Ginkgo Generated from branch based on main. Ginkgo version 1.11.0
A numerical linear algebra library targeting many-core architectures
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Class Hierarchy

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This inheritance list is sorted roughly, but not completely, alphabetically:
 Cgko::AbsoluteComputableThe AbsoluteComputable is an interface that allows to get the component wise absolute of a LinOp
  Cgko::EnableAbsoluteComputation< remove_complex< Vector< double > > >
   Cgko::experimental::distributed::Vector< ValueType >Vector is a format which explicitly stores (multiple) distributed column vectors in a dense storage format
  Cgko::EnableAbsoluteComputation< remove_complex< Coo< default_precision, int32 > > >
   Cgko::matrix::Coo< ValueType, IndexType >COO stores a matrix in the coordinate matrix format
  Cgko::EnableAbsoluteComputation< remove_complex< Csr< default_precision, int32 > > >
   Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::EnableAbsoluteComputation< remove_complex< Dense< default_precision > > >
   Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::EnableAbsoluteComputation< remove_complex< Diagonal< default_precision > > >
   Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
  Cgko::EnableAbsoluteComputation< remove_complex< Ell< default_precision, int32 > > >
   Cgko::matrix::Ell< ValueType, IndexType >ELL is a matrix format where stride with explicit zeros is used such that all rows have the same number of stored elements
  Cgko::EnableAbsoluteComputation< remove_complex< Fbcsr< default_precision, int32 > > >
   Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
  Cgko::EnableAbsoluteComputation< remove_complex< Hybrid< default_precision, int32 > > >
   Cgko::matrix::Hybrid< ValueType, IndexType >HYBRID is a matrix format which splits the matrix into ELLPACK and COO format
  Cgko::EnableAbsoluteComputation< remove_complex< Sellp< default_precision, int32 > > >
   Cgko::matrix::Sellp< ValueType, IndexType >SELL-P is a matrix format similar to ELL format
  Cgko::EnableAbsoluteComputation< AbsoluteLinOp >The EnableAbsoluteComputation mixin provides the default implementations of compute_absolute_linop and the absolute interface
   Cgko::experimental::distributed::Vector< ValueType >
   Cgko::experimental::distributed::Vector< to_complex< value_type > >
   Cgko::experimental::distributed::Vector< previous_precision< ValueType > >
   Cgko::matrix::Coo< ValueType, IndexType >
   Cgko::matrix::Csr< value_type, index_type >
   Cgko::matrix::Csr< ValueType, IndexType >
   Cgko::matrix::Dense< ValueType >
   Cgko::matrix::Dense< value_type >
   Cgko::matrix::Dense< to_complex< ValueType > >
   Cgko::matrix::Dense< absolute_type >
   Cgko::matrix::Dense< remove_complex< ValueType > >
   Cgko::matrix::Diagonal< ValueType >
   Cgko::matrix::Ell< value_type, index_type >
   Cgko::matrix::Ell< ValueType, IndexType >
   Cgko::matrix::Fbcsr< ValueType, IndexType >
 Cgko::AllocatorProvides generic allocation and deallocation functionality to be used by an Executor
  Cgko::CpuAllocatorBaseImplement this interface to provide an allocator for OmpExecutor or ReferenceExecutor
   Cgko::CpuAllocatorAllocator using new/delete
   Cgko::CudaHostAllocator
   Cgko::CudaUnifiedAllocator
   Cgko::HipHostAllocator
   Cgko::HipUnifiedAllocator
  Cgko::CudaAllocatorBaseImplement this interface to provide an allocator for CudaExecutor
   Cgko::CudaAllocatorAllocator using cudaMalloc
   Cgko::CudaAsyncAllocator
   Cgko::CudaHostAllocator
   Cgko::CudaUnifiedAllocator
  Cgko::HipAllocatorBaseImplement this interface to provide an allocator for HipExecutor
   Cgko::HipAllocator
   Cgko::HipAsyncAllocator
   Cgko::HipHostAllocator
   Cgko::HipUnifiedAllocator
 Cgko::amd_deviceAmd_device handles the number of executor on Amd devices and have the corresponding recursive_mutex
 Cgko::solver::ApplyWithInitialGuessApplyWithInitialGuess provides a way to give the input guess for apply function
  Cgko::solver::EnableApplyWithInitialGuess< Chebyshev< default_precision > >
   Cgko::solver::Chebyshev< ValueType >Chebyshev iteration is an iterative method for solving nonsymmetric problems based on some knowledge of the spectrum of the (preconditioned) system matrix
  Cgko::solver::EnableApplyWithInitialGuess< Multigrid >
   Cgko::solver::MultigridMultigrid methods have a hierarchy of many levels, whose corase level is a subset of the fine level, of the problem
  Cgko::solver::EnableApplyWithInitialGuess< DerivedType >EnableApplyWithInitialGuess providing default operation for ApplyWithInitialGuess with correct validation and log
   Cgko::solver::Chebyshev< ValueType >
 Cgko::array< ValueType >An array is a container which encapsulates fixed-sized arrays, stored on the Executor tied to the array
 Cgko::device_matrix_data< ValueType, IndexType >::arraysStores the internal arrays of a device_matrix_data object
 Cgko::batch_dim< Dimensionality, DimensionType >A type representing the dimensions of a multidimensional batch object
 Cgko::batch::solver::BatchSolverThe BatchSolver is a base class for all batched solvers and provides the common getters and setter for these batched solver classes
  Cgko::batch::solver::EnableBatchSolver< Bicgstab< default_precision >, default_precision >
   Cgko::batch::solver::Bicgstab< ValueType >BiCGSTAB or the Bi-Conjugate Gradient-Stabilized is a Krylov subspace solver
  Cgko::batch::solver::EnableBatchSolver< Cg< default_precision >, default_precision >
   Cgko::batch::solver::Cg< ValueType >Cg or the Conjugate Gradient is a Krylov subspace solver
  Cgko::batch::solver::EnableBatchSolver< ConcreteSolver, ValueType, PolymorphicBase >This mixin provides apply and common iterative solver functionality to all the batched solvers
 Cgko::bfloat16A class providing basic support for bfloat16 precision floating point types
 Cgko::preconditioner::block_interleaved_storage_scheme< IndexType >Defines the parameters of the interleaved block storage scheme used by block-Jacobi blocks
 Cgko::experimental::mpi::CollectiveCommunicatorInterface for a collective communicator
  Cgko::experimental::mpi::DenseCommunicatorA CollectiveCommunicator that uses a dense communication
  Cgko::experimental::mpi::NeighborhoodCommunicatorA CollectiveCommunicator that uses a neighborhood topology
 Cgko::experimental::mpi::communicatorA thin wrapper of MPI_Comm that supports most MPI calls
 Cgko::experimental::mpi::contiguous_typeA move-only wrapper for a contiguous MPI_Datatype
 CConvertibleTo
  Cgko::EnablePolymorphicAssignment< BlockOperator >
   Cgko::EnableLinOp< BlockOperator >
    Cgko::BlockOperatorA BlockOperator represents a linear operator that is partitioned into multiple blocks
  Cgko::EnablePolymorphicAssignment< Combination< default_precision > >
   Cgko::EnableLinOp< Combination< default_precision > >
    Cgko::Combination< ValueType >The Combination class can be used to construct a linear combination of multiple linear operators c1 * op1 + c2 * op2 + ... + ck * opk
  Cgko::EnablePolymorphicAssignment< Composition< default_precision > >
   Cgko::EnableLinOp< Composition< default_precision > >
    Cgko::Composition< default_precision >
     Cgko::factorization::ParIc< ValueType, IndexType >ParIC is an incomplete Cholesky factorization which is computed in parallel
     Cgko::factorization::ParIct< ValueType, IndexType >ParICT is an incomplete threshold-based Cholesky factorization which is computed in parallel
     Cgko::factorization::ParIlu< ValueType, IndexType >ParILU is an incomplete LU factorization which is computed in parallel
     Cgko::factorization::ParIlut< ValueType, IndexType >ParILUT is an incomplete threshold-based LU factorization which is computed in parallel
    Cgko::Composition< ValueType >The Composition class can be used to compose linear operators op1, op2, ..., opn and obtain the operator op1 * op2 * ... * opn
  Cgko::EnablePolymorphicAssignment< ConcreteFactory >
   Cgko::EnableDefaultFactory< ConcreteFactory, ConcreteBatchLinOp, ParametersType, PolymorphicBase >
    Cgko::batch::preconditioner::Jacobi< ValueType, IndexType >::Factory
    Cgko::batch::solver::Bicgstab< ValueType >::Factory
    Cgko::batch::solver::Cg< ValueType >::Factory
   Cgko::EnableDefaultFactory< ConcreteFactory, ConcreteLinOp, ParametersType, PolymorphicBase >
    Cgko::experimental::distributed::preconditioner::Schwarz< ValueType, LocalIndexType, GlobalIndexType >::Factory
    Cgko::experimental::reorder::ScaledReordered< ValueType, IndexType >::Factory
    Cgko::experimental::solver::Direct< ValueType, IndexType >::Factory
    Cgko::factorization::Ic< ValueType, IndexType >::Factory
    Cgko::factorization::Ilu< ValueType, IndexType >::Factory
    Cgko::factorization::ParIc< ValueType, IndexType >::Factory
    Cgko::factorization::ParIct< ValueType, IndexType >::Factory
    Cgko::factorization::ParIlu< ValueType, IndexType >::Factory
    Cgko::factorization::ParIlut< ValueType, IndexType >::Factory
    Cgko::multigrid::FixedCoarsening< ValueType, IndexType >::Factory
    Cgko::multigrid::Pgm< ValueType, IndexType >::Factory
    Cgko::preconditioner::Ic< LSolverTypeOrValueType, IndexType >::Factory
    Cgko::preconditioner::Ilu< LSolverTypeOrValueType, USolverTypeOrValueType, ReverseApply, IndexType >::Factory
    Cgko::preconditioner::Isai< IsaiType, ValueType, IndexType >::Factory
    Cgko::preconditioner::Jacobi< ValueType, IndexType >::Factory
    Cgko::solver::Bicg< ValueType >::Factory
    Cgko::solver::Bicgstab< ValueType >::Factory
    Cgko::solver::CbGmres< ValueType >::Factory
    Cgko::solver::Cg< ValueType >::Factory
    Cgko::solver::Cgs< ValueType >::Factory
    Cgko::solver::Chebyshev< ValueType >::Factory
    Cgko::solver::Factory
    Cgko::solver::Fcg< ValueType >::Factory
    Cgko::solver::Gcr< ValueType >::Factory
    Cgko::solver::Gmres< ValueType >::Factory
    Cgko::solver::Idr< ValueType >::Factory
    Cgko::solver::LowerTrs< ValueType, IndexType >::Factory
    Cgko::solver::Minres< ValueType >::Factory
    Cgko::solver::Multigrid::Factory
    Cgko::solver::PipeCg< ValueType >::Factory
    Cgko::solver::UpperTrs< ValueType, IndexType >::Factory
   Cgko::EnableDefaultFactory< ConcreteFactory, ConcreteReorderingBase, ParametersType, PolymorphicBase >
    Cgko::reorder::Rcm< ValueType, IndexType >::Factory
   Cgko::EnableDefaultFactory< ConcreteFactory, ConcreteCriterion, ParametersType, PolymorphicBase >
    Cgko::stop::AbsoluteResidualNorm< ValueType >::Factory
    Cgko::stop::Combined::Factory
    Cgko::stop::ImplicitResidualNorm< ValueType >::Factory
    Cgko::stop::Iteration::Factory
    Cgko::stop::RelativeResidualNorm< ValueType >::Factory
    Cgko::stop::ResidualNorm< ValueType >::Factory
    Cgko::stop::ResidualNormReduction< ValueType >::Factory
    Cgko::stop::Time::Factory
