vector used for the vmult operation.

Definition at line 1184 of file precondition.h.

◆ data

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
AdditionalData PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::data
private

Stores the additional data passed to the initialize function, obtained through a copy operation.

Definition at line 1190 of file precondition.h.

◆ theta

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
double PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::theta
private

Average of the largest and smallest eigenvalue under consideration.

Definition at line 1195 of file precondition.h.

◆ delta

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
double PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::delta
private

Half the interval length between the largest and smallest eigenvalue under consideration.

Definition at line 1201 of file precondition.h.

◆ eigenvalues_are_initialized

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
bool PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::eigenvalues_are_initialized
private

Stores whether the preconditioner has been set up and eigenvalues have been computed.

Definition at line 1207 of file precondition.h.

◆ mutex

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
Threads::Mutex PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::mutex
mutableprivate

A mutex to avoid that multiple vmult() invocations by different threads overwrite the temporary vectors.

Definition at line 1213 of file precondition.h.


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