Reference documentation for deal.II version Git c92c73f660 20210612 09:30:03 +0200

#include <deal.II/lac/precondition.h>
Classes  
struct  AdditionalData 
struct  EigenvalueInformation 
Public Types  
using  size_type = types::global_dof_index 
Public Member Functions  
PreconditionChebyshev ()  
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 
EigenvalueInformation  estimate_eigenvalues (const VectorType &src) const 
template<class Archive >  
void  serialize (Archive &ar, const unsigned int version) 
Subscriptor functionality  
Classes derived from Subscriptor provide a facility to subscribe to this object. This is mostly used by the SmartPointer class.  
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 
Static Public Member Functions  
static ::ExceptionBase &  ExcInUse (int arg1, std::string arg2, std::string arg3) 
static ::ExceptionBase &  ExcNoSubscriber (std::string arg1, std::string arg2) 
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 
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 threeterm recurrence:
\[ x^{n+1} = x^{n} + \rho_n \rho_{n1} (x^{n}  x^{n1}) + \frac{\rho_n}{\lambda_{\max{}}\lambda_{\min{}}} P^{1} (bAx^n). \]
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 \(\rho_n = \left(2\frac{\lambda_{\max{}}+\lambda_{\min{}}} {\lambda_{\max{}}\lambda_{\min{}}}  \rho_{n1}\right)^{1}\). 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 blockJacobi 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 matrixfree computations. In that context, this class can be used as a multigrid smoother that is trivially parallel (assuming that matrixvector products are parallel and the inner preconditioner is parallel). Its use is demonstrated in the step37 and step59 tutorial programs.
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 required. 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 highfrequency content again.
The computation of eigenvalues happens the first time one of the vmult(), Tvmult(), step() or Tstep() functions is called or when estimate_eigenvalues() is called directly. In the latter case, it is necessary to provide a temporary vector of the same layout as the source and destination vectors used during application of the preconditioner.
The estimates for minimum and maximum eigenvalue are taken from SolverCG (even if the solver did not converge in the requested number of iterations). Finally, the maximum eigenvalue is multiplied by a safety factor of 1.2.
Due to the cost of the eigenvalue estimate, 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.
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
.
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
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 matrixvector 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.
Definition at line 991 of file precondition.h.
using PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::size_type = types::global_dof_index 
Declare type for container size.
Definition at line 997 of file precondition.h.
PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::PreconditionChebyshev  (  ) 
Constructor.
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.
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
.
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
.
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::step  (  VectorType &  dst, 
const VectorType &  src  
)  const 
Perform one step of the preconditioned Richardson iteration.
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::Tstep  (  VectorType &  dst, 
const VectorType &  src  
)  const 
Perform one transposed step of the preconditioned Richardson iteration.
void PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::clear  (  ) 
Resets the preconditioner.
size_type PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::m  (  )  const 
Return the dimension of the codomain (or range) space. Note that the matrix is of dimension \(m \times n\).
size_type PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::n  (  )  const 
Return the dimension of the domain space. Note that the matrix is of dimension \(m \times n\).
EigenvalueInformation PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::estimate_eigenvalues  (  const VectorType &  src  )  const 
Compute eigenvalue estimates required for the preconditioner.
This function is called automatically on first use of the preconditioner if it is not called by the user. The layout of the vector src
is used to create internal temporary vectors and its content does not matter.
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.

inherited 
Subscribes a user of the object by storing the pointer validity
. The subscriber may be identified by text supplied as identifier
.
Definition at line 136 of file subscriptor.cc.

inherited 
Unsubscribes a user from the object.
identifier
and the validity
pointer must be the same as the one supplied to subscribe(). Definition at line 156 of file subscriptor.cc.

inlineinherited 
Return the present number of subscriptions to this object. This allows to use this class for reference counted lifetime determination where the last one to unsubscribe also deletes the object.
Definition at line 301 of file subscriptor.h.

inlineinherited 
List the subscribers to the input stream
.
Definition at line 318 of file subscriptor.h.

inherited 
List the subscribers to deallog
.
Definition at line 204 of file subscriptor.cc.

inlineinherited 
Read or write the data of this object to or from a stream for the purpose of serialization using the BOOST serialization library.
This function does not actually serialize any of the member variables of this class. The reason is that what this class stores is only who subscribes to this object, but who does so at the time of storing the contents of this object does not necessarily have anything to do with who subscribes to the object when it is restored. Consequently, we do not want to overwrite the subscribers at the time of restoring, and then there is no reason to write the subscribers out in the first place.
Definition at line 310 of file subscriptor.h.

private 
A pointer to the underlying matrix.
Definition at line 1202 of file precondition.h.

mutableprivate 
Internal vector used for the vmult
operation.
Definition at line 1207 of file precondition.h.

mutableprivate 
Internal vector used for the vmult
operation.
Definition at line 1212 of file precondition.h.

mutableprivate 
Internal vector used for the vmult
operation.
Definition at line 1217 of file precondition.h.

private 
Stores the additional data passed to the initialize function, obtained through a copy operation.
Definition at line 1223 of file precondition.h.

private 
Average of the largest and smallest eigenvalue under consideration.
Definition at line 1228 of file precondition.h.

private 
Half the interval length between the largest and smallest eigenvalue under consideration.
Definition at line 1234 of file precondition.h.

private 
Stores whether the preconditioner has been set up and eigenvalues have been computed.
Definition at line 1240 of file precondition.h.

mutableprivate 
A mutex to avoid that multiple vmult() invocations by different threads overwrite the temporary vectors.
Definition at line 1246 of file precondition.h.