   Cgko::EnableDefaultFactory< ConcreteFactory, ProductType, ParametersType, PolymorphicBase >This mixin provides a default implementation of a concrete factory
  Cgko::EnablePolymorphicAssignment< ConcreteLinOp >
   Cgko::EnableLinOp< ConcreteLinOp, PolymorphicBase >The EnableLinOp mixin can be used to provide sensible default implementations of the majority of the LinOp and PolymorphicObject interface
    Cgko::Combination< ValueType >
    Cgko::Composition< ValueType >
    Cgko::Composition< value_type >
    Cgko::experimental::distributed::Vector< ValueType >
    Cgko::experimental::distributed::Vector< to_complex< value_type > >
    Cgko::experimental::distributed::Vector< previous_precision< ValueType > >
    Cgko::experimental::factorization::Factorization< value_type, index_type >
    Cgko::matrix::Coo< ValueType, IndexType >
    Cgko::matrix::Csr< value_type, index_type >
    Cgko::matrix::Csr< ValueType, IndexType >
    Cgko::matrix::Dense< ValueType >
    Cgko::matrix::Dense< value_type >
    Cgko::matrix::Dense< to_complex< ValueType > >
    Cgko::matrix::Dense< absolute_type >
    Cgko::matrix::Dense< remove_complex< ValueType > >
    Cgko::matrix::Diagonal< ValueType >
    Cgko::matrix::Ell< value_type, index_type >
    Cgko::matrix::Ell< ValueType, IndexType >
    Cgko::matrix::Fbcsr< ValueType, IndexType >
    Cgko::matrix::Identity< ValueType >
    Cgko::matrix::Permutation< index_type >
    Cgko::matrix::Permutation< IndexType >
    Cgko::matrix::SparsityCsr< value_type, index_type >
    Cgko::matrix::SparsityCsr< IndexType, ValueType >
    Cgko::matrix::SparsityCsr< ValueType, IndexType >
    Cgko::multigrid::Pgm< ValueType, IndexType >
    Cgko::preconditioner::Ic< LSolverTypeOrValueType, IndexType >
    Cgko::preconditioner::Jacobi< ValueType, IndexType >
    Cgko::solver::Bicg< ValueType >
    Cgko::solver::Bicgstab< ValueType >
    Cgko::solver::Cg< ValueType >
    Cgko::solver::Cgs< ValueType >
    Cgko::solver::Chebyshev< ValueType >
    Cgko::solver::Fcg< ValueType >
    Cgko::solver::Gcr< ValueType >
    Cgko::solver::Gmres< ValueType >
    Cgko::solver::Idr< ValueType >
    Cgko::solver::LowerTrs< value_type, index_type >
    Cgko::solver::LowerTrs< ValueType, IndexType >
    Cgko::solver::Minres< ValueType >
    Cgko::solver::PipeCg< ValueType >
    Cgko::solver::UpperTrs< value_type, index_type >
    Cgko::solver::UpperTrs< ValueType, IndexType >
  Cgko::EnablePolymorphicAssignment< Perturbation< default_precision > >
   Cgko::EnableLinOp< Perturbation< default_precision > >
    Cgko::Perturbation< ValueType >The Perturbation class can be used to construct a LinOp to represent the operation (identity + scalar * basis * projector)
  Cgko::EnablePolymorphicAssignment< ConcreteBatchLinOp >
   Cgko::batch::EnableBatchLinOp< ConcreteBatchLinOp, PolymorphicBase >The EnableBatchLinOp mixin can be used to provide sensible default implementations of the majority of the BatchLinOp and PolymorphicObject interface
    Cgko::batch::matrix::Csr< ValueType, IndexType >
    Cgko::batch::matrix::Dense< ValueType >
  Cgko::EnablePolymorphicAssignment< MultiVector< default_precision > >
   Cgko::batch::MultiVector< ValueType >MultiVector stores multiple vectors in a batched fashion and is useful for batched operations
  Cgko::EnablePolymorphicAssignment< Csr< default_precision, int32 > >
   Cgko::EnableLinOp< Csr< default_precision, int32 > >
    Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
   Cgko::batch::EnableBatchLinOp< Csr< default_precision, int32 > >
    Cgko::batch::matrix::Csr< ValueType, IndexType >Csr is a general sparse matrix format that stores the column indices for each nonzero entry and a cumulative sum of the number of nonzeros in each row
  Cgko::EnablePolymorphicAssignment< Dense< default_precision > >
   Cgko::EnableLinOp< Dense< default_precision > >
    Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
   Cgko::batch::EnableBatchLinOp< Dense< default_precision > >
    Cgko::batch::matrix::Dense< ValueType >Dense is a batch matrix format which explicitly stores all values of the matrix in each of the batches
  Cgko::EnablePolymorphicAssignment< Ell< default_precision, int32 > >
   Cgko::EnableLinOp< Ell< default_precision, int32 > >
    Cgko::matrix::Ell< ValueType, IndexType >ELL is a matrix format where stride with explicit zeros is used such that all rows have the same number of stored elements
   Cgko::batch::EnableBatchLinOp< Ell< default_precision, int32 > >
    Cgko::batch::matrix::Ell< ValueType, IndexType >Ell is a sparse matrix format that stores the same number of nonzeros in each row, enabling coalesced accesses
  Cgko::EnablePolymorphicAssignment< Identity< default_precision > >
   Cgko::EnableLinOp< Identity< default_precision > >
    Cgko::matrix::Identity< ValueType >This class is a utility which efficiently implements the identity matrix (a linear operator which maps each vector to itself)
   Cgko::batch::EnableBatchLinOp< Identity< default_precision > >
    Cgko::batch::matrix::Identity< ValueType >The batch Identity matrix, which represents a batch of Identity matrices
  Cgko::EnablePolymorphicAssignment< Jacobi< default_precision, int32 > >
   Cgko::EnableLinOp< Jacobi< default_precision, int32 > >
    Cgko::preconditioner::Jacobi< ValueType, IndexType >A block-Jacobi preconditioner is a block-diagonal linear operator, obtained by inverting the diagonal blocks of the source operator
   Cgko::batch::EnableBatchLinOp< Jacobi< default_precision, int32 > >
    Cgko::batch::preconditioner::Jacobi< ValueType, IndexType >A block-Jacobi preconditioner is a block-diagonal linear operator, obtained by inverting the diagonal blocks (stored in a dense row major fashion) of the source operator
  Cgko::EnablePolymorphicAssignment< Bicgstab< default_precision > >
   Cgko::EnableLinOp< Bicgstab< default_precision > >
    Cgko::solver::Bicgstab< ValueType >BiCGSTAB or the Bi-Conjugate Gradient-Stabilized is a Krylov subspace solver
   Cgko::batch::EnableBatchLinOp< Bicgstab< default_precision >, BatchLinOp >
    Cgko::batch::solver::EnableBatchSolver< Bicgstab< default_precision >, default_precision >
  Cgko::EnablePolymorphicAssignment< Cg< default_precision > >
   Cgko::EnableLinOp< Cg< default_precision > >
    Cgko::solver::Cg< ValueType >CG or the conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods
   Cgko::batch::EnableBatchLinOp< Cg< default_precision >, BatchLinOp >
    Cgko::batch::solver::EnableBatchSolver< Cg< default_precision >, default_precision >
  Cgko::EnablePolymorphicAssignment< ConcreteSolver >
   Cgko::batch::EnableBatchLinOp< ConcreteSolver, BatchLinOp >
    Cgko::batch::solver::EnableBatchSolver< ConcreteSolver, ValueType, PolymorphicBase >This mixin provides apply and common iterative solver functionality to all the batched solvers
  Cgko::EnablePolymorphicAssignment< Matrix< default_precision, int32, int64 > >
   Cgko::EnableLinOp< Matrix< default_precision, int32, int64 > >
    Cgko::experimental::distributed::Matrix< ValueType, LocalIndexType, GlobalIndexType >The Matrix class defines a (MPI-)distributed matrix
  Cgko::EnablePolymorphicAssignment< Partition< int32, int64 > >
   Cgko::experimental::distributed::Partition< LocalIndexType, GlobalIndexType >Represents a partition of a range of indices [0, size) into a disjoint set of parts
  Cgko::EnablePolymorphicAssignment< RowGatherer< int32 > >
   Cgko::EnableLinOp< RowGatherer< int32 > >
    Cgko::matrix::RowGatherer< IndexType >RowGatherer is a matrix "format" which stores the gather indices arrays which can be used to gather rows to another matrix
   Cgko::experimental::distributed::RowGatherer< LocalIndexType >The distributed::RowGatherer gathers the rows of distributed::Vector that are located on other processes
  Cgko::EnablePolymorphicAssignment< Vector< double > >
   Cgko::EnableLinOp< Vector< double > >
    Cgko::experimental::distributed::Vector< ValueType >Vector is a format which explicitly stores (multiple) distributed column vectors in a dense storage format
  Cgko::EnablePolymorphicAssignment< Schwarz< default_precision, int32, int64 > >
   Cgko::EnableLinOp< Schwarz< default_precision, int32, int64 > >
    Cgko::experimental::distributed::preconditioner::Schwarz< ValueType, LocalIndexType, GlobalIndexType >A Schwarz preconditioner is a simple domain decomposition preconditioner that generalizes the Block Jacobi preconditioner, incorporating options for different local subdomain solvers and overlaps between the subdomains
  Cgko::EnablePolymorphicAssignment< Cholesky< ValueType, IndexType > >
   Cgko::experimental::factorization::Cholesky< ValueType, IndexType >Computes a Cholesky factorization of a symmetric, positive-definite sparse matrix
  Cgko::EnablePolymorphicAssignment< Factorization< ValueType, IndexType > >
   Cgko::EnableLinOp< Factorization< ValueType, IndexType > >
    Cgko::experimental::factorization::Factorization< ValueType, IndexType >Represents a generic factorization consisting of two triangular factors (upper and lower) and an optional diagonal scaling matrix
  Cgko::EnablePolymorphicAssignment< Lu< ValueType, IndexType > >
   Cgko::experimental::factorization::Lu< ValueType, IndexType >Computes an LU factorization of a sparse matrix
  Cgko::EnablePolymorphicAssignment< Amd< int32 > >
   Cgko::experimental::reorder::Amd< IndexType >Computes a Approximate Minimum Degree (AMD) reordering of an input matrix
  Cgko::EnablePolymorphicAssignment< Mc64< default_precision, int32 > >
   Cgko::experimental::reorder::Mc64< ValueType, IndexType >MC64 is an algorithm for permuting large entries to the diagonal of a sparse matrix
  Cgko::EnablePolymorphicAssignment< Rcm< int32 > >
   Cgko::experimental::reorder::Rcm< IndexType >Rcm (Reverse Cuthill-McKee) is a reordering algorithm minimizing the bandwidth of a matrix
  Cgko::EnablePolymorphicAssignment< ScaledReordered< default_precision, int32 > >
   Cgko::EnableLinOp< ScaledReordered< default_precision, int32 > >
    Cgko::experimental::reorder::ScaledReordered< ValueType, IndexType >Provides an interface to wrap reorderings like Rcm and diagonal scaling like equilibration around a LinOp like e.g
  Cgko::EnablePolymorphicAssignment< Direct< ValueType, IndexType > >
   Cgko::EnableLinOp< Direct< ValueType, IndexType > >
    Cgko::experimental::solver::Direct< ValueType, IndexType >A direct solver based on a factorization into lower and upper triangular factors (with an optional diagonal scaling)
  Cgko::EnablePolymorphicAssignment< Composition< gko::default_precision > >
   Cgko::EnableLinOp< Composition< gko::default_precision > >
    Cgko::Composition< gko::default_precision >
     Cgko::factorization::Ic< ValueType, IndexType >Represents an incomplete Cholesky factorization (IC(0)) of a sparse matrix
     Cgko::factorization::Ilu< ValueType, IndexType >Represents an incomplete LU factorization – ILU(0) – of a sparse matrix
  Cgko::EnablePolymorphicAssignment< Coo< default_precision, int32 > >
