Ifpack2 Templated Preconditioning Package Version 1.0
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
Ifpack2::BlockRelaxation< MatrixType, ContainerType >Ifpack2::BlockRelaxation: defines relaxation preconditioners for Tpetra::RowMatrix objects
Ifpack2::BorderedOperator< Scalar, LocalOrdinal, GlobalOrdinal, Node >Ifpack2 bordered operator
Ifpack2::Chebyshev< MatrixType >A class for preconditioning with Chebyshev polynomials
Ifpack2::Diagonal< MatrixType >A class for diagonal preconditioning
Ifpack2::DiagonalFilter< MatrixType >Ifpack2_DiagonalFilter: Filter to modify the diagonal entries of a given Tpetra_RowMatrix
Ifpack2::DropFilter< MatrixType >Ifpack2::DropFilter: Filter based on matrix entries
Ifpack2::FactoryA factory class to create Ifpack2 preconditioners
Ifpack2::IlukGraph< LocalOrdinal, GlobalOrdinal, Node >A class for constructing level filled graphs for use with ILU(k) class preconditioners
Ifpack2::ILUT< MatrixType >A class for constructing and using an ILUT factorization
Ifpack2::LinearPartitioner< GraphType >Ifpack2::LinearPartitioner: A class to define linear partitions
Ifpack2::LocalFilter< MatrixType >Ifpack2_LocalFilter a class for light-weight extraction of the submatrix corresponding to local rows and columns
Ifpack2::OverlapGraph< LocalOrdinal, GlobalOrdinal, Node >Ifpack2::OverlapGraph constructs an overlapped graph
Ifpack2::Partitioner< GraphType >Ifpack2::Partitioner: A class to decompose local Ifpack2::Graph objects
Ifpack2::Preconditioner< Scalar, LocalOrdinal, GlobalOrdinal, Node >Base class for all Ifpack2 preconditioners
Ifpack2::Relaxation< MatrixType >Ifpack2::Relaxation: defines relaxation preconditioners for Tpetra::RowMatrix objects
Ifpack2::RILUK< MatrixType >A class for constructing and using an incomplete lower/upper (ILU) factorization of a given Tpetra::RowMatrix
Ifpack2::SingletonFilter< MatrixType >Ifpack2_SingletonFilter: Filter based on matrix entries
Ifpack2::SparsityFilter< MatrixType >Ifpack2_SparsityFilter: a class to drop based on sparsity
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