Reference documentation for deal.II version 9.6.0
\(\newcommand{\dealvcentcolon}{\mathrel{\mathop{:}}}\) \(\newcommand{\dealcoloneq}{\dealvcentcolon\mathrel{\mkern-1.2mu}=}\) \(\newcommand{\jump}[1]{\left[\!\left[ #1 \right]\!\right]}\) \(\newcommand{\average}[1]{\left\{\!\left\{ #1 \right\}\!\right\}}\)
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Public Types | Public Member Functions | Public Attributes | List of all members
PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::AdditionalData Struct Reference

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

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

Public Types

enum class  PolynomialType { first_kind , fourth_kind }
 
using EigenvalueAlgorithm = internal::EigenvalueAlgorithm
 

Public Member Functions

 AdditionalData (const unsigned int degree=1, const double smoothing_range=0., const unsigned int eig_cg_n_iterations=8, const double eig_cg_residual=1e-2, const double max_eigenvalue=1, const EigenvalueAlgorithm eigenvalue_algorithm=EigenvalueAlgorithm::lanczos, const PolynomialType polynomial_type=PolynomialType::first_kind)
 

Public Attributes

unsigned int degree
 
PolynomialType polynomial_type
 
double smoothing_range
 
unsigned int eig_cg_n_iterations
 
double eig_cg_residual
 
double max_eigenvalue
 
::AffineConstraints< double > constraints
 
EigenvalueAlgorithm eigenvalue_algorithm
 
std::shared_ptr< PreconditionerType > preconditioner
 

Detailed Description

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

Standardized data struct to pipe additional parameters to the preconditioner.

Definition at line 2116 of file precondition.h.

Member Typedef Documentation

◆ EigenvalueAlgorithm

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
using PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::AdditionalData::EigenvalueAlgorithm = internal::EigenvalueAlgorithm

Definition at line 2119 of file precondition.h.

Member Enumeration Documentation

◆ PolynomialType

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
enum class PreconditionChebyshev::AdditionalData::PolynomialType
strong

An enum to define the available types of polynomial types.

Enumerator
first_kind 

First-kind Chebyshev polynomials.

fourth_kind 

Fourth-kind Chebyshev polynomials according to [148] and [175].

Definition at line 2124 of file precondition.h.

Constructor & Destructor Documentation

◆ AdditionalData()

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::AdditionalData::AdditionalData ( const unsigned int degree = 1,
const double smoothing_range = 0.,
const unsigned int eig_cg_n_iterations = 8,
const double eig_cg_residual = 1e-2,
const double max_eigenvalue = 1,
const EigenvalueAlgorithm eigenvalue_algorithm = EigenvalueAlgorithm::lanczos,
const PolynomialType polynomial_type = PolynomialType::first_kind )

Constructor.

Member Data Documentation

◆ degree

template<typename MatrixType = SparseMatrix<double>, typename VectorType = Vector<double>, typename PreconditionerType = DiagonalMatrix<VectorType>>
unsigned int PreconditionChebyshev< MatrixType, VectorType, PreconditionerType >::AdditionalData::degree

This determines the degree of the Chebyshev polynomial. The degree of the polynomial gives the number of matrix-vector products to be performed for one application of the step() operation. During vmult(), the method performs (degree-1) matrix-vector products. Degree one corresponds to a damped Jacobi method.

If the degree is set to numbers::invalid_unsigned_int, the algorithm will automatically determine the number of necessary iterations based on the usual Chebyshev error formula as mentioned in the discussion of the main class.

Definition at line 2162 of file precondition.h.

◆ polynomial_type

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

Specifies the polynomial type to be used.

Definition at line 2167 of file precondition.h.

◆ smoothing_range

template<typename PreconditionerType >
double internal::EigenvalueAlgorithmAdditionalData< PreconditionerType >::smoothing_range
inherited

This sets the range between the largest eigenvalue in the matrix and the smallest eigenvalue to be treated. If the parameter is set to a number less than 1, an estimate for the largest and for the smallest eigenvalue will be calculated internally. For a smoothing range larger than one, the preconditioner will act in the interval \([\lambda_\mathrm{max}/ \tt{smoothing\_range}, \lambda_\mathrm{max}]\), where \(\lambda_\mathrm{max}\) is an estimate of the maximum eigenvalue of the matrix. A choice of smoothing_range between 5 and 20 is useful in case the preconditioner is used as a smoother in multigrid.

Definition at line 158 of file precondition.h.

◆ eig_cg_n_iterations

template<typename PreconditionerType >
unsigned int internal::EigenvalueAlgorithmAdditionalData< PreconditionerType >::eig_cg_n_iterations
inherited

Maximum number of CG iterations performed for finding the maximum eigenvalue. If set to zero, no computations are performed. Instead, the user must supply a largest eigenvalue via the variable PreconditionRelaxation::AdditionalData::max_eigenvalue.

Definition at line 166 of file precondition.h.

◆ eig_cg_residual

template<typename PreconditionerType >
double internal::EigenvalueAlgorithmAdditionalData< PreconditionerType >::eig_cg_residual
inherited

Tolerance for iterations performed for finding the maximum eigenvalue by the eigenvalue algorithm (Lanczos or power iteration).

Definition at line 172 of file precondition.h.

◆ max_eigenvalue

template<typename PreconditionerType >
double internal::EigenvalueAlgorithmAdditionalData< PreconditionerType >::max_eigenvalue
inherited

Maximum eigenvalue to work with. Only in effect if eig_cg_n_iterations is set to zero, otherwise this parameter is ignored.

Definition at line 179 of file precondition.h.

◆ constraints

template<typename PreconditionerType >
::AffineConstraints<double> internal::EigenvalueAlgorithmAdditionalData< PreconditionerType >::constraints
inherited

Constraints to be used for the operator given. This variable is used to zero out the correct entries when creating an initial guess.

Definition at line 185 of file precondition.h.

◆ eigenvalue_algorithm

template<typename PreconditionerType >
EigenvalueAlgorithm internal::EigenvalueAlgorithmAdditionalData< PreconditionerType >::eigenvalue_algorithm
inherited

Stores the preconditioner object that the Chebyshev is wrapped around.

Definition at line 190 of file precondition.h.

◆ preconditioner

template<typename PreconditionerType >
std::shared_ptr<PreconditionerType> internal::EigenvalueAlgorithmAdditionalData< PreconditionerType >::preconditioner
inherited

Preconditioner.

Definition at line 195 of file precondition.h.

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