   Cgko::EnableLinOp< Coo< default_precision, int32 > >
    Cgko::matrix::Coo< ValueType, IndexType >COO stores a matrix in the coordinate matrix format
  Cgko::EnablePolymorphicAssignment< Diagonal< default_precision > >
   Cgko::EnableLinOp< Diagonal< default_precision > >
    Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
  Cgko::EnablePolymorphicAssignment< Fbcsr< default_precision, int32 > >
   Cgko::EnableLinOp< Fbcsr< default_precision, int32 > >
    Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
  Cgko::EnablePolymorphicAssignment< Fft >
   Cgko::EnableLinOp< Fft >
    Cgko::matrix::FftThis LinOp implements a 1D Fourier matrix using the FFT algorithm
  Cgko::EnablePolymorphicAssignment< Fft2 >
   Cgko::EnableLinOp< Fft2 >
    Cgko::matrix::Fft2This LinOp implements a 2D Fourier matrix using the FFT algorithm
  Cgko::EnablePolymorphicAssignment< Fft3 >
   Cgko::EnableLinOp< Fft3 >
    Cgko::matrix::Fft3This LinOp implements a 3D Fourier matrix using the FFT algorithm
  Cgko::EnablePolymorphicAssignment< Hybrid< default_precision, int32 > >
   Cgko::EnableLinOp< Hybrid< default_precision, int32 > >
    Cgko::matrix::Hybrid< ValueType, IndexType >HYBRID is a matrix format which splits the matrix into ELLPACK and COO format
  Cgko::EnablePolymorphicAssignment< Permutation< int32 > >
   Cgko::EnableLinOp< Permutation< int32 > >
    Cgko::matrix::Permutation< IndexType >Permutation is a matrix format that represents a permutation matrix, i.e
  Cgko::EnablePolymorphicAssignment< ScaledPermutation< default_precision, int32 > >
   Cgko::EnableLinOp< ScaledPermutation< default_precision, int32 > >
    Cgko::matrix::ScaledPermutation< ValueType, IndexType >ScaledPermutation is a matrix combining a permutation with scaling factors
  Cgko::EnablePolymorphicAssignment< Sellp< default_precision, int32 > >
   Cgko::EnableLinOp< Sellp< default_precision, int32 > >
    Cgko::matrix::Sellp< ValueType, IndexType >SELL-P is a matrix format similar to ELL format
  Cgko::EnablePolymorphicAssignment< SparsityCsr< default_precision, int32 > >
   Cgko::EnableLinOp< SparsityCsr< default_precision, int32 > >
    Cgko::matrix::SparsityCsr< ValueType, IndexType >SparsityCsr is a matrix format which stores only the sparsity pattern of a sparse matrix by compressing each row of the matrix (compressed sparse row format)
  Cgko::EnablePolymorphicAssignment< FixedCoarsening< default_precision, int32 > >
   Cgko::EnableLinOp< FixedCoarsening< default_precision, int32 > >
    Cgko::multigrid::FixedCoarsening< ValueType, IndexType >FixedCoarsening is a very simple coarse grid generation algorithm
  Cgko::EnablePolymorphicAssignment< Pgm< default_precision, int32 > >
   Cgko::EnableLinOp< Pgm< default_precision, int32 > >
    Cgko::multigrid::Pgm< ValueType, IndexType >Parallel graph match (Pgm) is the aggregate method introduced in the paper M
  Cgko::EnablePolymorphicAssignment< GaussSeidel< default_precision, int32 > >
   Cgko::preconditioner::GaussSeidel< ValueType, IndexType >This class generates the Gauss-Seidel preconditioner
  Cgko::EnablePolymorphicAssignment< Ic< solver::LowerTrs<>, int32 > >
   Cgko::EnableLinOp< Ic< solver::LowerTrs<>, int32 > >
    Cgko::preconditioner::Ic< LSolverTypeOrValueType, IndexType >The Incomplete Cholesky (IC) preconditioner solves the equation $LL^H*x = b$ for a given lower triangular matrix L and the right hand side b (can contain multiple right hand sides)
  Cgko::EnablePolymorphicAssignment< Isai< IsaiType, ValueType, IndexType > >
   Cgko::EnableLinOp< Isai< IsaiType, ValueType, IndexType > >
    Cgko::preconditioner::Isai< IsaiType==isai_type::general ? isai_type::general :IsaiType==isai_type::spd ? isai_type::spd :IsaiType==isai_type::lower ? isai_type::upper :isai_type::lower, ValueType, IndexType >
    Cgko::preconditioner::Isai< isai_type::lower, ValueType, IndexType >
    Cgko::preconditioner::Isai< isai_type::upper, ValueType, IndexType >
    Cgko::preconditioner::Isai< isai_type::general, ValueType, IndexType >
    Cgko::preconditioner::Isai< isai_type::spd, ValueType, IndexType >
    Cgko::preconditioner::Isai< IsaiType, ValueType, IndexType >The Incomplete Sparse Approximate Inverse (ISAI) Preconditioner generates an approximate inverse matrix for a given square matrix A, lower triangular matrix L, upper triangular matrix U or symmetric positive (spd) matrix B
  Cgko::EnablePolymorphicAssignment< Sor< default_precision, int32 > >
   Cgko::preconditioner::Sor< ValueType, IndexType >This class generates the (S)SOR preconditioner
  Cgko::EnablePolymorphicAssignment< Rcm< default_precision, int32 > >
   Cgko::reorder::Rcm< ValueType, IndexType >Rcm (Reverse Cuthill-McKee) is a reordering algorithm minimizing the bandwidth of a matrix
  Cgko::EnablePolymorphicAssignment< Bicg< default_precision > >
   Cgko::EnableLinOp< Bicg< default_precision > >
    Cgko::solver::Bicg< ValueType >BICG or the Biconjugate gradient method is a Krylov subspace solver
  Cgko::EnablePolymorphicAssignment< CbGmres< default_precision > >
   Cgko::EnableLinOp< CbGmres< default_precision > >
    Cgko::solver::CbGmres< ValueType >CB-GMRES or the compressed basis generalized minimal residual method is an iterative type Krylov subspace method which is suitable for nonsymmetric linear systems
  Cgko::EnablePolymorphicAssignment< Cgs< default_precision > >
   Cgko::EnableLinOp< Cgs< default_precision > >
    Cgko::solver::Cgs< ValueType >CGS or the conjugate gradient square method is an iterative type Krylov subspace method which is suitable for general systems
  Cgko::EnablePolymorphicAssignment< Chebyshev< default_precision > >
   Cgko::EnableLinOp< Chebyshev< default_precision > >
    Cgko::solver::Chebyshev< ValueType >Chebyshev iteration is an iterative method for solving nonsymmetric problems based on some knowledge of the spectrum of the (preconditioned) system matrix
  Cgko::EnablePolymorphicAssignment< Fcg< default_precision > >
   Cgko::EnableLinOp< Fcg< default_precision > >
    Cgko::solver::Fcg< ValueType >FCG or the flexible conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods
  Cgko::EnablePolymorphicAssignment< Gcr< default_precision > >
   Cgko::EnableLinOp< Gcr< default_precision > >
    Cgko::solver::Gcr< ValueType >GCR or the generalized conjugate residual method is an iterative type Krylov subspace method similar to GMRES which is suitable for nonsymmetric linear systems
  Cgko::EnablePolymorphicAssignment< Gmres< default_precision > >
   Cgko::EnableLinOp< Gmres< default_precision > >
    Cgko::solver::Gmres< ValueType >GMRES or the generalized minimal residual method is an iterative type Krylov subspace method which is suitable for nonsymmetric linear systems
  Cgko::EnablePolymorphicAssignment< Idr< default_precision > >
   Cgko::EnableLinOp< Idr< default_precision > >
    Cgko::solver::Idr< ValueType >IDR(s) is an efficient method for solving large nonsymmetric systems of linear equations
  Cgko::EnablePolymorphicAssignment< LowerTrs< default_precision, int32 > >
   Cgko::EnableLinOp< LowerTrs< default_precision, int32 > >
    Cgko::solver::LowerTrs< ValueType, IndexType >LowerTrs is the triangular solver which solves the system L x = b, when L is a lower triangular matrix
  Cgko::EnablePolymorphicAssignment< Minres< default_precision > >
   Cgko::EnableLinOp< Minres< default_precision > >
    Cgko::solver::Minres< ValueType >Minres is an iterative type Krylov subspace method, which is suitable for indefinite and full-rank symmetric/hermitian operators
  Cgko::EnablePolymorphicAssignment< Multigrid >
   Cgko::EnableLinOp< Multigrid >
    Cgko::solver::MultigridMultigrid methods have a hierarchy of many levels, whose corase level is a subset of the fine level, of the problem
  Cgko::EnablePolymorphicAssignment< PipeCg< default_precision > >
   Cgko::EnableLinOp< PipeCg< default_precision > >
    Cgko::solver::PipeCg< ValueType >PIPE_CG or the pipelined conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods
  Cgko::EnablePolymorphicAssignment< UpperTrs< default_precision, int32 > >
   Cgko::EnableLinOp< UpperTrs< default_precision, int32 > >
    Cgko::solver::UpperTrs< ValueType, IndexType >UpperTrs is the triangular solver which solves the system U x = b, when U is an upper triangular matrix
  Cgko::batch::matrix::Csr< ValueType, IndexType >
  Cgko::batch::matrix::Dense< ValueType >
  Cgko::experimental::distributed::Vector< ValueType >
  Cgko::experimental::distributed::Vector< to_complex< value_type > >
  Cgko::experimental::distributed::Vector< previous_precision< ValueType > >
  Cgko::matrix::Coo< ValueType, IndexType >
  Cgko::matrix::Coo< ValueType, IndexType >
  Cgko::matrix::Coo< ValueType, IndexType >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Diagonal< ValueType >
  Cgko::matrix::Diagonal< ValueType >
  Cgko::matrix::Diagonal< ValueType >
  Cgko::matrix::Ell< value_type, index_type >
  Cgko::matrix::Ell< value_type, index_type >
  Cgko::matrix::Ell< value_type, index_type >
  Cgko::matrix::Ell< ValueType, IndexType >
  Cgko::matrix::Ell< ValueType, IndexType >
  Cgko::matrix::Ell< ValueType, IndexType >
  Cgko::matrix::Fbcsr< ValueType, IndexType >
  Cgko::matrix::Fbcsr< ValueType, IndexType >
  Cgko::matrix::Fbcsr< ValueType, IndexType >
  Cgko::matrix::Fbcsr< ValueType, IndexType >
  Cgko::matrix::SparsityCsr< value_type, index_type >
  Cgko::matrix::SparsityCsr< value_type, index_type >
  Cgko::matrix::SparsityCsr< IndexType, ValueType >
  Cgko::matrix::SparsityCsr< IndexType, ValueType >
  Cgko::matrix::SparsityCsr< ValueType, IndexType >
  Cgko::matrix::SparsityCsr< ValueType, IndexType >
  Cgko::preconditioner::Jacobi< ValueType, IndexType >
  Cgko::EnablePolymorphicAssignment< ConcreteType, ResultType >This mixin is used to enable a default PolymorphicObject::copy_from() implementation for objects that have implemented conversions between them
   Cgko::experimental::distributed::Partition< LocalIndexType, GlobalIndexType >
  Cgko::batch::MultiVector< ValueType >MultiVector stores multiple vectors in a batched fashion and is useful for batched operations
  Cgko::batch::matrix::Csr< ValueType, IndexType >Csr is a general sparse matrix format that stores the column indices for each nonzero entry and a cumulative sum of the number of nonzeros in each row
  Cgko::batch::matrix::Dense< ValueType >Dense is a batch matrix format which explicitly stores all values of the matrix in each of the batches
  Cgko::batch::matrix::Ell< ValueType, IndexType >Ell is a sparse matrix format that stores the same number of nonzeros in each row, enabling coalesced accesses
  Cgko::experimental::distributed::Matrix< ValueType, LocalIndexType, GlobalIndexType >The Matrix class defines a (MPI-)distributed matrix
  Cgko::experimental::distributed::Vector< ValueType >Vector is a format which explicitly stores (multiple) distributed column vectors in a dense storage format
