deal.II version GIT relicensing1927g3de9220933 20241003 08:40:00+00:00

#include <deal.II/lac/precondition.h>
Classes  
struct  AdditionalData 
Public Types  
using  size_type = types::global_dof_index 
using  EigenvalueInformation = internal::EigenvalueInformation 
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 ObserverPointer 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 Types  
using  map_value_type = decltype(counter_map)::value_type 
using  map_iterator = decltype(counter_map)::iterator 
Private Member Functions  
void  check_no_subscribers () const noexcept 
Private Attributes  
ObserverPointer< 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 
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 
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:
\[ x^{n+1} = x^{n} + \alpha^n_0 (x^{n}  x^{n1}) + \alpha^n_1 P^{1} (bAx^n) \quad\text{with}\quad \alpha^0_0 := 0,\; \alpha^0_1 := \frac{2\rho_0}{\lambda_{\max}\lambda_{\min}}\; \alpha^n_0 := \rho_n \rho_{n1},\;\text{and}\; \alpha^n_1 := \frac{4\rho_n}{\lambda_{\max}\lambda_{\min}}, \]
where the parameter \(\rho_0\) is set to \(\rho_0 = \frac{\lambda_{\max{}}\lambda_{\min{}}}{\lambda_{\max{}}+\lambda_{\min{}}}\) 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(). This class offers several algorithms to this end, see PreconditionChebyshev::AdditionalData::EigenvalueAlgorithm. The default algorithm invokes the Lanczos method via the SolverCG class, which requires symmetry and positive definiteness of the (preconditioned) matrix system are required. Also note that deal.II needs to be configured with LAPACK support to use this option. The eigenvalue algorithm can be controlled by PreconditionChebyshev::AdditionalData::eig_cg_n_iterations specifying how many iterations should be performed. For all algorithms, the iterative process is 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 the underlying solver or eigenvalue algorithm in the given number of iterations, 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 a bad quality, e.g. when the polynomial basis or numbering of unknowns is such that the initial vector described above is a bad choice. 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 [208].
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. Note that this currently only works for symmetric positive definite matrices with the eigenvalue algorithm set to the conjugate gradient algorithm. 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 [208].
The class MatrixType
must be derived from Subscriptor because a ObserverPointer 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.
MatrixType
argumentThis class enables to embed the vector updates into the matrixvector product in case the MatrixType
supports this. To this end, the VectorType
needs to be of type LinearAlgebra::distributed::Vector, the PreconditionerType
needs to be DiagonalMatrix, and the class MatrixType
needs to provide a function with the signature
where the two given functions run before and after the matrixvector product, respectively. They take as arguments a subrange among the locally owned elements of the vector, defined as halfopen intervals. The intervals are designed to be scheduled close to the time the matrixvector product touches upon the entries in the src
and dst
vectors, respectively, with the requirement that
src
or dst
once the operation_before_matrix_vector_product
has been run on that vector entry; operation_after_matrix_vector_product
may run on a range of entries [i,j)
once the matrixvector product does not access the entries [i,j)
in src
and dst
any more. The motivation for this function is to increase data locality and hence cache usage. For the example of a class similar to the one in the step37 tutorial program, the implementation is
In terms of the Chebyshev iteration, the operation before the loop will set dst
to zero, whereas the operation after the loop performs the iteration leading to \(x^{n+1}\) described above, modifying the dst
and src
vectors.
Definition at line 2103 of file precondition.h.
using PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::size_type = types::global_dof_index 
Declare type for container size.
Definition at line 2109 of file precondition.h.
using PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::EigenvalueInformation = internal::EigenvalueInformation 
Definition at line 2236 of file precondition.h.

privateinherited 
The data type used in counter_map.
Definition at line 230 of file subscriptor.h.

privateinherited 
The iterator type used in counter_map.
Definition at line 235 of file subscriptor.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 130 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 150 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 198 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.

privatenoexceptinherited 
Check that there are no objects subscribing to this object. If this check passes then it is safe to destroy the current object. It this check fails then this function will either abort or print an error message to deallog (by using the AssertNothrow mechanism), but will not throw an exception.
Definition at line 52 of file subscriptor.cc.

private 
A pointer to the underlying matrix.
Definition at line 2260 of file precondition.h.

mutableprivate 
Internal vector used for the vmult
operation.
Definition at line 2265 of file precondition.h.

mutableprivate 
Internal vector used for the vmult
operation.
Definition at line 2270 of file precondition.h.

mutableprivate 
Internal vector used for the vmult
operation.
Definition at line 2275 of file precondition.h.

private 
Stores the additional data passed to the initialize function, obtained through a copy operation.
Definition at line 2281 of file precondition.h.

private 
Average of the largest and smallest eigenvalue under consideration.
Definition at line 2286 of file precondition.h.

private 
Half the interval length between the largest and smallest eigenvalue under consideration.
Definition at line 2292 of file precondition.h.

private 
Stores whether the preconditioner has been set up and eigenvalues have been computed.
Definition at line 2298 of file precondition.h.

mutableprivate 
A mutex to avoid that multiple vmult() invocations by different threads overwrite the temporary vectors.
Definition at line 2304 of file precondition.h.

mutableprivateinherited 
Store the number of objects which subscribed to this object. Initially, this number is zero, and upon destruction it shall be zero again (i.e. all objects which subscribed should have unsubscribed again).
The creator (and owner) of an object is counted in the map below if HE manages to supply identification.
We use the mutable
keyword in order to allow subscription to constant objects also.
This counter may be read from and written to concurrently in multithreaded code: hence we use the std::atomic
class template.
Definition at line 219 of file subscriptor.h.

mutableprivateinherited 
In this map, we count subscriptions for each different identification string supplied to subscribe().
Definition at line 225 of file subscriptor.h.

mutableprivateinherited 
In this vector, we store pointers to the validity bool in the ObserverPointer objects that subscribe to this class.
Definition at line 241 of file subscriptor.h.

mutableprivateinherited 
Pointer to the typeinfo object of this object, from which we can later deduce the class name. Since this information on the derived class is neither available in the destructor, nor in the constructor, we obtain it in between and store it here.
Definition at line 249 of file subscriptor.h.