Reference documentation for deal.II version 8.1.0
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Namespaces | Classes | Enumerations
Collaboration diagram for Basic matrices:

Namespaces

 SparseMatrixIterators
 
 ChunkSparseMatrixIterators
 
 BlockMatrixIterators
 

Classes

class  PETScWrappers::MPI::SparseMatrix
 
struct  PETScWrappers::MPI::SparseMatrix::Traits
 
class  PETScWrappers::MatrixBase
 
struct  PETScWrappers::MatrixBase::LastAction
 
singleton  FullMatrix< number >
 
class  FullMatrix< number >::Accessor
 
class  FullMatrix< number >::const_iterator
 
singleton  TridiagonalMatrix< number >
 
class  PETScWrappers::MPI::BlockSparseMatrix
 
class  SparseMatrixIterators::Accessor< number, true >
 
class  SparseMatrixIterators::Accessor< number, false >
 
class  SparseMatrixIterators::Accessor< number, false >::Reference
 
class  SparseMatrixIterators::Iterator< number, Constness >
 
class  ChunkSparseMatrix< number >
 
class  BlockMatrixIterators::AccessorBase< BlockMatrix >
 
class  BlockMatrixIterators::Accessor< BlockMatrix, false >
 
class  BlockMatrixIterators::Accessor< BlockMatrix, true >
 
class  BlockMatrixBase< MatrixType >
 
struct  BlockMatrixBase< MatrixType >::TemporaryData
 
class  SparseMatrixEZ< number >
 
struct  SparseMatrixEZ< number >::Entry
 
struct  SparseMatrixEZ< number >::RowInfo
 
class  IdentityMatrix
 
class  BlockSparseMatrixEZ< Number >
 
class  PETScWrappers::MatrixFree
 
class  LAPACKFullMatrix< number >
 

Enumerations

enum  Values { none, insert, add }
 

Detailed Description

These are the actual matrix classes provided by deal.II. It is possible to store values in them and retrieve them. Furthermore, they provide the full interface required by linear solvers (see Linear solver classes).

Among the matrices in this group are full matrices, different sparse matrices, and block matrices. In addition, some of the classes in the interfaces to other linear algebra libraries (for example the PETScWrappers) are matrices.

Most of the deal.II sparse matrix classes are separated from their sparsity patterns, to make storing several matrices with the same sparsity pattern more efficient. See Sparsity patterns for more information.