  Cgko::matrix::Coo< ValueType, IndexType >COO stores a matrix in the coordinate matrix format
  Cgko::matrix::Coo< ValueType, IndexType >COO stores a matrix in the coordinate matrix format
  Cgko::matrix::Coo< ValueType, IndexType >COO stores a matrix in the coordinate matrix format
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
  Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
  Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
  Cgko::matrix::Ell< ValueType, IndexType >ELL is a matrix format where stride with explicit zeros is used such that all rows have the same number of stored elements
  Cgko::matrix::Ell< ValueType, IndexType >ELL is a matrix format where stride with explicit zeros is used such that all rows have the same number of stored elements
  Cgko::matrix::Ell< ValueType, IndexType >ELL is a matrix format where stride with explicit zeros is used such that all rows have the same number of stored elements
  Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
  Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
  Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
  Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
  Cgko::matrix::Hybrid< ValueType, IndexType >HYBRID is a matrix format which splits the matrix into ELLPACK and COO format
  Cgko::matrix::Hybrid< ValueType, IndexType >HYBRID is a matrix format which splits the matrix into ELLPACK and COO format
  Cgko::matrix::Hybrid< ValueType, IndexType >HYBRID is a matrix format which splits the matrix into ELLPACK and COO format
  Cgko::matrix::Sellp< ValueType, IndexType >SELL-P is a matrix format similar to ELL format
  Cgko::matrix::Sellp< ValueType, IndexType >SELL-P is a matrix format similar to ELL format
  Cgko::matrix::Sellp< ValueType, IndexType >SELL-P is a matrix format similar to ELL format
  Cgko::matrix::SparsityCsr< ValueType, IndexType >SparsityCsr is a matrix format which stores only the sparsity pattern of a sparse matrix by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::SparsityCsr< ValueType, IndexType >SparsityCsr is a matrix format which stores only the sparsity pattern of a sparse matrix by compressing each row of the matrix (compressed sparse row format)
  Cgko::preconditioner::Jacobi< ValueType, IndexType >A block-Jacobi preconditioner is a block-diagonal linear operator, obtained by inverting the diagonal blocks of the source operator
 Cgko::matrix::CooBuilder< ValueType, IndexType >
 Cgko::cpx_real_type< T >Access the underlying real type of a complex number
 Cgko::log::criterion_dataStruct representing Criterion related data
 Cgko::stop::CriterionArgsThis struct is used to pass parameters to the EnableDefaultCriterionFactoryCriterionFactory::generate() method
 Cgko::matrix::CsrBuilder< ValueType, IndexType >
 Cgko::cuda_streamAn RAII wrapper for a custom CUDA stream
 Cgko::default_converter< S, R >Used to convert objects of type S to objects of type R using static_cast
 Cgko::deferred_factory_parameter< FactoryType >Represents a factory parameter of factory type that can either initialized by a pre-existing factory or by passing in a factory_parameters object whose .on(exec) will be called to instantiate a factory
 Cgko::device_matrix_data< ValueType, IndexType >This type is a device-side equivalent to matrix_data
 Cgko::DiagonalLinOpExtractableThe diagonal of a LinOp can be extracted
  Cgko::DiagonalExtractable< default_precision >
   Cgko::matrix::Coo< ValueType, IndexType >COO stores a matrix in the coordinate matrix format
   Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
   Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
   Cgko::matrix::Ell< ValueType, IndexType >ELL is a matrix format where stride with explicit zeros is used such that all rows have the same number of stored elements
   Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
   Cgko::matrix::Hybrid< ValueType, IndexType >HYBRID is a matrix format which splits the matrix into ELLPACK and COO format
   Cgko::matrix::Sellp< ValueType, IndexType >SELL-P is a matrix format similar to ELL format
  Cgko::DiagonalExtractable< ValueType >The diagonal of a LinOp implementing this interface can be extracted
   Cgko::matrix::Coo< ValueType, IndexType >
   Cgko::matrix::Csr< value_type, index_type >
   Cgko::matrix::Csr< ValueType, IndexType >
   Cgko::matrix::Dense< ValueType >
   Cgko::matrix::Dense< value_type >
   Cgko::matrix::Dense< to_complex< ValueType > >
   Cgko::matrix::Dense< absolute_type >
   Cgko::matrix::Dense< remove_complex< ValueType > >
   Cgko::matrix::Ell< value_type, index_type >
   Cgko::matrix::Ell< ValueType, IndexType >
   Cgko::matrix::Fbcsr< ValueType, IndexType >
 Cgko::dim< Dimensionality, DimensionType >A type representing the dimensions of a multidimensional object
 Cgko::dim< 1u, DimensionType >
 Cgko::experimental::distributed::DistributedBaseA base class for distributed objects
  Cgko::experimental::distributed::Vector< ValueType >
  Cgko::experimental::distributed::Vector< to_complex< value_type > >
  Cgko::experimental::distributed::Vector< previous_precision< ValueType > >
  Cgko::experimental::distributed::Matrix< ValueType, LocalIndexType, GlobalIndexType >The Matrix class defines a (MPI-)distributed matrix
  Cgko::experimental::distributed::RowGatherer< LocalIndexType >The distributed::RowGatherer gathers the rows of distributed::Vector that are located on other processes
  Cgko::experimental::distributed::Vector< ValueType >Vector is a format which explicitly stores (multiple) distributed column vectors in a dense storage format
 Cgko::enable_parameters_type< ConcreteParametersType, Factory >The enable_parameters_type mixin is used to create a base implementation of the factory parameters structure
 Cgko::enable_parameters_type< Parameters, Factory >
  Cgko::batch::solver::enable_preconditioned_iterative_solver_factory_parameters< Parameters, Factory >
  Cgko::solver::enable_iterative_solver_factory_parameters< Parameters, Factory >
   Cgko::solver::enable_preconditioned_iterative_solver_factory_parameters< Parameters, Factory >
 Cgko::enable_parameters_type< parameters_type, Amd< IndexType > >
  Cgko::experimental::reorder::Amd< IndexType >::parameters_type
 Cgko::enable_parameters_type< parameters_type, Cholesky >
  Cgko::experimental::factorization::Cholesky< ValueType, IndexType >::parameters_type
 Cgko::enable_parameters_type< parameters_type, Factory >
  Cgko::batch::solver::enable_preconditioned_iterative_solver_factory_parameters< parameters_type, Factory >
   Cgko::batch::solver::Bicgstab< ValueType >::parameters_type
   Cgko::batch::solver::Cg< ValueType >::parameters_type
  Cgko::solver::enable_iterative_solver_factory_parameters< parameters_type, Factory >
   Cgko::solver::enable_preconditioned_iterative_solver_factory_parameters< parameters_type, Factory >
    Cgko::solver::Bicg< ValueType >::parameters_type
    Cgko::solver::Bicgstab< ValueType >::parameters_type
    Cgko::solver::CbGmres< ValueType >::parameters_type
    Cgko::solver::Cg< ValueType >::parameters_type
    Cgko::solver::Cgs< ValueType >::parameters_type
    Cgko::solver::Chebyshev< ValueType >::parameters_type
    Cgko::solver::Fcg< ValueType >::parameters_type
    Cgko::solver::Gcr< ValueType >::parameters_type
    Cgko::solver::Gmres< ValueType >::parameters_type
    Cgko::solver::Idr< ValueType >::parameters_type
    Cgko::solver::Minres< ValueType >::parameters_type
    Cgko::solver::PipeCg< ValueType >::parameters_type
   Cgko::solver::Multigrid::parameters_type
   Cgko::solver::parameters_type
  Cgko::batch::preconditioner::Jacobi< ValueType, IndexType >::parameters_type
  Cgko::experimental::distributed::preconditioner::Schwarz< ValueType, LocalIndexType, GlobalIndexType >::parameters_type
  Cgko::experimental::reorder::ScaledReordered< ValueType, IndexType >::parameters_type
  Cgko::experimental::solver::Direct< ValueType, IndexType >::parameters_type
  Cgko::factorization::Ic< ValueType, IndexType >::parameters_type
  Cgko::factorization::Ilu< ValueType, IndexType >::parameters_type
  Cgko::factorization::ParIc< ValueType, IndexType >::parameters_type
  Cgko::factorization::ParIct< ValueType, IndexType >::parameters_type
  Cgko::factorization::ParIlu< ValueType, IndexType >::parameters_type
  Cgko::factorization::ParIlut< ValueType, IndexType >::parameters_type
  Cgko::multigrid::FixedCoarsening< ValueType, IndexType >::parameters_type
  Cgko::multigrid::Pgm< ValueType, IndexType >::parameters_type
  Cgko::preconditioner::Ic< LSolverTypeOrValueType, IndexType >::parameters_type
  Cgko::preconditioner::Ilu< LSolverTypeOrValueType, USolverTypeOrValueType, ReverseApply, IndexType >::parameters_type
  Cgko::preconditioner::Isai< IsaiType, ValueType, IndexType >::parameters_type
  Cgko::preconditioner::Jacobi< ValueType, IndexType >::parameters_type
  Cgko::reorder::Rcm< ValueType, IndexType >::parameters_type
  Cgko::solver::LowerTrs< ValueType, IndexType >::parameters_type
  Cgko::solver::UpperTrs< ValueType, IndexType >::parameters_type
  Cgko::stop::AbsoluteResidualNorm< ValueType >::parameters_type
  Cgko::stop::Combined::parameters_type
  Cgko::stop::ImplicitResidualNorm< ValueType >::parameters_type
  Cgko::stop::Iteration::parameters_type
  Cgko::stop::RelativeResidualNorm< ValueType >::parameters_type
  Cgko::stop::ResidualNorm< ValueType >::parameters_type
  Cgko::stop::ResidualNormReduction< ValueType >::parameters_type
  Cgko::stop::Time::parameters_type
 Cgko::enable_parameters_type< parameters_type, GaussSeidel >
  Cgko::preconditioner::GaussSeidel< ValueType, IndexType >::parameters_type
 Cgko::enable_parameters_type< parameters_type, Lu >
  Cgko::experimental::factorization::Lu< ValueType, IndexType >::parameters_type
 Cgko::enable_parameters_type< parameters_type, Mc64 >
  Cgko::experimental::reorder::Mc64< ValueType, IndexType >::parameters_type
 Cgko::enable_parameters_type< parameters_type, Rcm< IndexType > >
  Cgko::experimental::reorder::Rcm< IndexType >::parameters_type
 Cgko::enable_parameters_type< parameters_type, Sor >
  Cgko::preconditioner::Sor< ValueType, IndexType >::parameters_type
 Cstd::enable_shared_from_this
  Cgko::CudaExecutorThis is the Executor subclass which represents the CUDA device
  Cgko::DpcppExecutorThis is the Executor subclass which represents a DPC++ enhanced device
  Cgko::HipExecutorThis is the Executor subclass which represents the HIP enhanced device
  Cgko::OmpExecutorThis is the Executor subclass which represents the OpenMP device (typically CPU)
   Cgko::ReferenceExecutorThis is a specialization of the OmpExecutor, which runs the reference implementations of the kernels used for debugging purposes
 CEnableAbstractPolymorphicObject
  Cgko::AbstractFactory< ReorderingBase< IndexType >, ReorderingBaseArgs >
  Cgko::AbstractFactory< gko::reorder::ReorderingBase< IndexType >, gko::reorder::ReorderingBaseArgs >
  Cgko::AbstractFactory< Criterion, CriterionArgs >
  Cgko::AbstractFactory< LinOp, std::shared_ptr< const LinOp > >
   Cgko::LinOpFactoryA LinOpFactory represents a higher order mapping which transforms one linear operator into another
  Cgko::AbstractFactory< BatchLinOp, std::shared_ptr< const BatchLinOp > >
   Cgko::batch::BatchLinOpFactoryA BatchLinOpFactory represents a higher order mapping which transforms one batch linear operator into another
  Cgko::EnablePolymorphicObject< BlockOperator, LinOp >
   Cgko::EnableLinOp< BlockOperator >
  Cgko::EnablePolymorphicObject< Combination< default_precision >, LinOp >
   Cgko::EnableLinOp< Combination< default_precision > >
  Cgko::EnablePolymorphicObject< Composition< default_precision >, LinOp >
   Cgko::EnableLinOp< Composition< default_precision > >
  Cgko::EnablePolymorphicObject< ConcreteFactory, PolymorphicBase >
   Cgko::EnableDefaultFactory< ConcreteFactory, ConcreteBatchLinOp, ParametersType, PolymorphicBase >
   Cgko::EnableDefaultFactory< ConcreteFactory, ConcreteLinOp, ParametersType, PolymorphicBase >
   Cgko::EnableDefaultFactory< ConcreteFactory, ConcreteReorderingBase, ParametersType, PolymorphicBase >
   Cgko::EnableDefaultFactory< ConcreteFactory, ConcreteCriterion, ParametersType, PolymorphicBase >
   Cgko::EnableDefaultFactory< ConcreteFactory, ProductType, ParametersType, PolymorphicBase >This mixin provides a default implementation of a concrete factory
  Cgko::EnablePolymorphicObject< ConcreteLinOp, LinOp >
   Cgko::EnableLinOp< ConcreteLinOp, PolymorphicBase >The EnableLinOp mixin can be used to provide sensible default implementations of the majority of the LinOp and PolymorphicObject interface
  Cgko::EnablePolymorphicObject< Perturbation< default_precision >, LinOp >
   Cgko::EnableLinOp< Perturbation< default_precision > >
  Cgko::EnablePolymorphicObject< ConcreteBatchLinOp, BatchLinOp >
   Cgko::batch::EnableBatchLinOp< ConcreteBatchLinOp, PolymorphicBase >The EnableBatchLinOp mixin can be used to provide sensible default implementations of the majority of the BatchLinOp and PolymorphicObject interface
  Cgko::EnablePolymorphicObject< MultiVector< default_precision > >
   Cgko::batch::MultiVector< ValueType >MultiVector stores multiple vectors in a batched fashion and is useful for batched operations
  Cgko::EnablePolymorphicObject< Csr< default_precision, int32 >, BatchLinOp >
   Cgko::batch::EnableBatchLinOp< Csr< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Dense< default_precision >, BatchLinOp >
   Cgko::batch::EnableBatchLinOp< Dense< default_precision > >
  Cgko::EnablePolymorphicObject< Ell< default_precision, int32 >, BatchLinOp >
   Cgko::batch::EnableBatchLinOp< Ell< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Identity< default_precision >, BatchLinOp >
   Cgko::batch::EnableBatchLinOp< Identity< default_precision > >
  Cgko::EnablePolymorphicObject< Jacobi< default_precision, int32 >, BatchLinOp >
   Cgko::batch::EnableBatchLinOp< Jacobi< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Bicgstab< default_precision >, BatchLinOp >
   Cgko::batch::EnableBatchLinOp< Bicgstab< default_precision >, BatchLinOp >
  Cgko::EnablePolymorphicObject< Cg< default_precision >, BatchLinOp >
   Cgko::batch::EnableBatchLinOp< Cg< default_precision >, BatchLinOp >
  Cgko::EnablePolymorphicObject< ConcreteSolver, BatchLinOp >
   Cgko::batch::EnableBatchLinOp< ConcreteSolver, BatchLinOp >
  Cgko::EnablePolymorphicObject< Matrix< default_precision, int32, int64 >, LinOp >
   Cgko::EnableLinOp< Matrix< default_precision, int32, int64 > >
  Cgko::EnablePolymorphicObject< Partition< int32, int64 > >
   Cgko::experimental::distributed::Partition< LocalIndexType, GlobalIndexType >Represents a partition of a range of indices [0, size) into a disjoint set of parts
  Cgko::EnablePolymorphicObject< RowGatherer< int32 > >
   Cgko::experimental::distributed::RowGatherer< LocalIndexType >The distributed::RowGatherer gathers the rows of distributed::Vector that are located on other processes
  Cgko::EnablePolymorphicObject< Vector< double >, LinOp >
   Cgko::EnableLinOp< Vector< double > >
  Cgko::EnablePolymorphicObject< Schwarz< default_precision, int32, int64 >, LinOp >
   Cgko::EnableLinOp< Schwarz< default_precision, int32, int64 > >
  Cgko::EnablePolymorphicObject< Cholesky< ValueType, IndexType >, LinOpFactory >
   Cgko::experimental::factorization::Cholesky< ValueType, IndexType >Computes a Cholesky factorization of a symmetric, positive-definite sparse matrix
  Cgko::EnablePolymorphicObject< Factorization< ValueType, IndexType >, LinOp >
   Cgko::EnableLinOp< Factorization< ValueType, IndexType > >
  Cgko::EnablePolymorphicObject< Lu< ValueType, IndexType >, LinOpFactory >
   Cgko::experimental::factorization::Lu< ValueType, IndexType >Computes an LU factorization of a sparse matrix
  Cgko::EnablePolymorphicObject< Amd< int32 >, LinOpFactory >
   Cgko::experimental::reorder::Amd< IndexType >Computes a Approximate Minimum Degree (AMD) reordering of an input matrix
  Cgko::EnablePolymorphicObject< Mc64< default_precision, int32 >, LinOpFactory >
   Cgko::experimental::reorder::Mc64< ValueType, IndexType >MC64 is an algorithm for permuting large entries to the diagonal of a sparse matrix
  Cgko::EnablePolymorphicObject< Rcm< int32 >, LinOpFactory >
   Cgko::experimental::reorder::Rcm< IndexType >Rcm (Reverse Cuthill-McKee) is a reordering algorithm minimizing the bandwidth of a matrix
  Cgko::EnablePolymorphicObject< ScaledReordered< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< ScaledReordered< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Direct< ValueType, IndexType >, LinOp >
   Cgko::EnableLinOp< Direct< ValueType, IndexType > >
  Cgko::EnablePolymorphicObject< Composition< gko::default_precision >, LinOp >
   Cgko::EnableLinOp< Composition< gko::default_precision > >
  Cgko::EnablePolymorphicObject< Coo< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< Coo< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Csr< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< Csr< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Dense< default_precision >, LinOp >
   Cgko::EnableLinOp< Dense< default_precision > >
  Cgko::EnablePolymorphicObject< Diagonal< default_precision >, LinOp >
   Cgko::EnableLinOp< Diagonal< default_precision > >
  Cgko::EnablePolymorphicObject< Ell< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< Ell< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Fbcsr< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< Fbcsr< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Fft, LinOp >
   Cgko::EnableLinOp< Fft >
  Cgko::EnablePolymorphicObject< Fft2, LinOp >
   Cgko::EnableLinOp< Fft2 >
  Cgko::EnablePolymorphicObject< Fft3, LinOp >
   Cgko::EnableLinOp< Fft3 >
  Cgko::EnablePolymorphicObject< Hybrid< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< Hybrid< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Identity< default_precision >, LinOp >
   Cgko::EnableLinOp< Identity< default_precision > >
  Cgko::EnablePolymorphicObject< IdentityFactory< default_precision >, LinOpFactory >
   Cgko::matrix::IdentityFactory< ValueType >This factory is a utility which can be used to generate Identity operators
  Cgko::EnablePolymorphicObject< Permutation< int32 >, LinOp >
   Cgko::EnableLinOp< Permutation< int32 > >
  Cgko::EnablePolymorphicObject< RowGatherer< int32 >, LinOp >
   Cgko::EnableLinOp< RowGatherer< int32 > >
  Cgko::EnablePolymorphicObject< ScaledPermutation< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< ScaledPermutation< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Sellp< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< Sellp< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< SparsityCsr< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< SparsityCsr< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< FixedCoarsening< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< FixedCoarsening< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Pgm< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< Pgm< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< GaussSeidel< default_precision, int32 >, LinOpFactory >
   Cgko::preconditioner::GaussSeidel< ValueType, IndexType >This class generates the Gauss-Seidel preconditioner
  Cgko::EnablePolymorphicObject< Ic< solver::LowerTrs<>, int32 >, LinOp >
   Cgko::EnableLinOp< Ic< solver::LowerTrs<>, int32 > >
  Cgko::EnablePolymorphicObject< Isai< IsaiType, ValueType, IndexType >, LinOp >
   Cgko::EnableLinOp< Isai< IsaiType, ValueType, IndexType > >
  Cgko::EnablePolymorphicObject< Jacobi< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< Jacobi< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Sor< default_precision, int32 >, LinOpFactory >
   Cgko::preconditioner::Sor< ValueType, IndexType >This class generates the (S)SOR preconditioner
  Cgko::EnablePolymorphicObject< Rcm< default_precision, int32 >, ReorderingBase< int32 > >
   Cgko::reorder::Rcm< ValueType, IndexType >Rcm (Reverse Cuthill-McKee) is a reordering algorithm minimizing the bandwidth of a matrix
  Cgko::EnablePolymorphicObject< Bicg< default_precision >, LinOp >
   Cgko::EnableLinOp< Bicg< default_precision > >
  Cgko::EnablePolymorphicObject< Bicgstab< default_precision >, LinOp >
   Cgko::EnableLinOp< Bicgstab< default_precision > >
  Cgko::EnablePolymorphicObject< CbGmres< default_precision >, LinOp >
   Cgko::EnableLinOp< CbGmres< default_precision > >
  Cgko::EnablePolymorphicObject< Cg< default_precision >, LinOp >
   Cgko::EnableLinOp< Cg< default_precision > >
  Cgko::EnablePolymorphicObject< Cgs< default_precision >, LinOp >
   Cgko::EnableLinOp< Cgs< default_precision > >
  Cgko::EnablePolymorphicObject< Chebyshev< default_precision >, LinOp >
   Cgko::EnableLinOp< Chebyshev< default_precision > >
  Cgko::EnablePolymorphicObject< Fcg< default_precision >, LinOp >
   Cgko::EnableLinOp< Fcg< default_precision > >
  Cgko::EnablePolymorphicObject< Gcr< default_precision >, LinOp >
   Cgko::EnableLinOp< Gcr< default_precision > >
  Cgko::EnablePolymorphicObject< Gmres< default_precision >, LinOp >
   Cgko::EnableLinOp< Gmres< default_precision > >
  Cgko::EnablePolymorphicObject< Idr< default_precision >, LinOp >
   Cgko::EnableLinOp< Idr< default_precision > >
  Cgko::EnablePolymorphicObject< LowerTrs< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< LowerTrs< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< Minres< default_precision >, LinOp >
   Cgko::EnableLinOp< Minres< default_precision > >
  Cgko::EnablePolymorphicObject< Multigrid, LinOp >
   Cgko::EnableLinOp< Multigrid >
  Cgko::EnablePolymorphicObject< PipeCg< default_precision >, LinOp >
   Cgko::EnableLinOp< PipeCg< default_precision > >
  Cgko::EnablePolymorphicObject< UpperTrs< default_precision, int32 >, LinOp >
   Cgko::EnableLinOp< UpperTrs< default_precision, int32 > >
  Cgko::EnablePolymorphicObject< ResidualNormBase< default_precision >, Criterion >
   Cgko::stop::ResidualNormBase< default_precision >
    Cgko::stop::AbsoluteResidualNorm< ValueType >The AbsoluteResidualNorm class is a stopping criterion which stops the iteration process when the residual norm is below a certain threshold, i.e
    Cgko::stop::ImplicitResidualNorm< ValueType >The ImplicitResidualNorm class is a stopping criterion which stops the iteration process when the implicit residual norm is below a certain threshold relative to
    Cgko::stop::RelativeResidualNorm< ValueType >The RelativeResidualNorm class is a stopping criterion which stops the iteration process when the residual norm is below a certain threshold relative to the norm of the right-hand side, i.e
    Cgko::stop::ResidualNorm< ValueType >The ResidualNorm class is a stopping criterion which stops the iteration process when the actual residual norm is below a certain threshold relative to
    Cgko::stop::ResidualNormReduction< ValueType >The ResidualNormReduction class is a stopping criterion which stops the iteration process when the residual norm is below a certain threshold relative to the norm of the initial residual, i.e
  Cgko::EnablePolymorphicObject< Combined, Criterion >
   Cgko::stop::CombinedUsed to combine multiple criterions together through an OR operation
  Cgko::EnablePolymorphicObject< Iteration, Criterion >
   Cgko::stop::IterationStopping criterion which stops the iteration process after a preset number of iterations
  Cgko::EnablePolymorphicObject< ResidualNormBase< ValueType >, Criterion >
   Cgko::stop::ResidualNormBase< ValueType >The ResidualNormBase class provides a framework for stopping criteria related to the residual norm
  Cgko::EnablePolymorphicObject< Time, Criterion >
   Cgko::stop::TimeStopping criterion which stops the iteration process after a certain amount of time has passed
  Cgko::AbstractFactory< AbstractProductType, ComponentsType >The AbstractFactory is a generic interface template that enables easy implementation of the abstract factory design pattern
  Cgko::EnablePolymorphicObject< ConcreteObject, PolymorphicBase >This mixin inherits from (a subclass of) PolymorphicObject and provides a base implementation of a new concrete polymorphic object
   Cgko::experimental::distributed::Partition< LocalIndexType, GlobalIndexType >
  Cgko::LinOp
  Cgko::batch::BatchLinOp
  Cgko::reorder::ReorderingBase< IndexType >The ReorderingBase class is a base class for all the reordering algorithms
  Cgko::stop::CriterionBase class for all stopping criteria
 Cgko::EnableCreateMethod< ConcreteType >This mixin implements a static create() method on ConcreteType that dynamically allocates the memory, uses the passed-in arguments to construct the object, and returns an std::unique_ptr to such an object
  Cgko::Combination< ValueType >
  Cgko::Composition< ValueType >
  Cgko::Composition< value_type >
 Cgko::EnableCreateMethod< Combination< default_precision > >
  Cgko::Combination< ValueType >The Combination class can be used to construct a linear combination of multiple linear operators c1 * op1 + c2 * op2 + ... + ck * opk
 Cgko::EnableCreateMethod< Composition< default_precision > >
  Cgko::Composition< default_precision >
  Cgko::Composition< ValueType >The Composition class can be used to compose linear operators op1, op2, ..., opn and obtain the operator op1 * op2 * ... * opn
 Cgko::EnableCreateMethod< Composition< gko::default_precision > >
  Cgko::Composition< gko::default_precision >
 Cgko::EnableCreateMethod< Perturbation< default_precision > >
  Cgko::Perturbation< ValueType >The Perturbation class can be used to construct a LinOp to represent the operation (identity + scalar * basis * projector)
 Cgko::detail::EnableDeviceResetControls whether the DeviceReset function should be called thanks to a boolean
  Cgko::CudaExecutorThis is the Executor subclass which represents the CUDA device
  Cgko::HipExecutorThis is the Executor subclass which represents the HIP enhanced device
 CEnableLinOp< Ilu< solver::LowerTrs<>, gko::detail::transposed_type< solver::LowerTrs<> >, false, int32 > >
  Cgko::preconditioner::Ilu< LSolverTypeOrValueType, USolverTypeOrValueType, ReverseApply, IndexType >The Incomplete LU (ILU) preconditioner solves the equation $LUx = b$ for a given lower triangular matrix L, an upper triangular matrix U and the right hand side b (can contain multiple right hand sides)
 Cgko::experimental::mpi::environmentClass that sets up and finalizes the MPI environment
 Cgko::err< T >
 Cstd::exceptionSTL class
  Cgko::ErrorUsed to report exceptional behaviour in library functions
   Cgko::AllocationErrorAllocationError is thrown if a memory allocation fails
   Cgko::BadDimensionBadDimension is thrown if an operation is being applied to a LinOp with bad dimensions
   Cgko::BlockSizeError< IndexType >Error that denotes issues between block sizes and matrix dimensions
   Cgko::CublasErrorCublasError is thrown when a cuBLAS routine throws a non-zero error code
   Cgko::CudaErrorCudaError is thrown when a CUDA routine throws a non-zero error code
   Cgko::CufftErrorCufftError is thrown when a cuFFT routine throws a non-zero error code
   Cgko::CurandErrorCurandError is thrown when a cuRAND routine throws a non-zero error code
   Cgko::CusparseErrorCusparseError is thrown when a cuSPARSE routine throws a non-zero error code
   Cgko::DimensionMismatchDimensionMismatch is thrown if an operation is being applied to LinOps of incompatible size
   Cgko::HipErrorHipError is thrown when a HIP routine throws a non-zero error code
   Cgko::HipblasErrorHipblasError is thrown when a hipBLAS routine throws a non-zero error code
   Cgko::HipfftErrorHipfftError is thrown when a hipFFT routine throws a non-zero error code
   Cgko::HiprandErrorHiprandError is thrown when a hipRAND routine throws a non-zero error code
   Cgko::HipsparseErrorHipsparseError is thrown when a hipSPARSE routine throws a non-zero error code
   Cgko::InvalidStateErrorException thrown if an object is in an invalid state
   Cgko::KernelNotFoundKernelNotFound is thrown if Ginkgo cannot find a kernel which satisfies the criteria imposed by the input arguments
   Cgko::MetisErrorMetisError is thrown when METIS routine throws an error code
   Cgko::MpiErrorMpiError is thrown when a MPI routine throws a non-zero error code
   Cgko::NotCompiledNotCompiled is thrown when attempting to call an operation which is a part of a module that was not compiled on the system
   Cgko::NotImplementedNotImplemented is thrown in case an operation has not yet been implemented (but will be implemented in the future)
   Cgko::NotSupportedNotSupported is thrown in case it is not possible to perform the requested operation on the given object type
   Cgko::OutOfBoundsErrorOutOfBoundsError is thrown if a memory access is detected to be out-of-bounds
   Cgko::OverflowErrorOverflowError is thrown when an index calculation for storage requirements overflows
   Cgko::StreamErrorStreamError is thrown if accessing a stream failed
   Cgko::UnsupportedMatrixPropertyException throws if a matrix does not have a property required by a numerical method
   Cgko::ValueMismatchValueMismatch is thrown if two values are not equal
 Cgko::log::executor_dataStruct representing Executor related data
 Cgko::executor_deleter< T >This is a deleter that uses an executor's free method to deallocate the data
 Cgko::executor_deleter< T[]>
 Cstd::false_type
  Cgko::solver::has_with_criteria< SolverType, typename >Helper structure to test if the Factory of SolverType has a function with_criteria
 Cgko::matrix::FbcsrBuilder< ValueType, IndexType >
 Cgko::halfA class providing basic support for half precision floating point types
 Cgko::hip_streamAn RAII wrapper for a custom HIP stream
 Chwloc_obj_t
 Chwloc_obj_type_t
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::add >
  Cgko::accessor::add< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::bitwise_and >
  Cgko::accessor::bitwise_and< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::bitwise_or >
  Cgko::accessor::bitwise_or< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::bitwise_xor >
  Cgko::accessor::bitwise_xor< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::div >
  Cgko::accessor::div< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::equal >
  Cgko::accessor::equal< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::greater >
  Cgko::accessor::greater< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::greater_or_equal >
  Cgko::accessor::greater_or_equal< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::left_shift >
  Cgko::accessor::left_shift< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::less >
  Cgko::accessor::less< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::less_or_equal >
  Cgko::accessor::less_or_equal< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::logical_and >
  Cgko::accessor::logical_and< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::logical_or >
  Cgko::accessor::logical_or< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::max_operation >
  Cgko::accessor::max_operation< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::min_operation >
  Cgko::accessor::min_operation< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::mod >
  Cgko::accessor::mod< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::mul >
  Cgko::accessor::mul< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::not_equal >
  Cgko::accessor::not_equal< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::right_shift >
  Cgko::accessor::right_shift< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_binary_operation< Kind, FirstOperand, SecondOperand, ::gko::accessor::detail::sub >
  Cgko::accessor::sub< Kind, FirstOperand, SecondOperand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::abs_operation >
  Cgko::accessor::abs_operation< Operand >
   Cgko::accessor::abs_operaton< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::bitwise_not >
  Cgko::accessor::bitwise_not< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::conj_operation >
  Cgko::accessor::conj_operation< Operand >
   Cgko::accessor::conj_operaton< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::imag_operation >
  Cgko::accessor::imag_operation< Operand >
   Cgko::accessor::imag_operaton< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::logical_not >
  Cgko::accessor::logical_not< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::one_operation >
  Cgko::accessor::one_operation< Operand >
   Cgko::accessor::one_operaton< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::real_operation >
  Cgko::accessor::real_operation< Operand >
   Cgko::accessor::real_operaton< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::squared_norm_operation >
  Cgko::accessor::squared_norm_operation< Operand >
   Cgko::accessor::squared_norm_operaton< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::unary_minus >
  Cgko::accessor::unary_minus< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::unary_plus >
  Cgko::accessor::unary_plus< Operand >
 Cgko::detail::implement_unary_operation< Operand, ::gko::accessor::detail::zero_operation >
  Cgko::accessor::zero_operation< Operand >
 Cgko::experimental::distributed::index_map< LocalIndexType, GlobalIndexType >This class defines mappings between global and local indices
 Cgko::index_set< IndexType >An index set class represents an ordered set of intervals
 Cgko::log::iteration_complete_dataStruct representing iteration complete related data
 Cgko::solver::IterativeBaseA LinOp implementing this interface stores a stopping criterion factory
  Cgko::solver::EnableIterativeBase< Bicg< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Bicg< default_precision > >
    Cgko::solver::Bicg< ValueType >BICG or the Biconjugate gradient method is a Krylov subspace solver
  Cgko::solver::EnableIterativeBase< Bicgstab< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Bicgstab< default_precision > >
    Cgko::solver::Bicgstab< ValueType >BiCGSTAB or the Bi-Conjugate Gradient-Stabilized is a Krylov subspace solver
  Cgko::solver::EnableIterativeBase< CbGmres< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, CbGmres< default_precision > >
    Cgko::solver::CbGmres< ValueType >CB-GMRES or the compressed basis generalized minimal residual method is an iterative type Krylov subspace method which is suitable for nonsymmetric linear systems
  Cgko::solver::EnableIterativeBase< Cg< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Cg< default_precision > >
    Cgko::solver::Cg< ValueType >CG or the conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods
  Cgko::solver::EnableIterativeBase< Cgs< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Cgs< default_precision > >
    Cgko::solver::Cgs< ValueType >CGS or the conjugate gradient square method is an iterative type Krylov subspace method which is suitable for general systems
  Cgko::solver::EnableIterativeBase< Chebyshev< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Chebyshev< default_precision > >
    Cgko::solver::Chebyshev< ValueType >Chebyshev iteration is an iterative method for solving nonsymmetric problems based on some knowledge of the spectrum of the (preconditioned) system matrix
  Cgko::solver::EnableIterativeBase< Fcg< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Fcg< default_precision > >
    Cgko::solver::Fcg< ValueType >FCG or the flexible conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods
  Cgko::solver::EnableIterativeBase< Gcr< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Gcr< default_precision > >
    Cgko::solver::Gcr< ValueType >GCR or the generalized conjugate residual method is an iterative type Krylov subspace method similar to GMRES which is suitable for nonsymmetric linear systems
  Cgko::solver::EnableIterativeBase< Gmres< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Gmres< default_precision > >
    Cgko::solver::Gmres< ValueType >GMRES or the generalized minimal residual method is an iterative type Krylov subspace method which is suitable for nonsymmetric linear systems
  Cgko::solver::EnableIterativeBase< Idr< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Idr< default_precision > >
    Cgko::solver::Idr< ValueType >IDR(s) is an efficient method for solving large nonsymmetric systems of linear equations
  Cgko::solver::EnableIterativeBase< Minres< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Minres< default_precision > >
    Cgko::solver::Minres< ValueType >Minres is an iterative type Krylov subspace method, which is suitable for indefinite and full-rank symmetric/hermitian operators
  Cgko::solver::EnableIterativeBase< Multigrid >
   Cgko::solver::MultigridMultigrid methods have a hierarchy of many levels, whose corase level is a subset of the fine level, of the problem
  Cgko::solver::EnableIterativeBase< PipeCg< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, PipeCg< default_precision > >
    Cgko::solver::PipeCg< ValueType >PIPE_CG or the pipelined conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods
  Cgko::solver::EnableIterativeBase< DerivedType >A LinOp deriving from this CRTP class stores a stopping criterion factory and allows applying with a guess
   Cgko::solver::EnablePreconditionedIterativeSolver< ValueType, DerivedType >A LinOp implementing this interface stores a system matrix and stopping criterion factory
    Cgko::solver::Bicg< ValueType >
    Cgko::solver::Bicgstab< ValueType >
    Cgko::solver::Cg< ValueType >
    Cgko::solver::Cgs< ValueType >
    Cgko::solver::Chebyshev< ValueType >
    Cgko::solver::Fcg< ValueType >
    Cgko::solver::Gcr< ValueType >
    Cgko::solver::Gmres< ValueType >
    Cgko::solver::Idr< ValueType >
    Cgko::solver::Minres< ValueType >
    Cgko::solver::PipeCg< ValueType >
 Cgko::log::linop_dataStruct representing LinOp related data
 Cgko::log::linop_factory_dataStruct representing LinOp factory related data
 Cgko::log::LoggableLoggable class is an interface which should be implemented by classes wanting to support logging
  Cgko::log::EnableLogging< Executor >
   Cgko::ExecutorThe first step in using the Ginkgo library consists of creating an executor
    Cgko::detail::ExecutorBase< CudaExecutor >
     Cgko::CudaExecutorThis is the Executor subclass which represents the CUDA device
    Cgko::detail::ExecutorBase< DpcppExecutor >
     Cgko::DpcppExecutorThis is the Executor subclass which represents a DPC++ enhanced device
    Cgko::detail::ExecutorBase< HipExecutor >
     Cgko::HipExecutorThis is the Executor subclass which represents the HIP enhanced device
    Cgko::detail::ExecutorBase< OmpExecutor >
     Cgko::OmpExecutorThis is the Executor subclass which represents the OpenMP device (typically CPU)
  Cgko::log::EnableLogging< PolymorphicObject >
   Cgko::PolymorphicObjectA PolymorphicObject is the abstract base for all "heavy" objects in Ginkgo that behave polymorphically
  Cgko::log::EnableLogging< ConcreteLoggable, PolymorphicBase >EnableLogging is a mixin which should be inherited by any class which wants to enable logging
 Cgko::log::Record::logged_dataStruct storing the actually logged data
 Cgko::log::Logger
  Cgko::batch::log::BatchConvergence< ValueType >Logs the final residuals and iteration counts for a batch solver
  Cgko::log::Convergence< ValueType >Convergence is a Logger which logs data strictly from the criterion_check_completed event
  Cgko::log::Papi< ValueType >Papi is a Logger which logs every event to the PAPI software
  Cgko::log::PerformanceHintPerformanceHint is a Logger which analyzes the performance of the application and outputs hints for unnecessary copies and allocations
  Cgko::log::ProfilerHookThis Logger can be used to annotate the execution of Ginkgo functionality with profiler-specific ranges
  Cgko::log::RecordRecord is a Logger which logs every event to an object
  Cgko::log::SolverProgressThis Logger outputs the value of all scalar values (and potentially vectors) stored internally by the solver after each iteration
  Cgko::log::Stream< ValueType >Stream is a Logger which logs every event to a stream
 Cgko::machine_topologyThe machine topology class represents the hierarchical topology of a machine, including NUMA nodes, cores and PCI Devices
 Cgko::matrix_assembly_data< ValueType, IndexType >This structure is used as an intermediate type to assemble a sparse matrix
 Cgko::matrix_data< ValueType, IndexType >This structure is used as an intermediate data type to store a sparse matrix
 Cgko::matrix_data_entry< ValueType, IndexType >Type used to store nonzeros
 Cgko::accessor::mmul_operation< Kind, FirstAccessor, SecondAccessor >
 Cgko::multigrid::MultigridLevelThis class represents two levels in a multigrid hierarchy
  Cgko::multigrid::EnableMultigridLevel< default_precision >
   Cgko::multigrid::FixedCoarsening< ValueType, IndexType >FixedCoarsening is a very simple coarse grid generation algorithm
   Cgko::multigrid::Pgm< ValueType, IndexType >Parallel graph match (Pgm) is the aggregate method introduced in the paper M
  Cgko::multigrid::EnableMultigridLevel< ValueType >The EnableMultigridLevel gives the default implementation of MultigridLevel with composition and provides set_multigrid_level function
   Cgko::multigrid::Pgm< ValueType, IndexType >
 Cgko::matrix::Csr< ValueType, IndexType >::multiply_add_reuse_infoClass describing the internal lookup structures created by multiply_add_reuse to recompute a sparse matrix-matrix product with updated values
 Cgko::matrix::Csr< ValueType, IndexType >::multiply_reuse_infoClass describing the internal lookup structures created by multiply_reuse(const Csr*) to recompute a sparse matrix-matrix product with updated values
 Cgko::log::ProfilerHook::nested_summary_entry
 Cgko::log::ProfilerHook::NestedSummaryWriterReceives the results from ProfilerHook::create_nested_summary()
  Cgko::log::ProfilerHook::TableSummaryWriterWrites the results from ProfilerHook::create_summary() and ProfilerHook::create_nested_summary() to a ASCII table in Markdown format
 Cgko::null_deleter< T >This is a deleter that does not delete the object
 Cgko::null_deleter< T[]>
 Cgko::nvidia_deviceNvidia_device handles the number of executor on Nvidia devices and have the corresponding recursive_mutex
 Cgko::OperationOperations can be used to define functionalities whose implementations differ among devices
 Cgko::log::operation_dataStruct representing Operator related data
 Cgko::Permutable< IndexType >Linear operators which support permutation should implement the Permutable interface
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< ValueType, IndexType >
 Cgko::Permutable< int32 >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
 Cgko::Permutable< int64 >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
 Cgko::matrix::Csr< ValueType, IndexType >::permuting_reuse_infoA struct describing a transformation of the matrix that reorders the values of the matrix into the transformed matrix
 Cgko::config::pnodePnode describes a tree of properties
 Cgko::log::polymorphic_object_dataStruct representing PolymorphicObject related data
 Cgko::precision_reductionThis class is used to encode storage precisions of low precision algorithms
 Cgko::PreconditionableA LinOp implementing this interface can be preconditioned
  Cgko::solver::EnablePreconditionable< Bicg< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Bicg< default_precision > >
  Cgko::solver::EnablePreconditionable< Bicgstab< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Bicgstab< default_precision > >
  Cgko::solver::EnablePreconditionable< CbGmres< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, CbGmres< default_precision > >
  Cgko::solver::EnablePreconditionable< Cg< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Cg< default_precision > >
  Cgko::solver::EnablePreconditionable< Cgs< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Cgs< default_precision > >
  Cgko::solver::EnablePreconditionable< Chebyshev< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Chebyshev< default_precision > >
  Cgko::solver::EnablePreconditionable< Fcg< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Fcg< default_precision > >
  Cgko::solver::EnablePreconditionable< Gcr< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Gcr< default_precision > >
  Cgko::solver::EnablePreconditionable< Gmres< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Gmres< default_precision > >
  Cgko::solver::EnablePreconditionable< Idr< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Idr< default_precision > >
  Cgko::solver::EnablePreconditionable< Minres< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Minres< default_precision > >
  Cgko::solver::EnablePreconditionable< PipeCg< default_precision > >
   Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, PipeCg< default_precision > >
  Cgko::solver::EnablePreconditionable< DerivedType >Mixin providing default operation for Preconditionable with correct value semantics
   Cgko::solver::EnablePreconditionedIterativeSolver< ValueType, DerivedType >A LinOp implementing this interface stores a system matrix and stopping criterion factory
 Cgko::log::profiling_scope_guardScope guard that annotates its scope with the provided profiler hooks
 Cgko::ptr_param< T >This class is used for function parameters in the place of raw pointers
 Cgko::range< Accessor >A range is a multidimensional view of the memory
 Cgko::syn::range< Start, End, Step >Range records start, end, step in template
 Cgko::ReadableFromMatrixData< ValueType, IndexType >A LinOp implementing this interface can read its data from a matrix_data structure
  Cgko::matrix::Coo< ValueType, IndexType >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Diagonal< ValueType >
  Cgko::matrix::Diagonal< ValueType >
  Cgko::matrix::Ell< value_type, index_type >
  Cgko::matrix::Ell< ValueType, IndexType >
  Cgko::matrix::Fbcsr< ValueType, IndexType >
  Cgko::matrix::SparsityCsr< value_type, index_type >
  Cgko::matrix::SparsityCsr< IndexType, ValueType >
  Cgko::matrix::SparsityCsr< ValueType, IndexType >
 Cgko::ReadableFromMatrixData< default_precision, int32 >
  Cgko::matrix::Coo< ValueType, IndexType >COO stores a matrix in the coordinate matrix format
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
  Cgko::matrix::Ell< ValueType, IndexType >ELL is a matrix format where stride with explicit zeros is used such that all rows have the same number of stored elements
  Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
  Cgko::matrix::Hybrid< ValueType, IndexType >HYBRID is a matrix format which splits the matrix into ELLPACK and COO format
  Cgko::matrix::Sellp< ValueType, IndexType >SELL-P is a matrix format similar to ELL format
  Cgko::matrix::SparsityCsr< ValueType, IndexType >SparsityCsr is a matrix format which stores only the sparsity pattern of a sparse matrix by compressing each row of the matrix (compressed sparse row format)
 Cgko::ReadableFromMatrixData< default_precision, int64 >
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
 Cgko::config::registryThis class stores additional context for creating Ginkgo objects from configuration files
 Cgko::reorder::ReorderingBaseArgsThis struct is used to pass parameters to the EnableDefaultReorderingBaseFactory::generate() method
 Cgko::experimental::mpi::requestLight, move-only wrapper around the MPI_Request handle
 Cgko::accessor::row_major< ValueType, Dimensionality >A row_major accessor is a bridge between a range and the row-major memory layout
 Cgko::matrix::Csr< ValueType, IndexType >::scale_add_reuse_infoClass describing the internal lookup structures created by scale_add_reuse to recompute a sparse matrix-matrix sum with updated values
 Cgko::ScaledIdentityAddableAdds the operation M <- a I + b M for matrix M, identity operator I and scalars a and b, where M is the calling object
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
 Cgko::scoped_device_id_guardThis move-only class uses RAII to set the device id within a scoped block, if necessary
 Cgko::segmented_array< T >A minimal interface for a segmented array
 Cgko::solver::detail::SolverBaseLinOpA LinOp implementing this interface stores a system matrix
  Cgko::solver::SolverBase< factorization::Factorization< ValueType, IndexType > >
   Cgko::solver::EnableSolverBase< Direct< ValueType, IndexType >, factorization::Factorization< ValueType, IndexType > >
    Cgko::experimental::solver::Direct< ValueType, IndexType >A direct solver based on a factorization into lower and upper triangular factors (with an optional diagonal scaling)
  Cgko::solver::SolverBase< LinOp >
   Cgko::solver::EnableSolverBase< Bicg< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Bicg< default_precision > >
   Cgko::solver::EnableSolverBase< Bicgstab< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Bicgstab< default_precision > >
   Cgko::solver::EnableSolverBase< CbGmres< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, CbGmres< default_precision > >
   Cgko::solver::EnableSolverBase< Cg< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Cg< default_precision > >
   Cgko::solver::EnableSolverBase< Cgs< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Cgs< default_precision > >
   Cgko::solver::EnableSolverBase< Chebyshev< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Chebyshev< default_precision > >
   Cgko::solver::EnableSolverBase< Fcg< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Fcg< default_precision > >
   Cgko::solver::EnableSolverBase< Gcr< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Gcr< default_precision > >
   Cgko::solver::EnableSolverBase< Gmres< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Gmres< default_precision > >
   Cgko::solver::EnableSolverBase< Idr< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Idr< default_precision > >
   Cgko::solver::EnableSolverBase< Minres< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, Minres< default_precision > >
   Cgko::solver::EnableSolverBase< Multigrid >
    Cgko::solver::MultigridMultigrid methods have a hierarchy of many levels, whose corase level is a subset of the fine level, of the problem
   Cgko::solver::EnableSolverBase< PipeCg< default_precision > >
    Cgko::solver::EnablePreconditionedIterativeSolver< default_precision, PipeCg< default_precision > >
   Cgko::solver::EnableSolverBase< DerivedType, MatrixType >A LinOp deriving from this CRTP class stores a system matrix
    Cgko::solver::LowerTrs< value_type, index_type >
    Cgko::solver::LowerTrs< ValueType, IndexType >
    Cgko::solver::UpperTrs< value_type, index_type >
    Cgko::solver::UpperTrs< ValueType, IndexType >
    Cgko::solver::EnablePreconditionedIterativeSolver< ValueType, DerivedType >A LinOp implementing this interface stores a system matrix and stopping criterion factory
  Cgko::solver::SolverBase< matrix::Csr< default_precision, int32 > >
   Cgko::solver::EnableSolverBase< LowerTrs< default_precision, int32 >, matrix::Csr< default_precision, int32 > >
    Cgko::solver::LowerTrs< ValueType, IndexType >LowerTrs is the triangular solver which solves the system L x = b, when L is a lower triangular matrix
   Cgko::solver::EnableSolverBase< UpperTrs< default_precision, int32 >, matrix::Csr< default_precision, int32 > >
    Cgko::solver::UpperTrs< ValueType, IndexType >UpperTrs is the triangular solver which solves the system U x = b, when U is an upper triangular matrix
  Cgko::solver::SolverBase< MatrixType >
 Cgko::spanA span is a lightweight structure used to create sub-ranges from other ranges
  Cgko::local_spanA span that is used exclusively for local numbering
 Cgko::experimental::mpi::statusThe status struct is a light wrapper around the MPI_Status struct
 Cgko::stopping_statusThis class is used to keep track of the stopping status of one vector
 Cgko::matrix::Csr< ValueType, IndexType >::strategy_typeStrategy_type is to decide how to set the csr algorithm
  Cgko::matrix::Csr< ValueType, IndexType >::automatical
  Cgko::matrix::Csr< ValueType, IndexType >::classicalClassical is a strategy_type which uses the same number of threads on each row
  Cgko::matrix::Csr< ValueType, IndexType >::cusparseCusparse is a strategy_type which uses the sparselib csr
  Cgko::matrix::Csr< ValueType, IndexType >::load_balanceLoad_balance is a strategy_type which uses the load balance algorithm
  Cgko::matrix::Csr< ValueType, IndexType >::merge_pathMerge_path is a strategy_type which uses the merge_path algorithm
  Cgko::matrix::Csr< ValueType, IndexType >::sparselibSparselib is a strategy_type which uses the sparselib csr
 Cgko::matrix::Hybrid< ValueType, IndexType >::strategy_typeStrategy_type is to decide how to set the hybrid config
  Cgko::matrix::Hybrid< ValueType, IndexType >::automaticAutomatic is a strategy_type which decides the number of stored elements per row of the ell part automatically
  Cgko::matrix::Hybrid< ValueType, IndexType >::column_limitColumn_limit is a strategy_type which decides the number of stored elements per row of the ell part by specifying the number of columns
  Cgko::matrix::Hybrid< ValueType, IndexType >::imbalance_bounded_limitImbalance_bounded_limit is a strategy_type which decides the number of stored elements per row of the ell part
  Cgko::matrix::Hybrid< ValueType, IndexType >::imbalance_limitImbalance_limit is a strategy_type which decides the number of stored elements per row of the ell part according to the percent
  Cgko::matrix::Hybrid< ValueType, IndexType >::minimal_storage_limitMinimal_storage_limit is a strategy_type which decides the number of stored elements per row of the ell part
 Cgko::log::ProfilerHook::summary_entry
 Cgko::log::ProfilerHook::SummaryWriterReceives the results from ProfilerHook::create_summary()
  Cgko::log::ProfilerHook::TableSummaryWriterWrites the results from ProfilerHook::create_summary() and ProfilerHook::create_nested_summary() to a ASCII table in Markdown format
 Cgko::time_pointAn opaque wrapper for a time point generated by a timer
 Cgko::TimerRepresents a generic timer that can be used to record time points and measure time differences on host or device streams
  Cgko::CpuTimerA timer using std::chrono::steady_clock for timing
  Cgko::CudaTimerA timer using events for timing on a CudaExecutor
  Cgko::DpcppTimerA timer using kernels for timing on a DpcppExecutor in profiling mode
  Cgko::HipTimerA timer using events for timing on a HipExecutor
 Cgko::TransposableLinear operators which support transposition should implement the Transposable interface
  Cgko::Combination< ValueType >
  Cgko::Composition< ValueType >
  Cgko::Composition< value_type >
  Cgko::Composition< gko::default_precision >
  Cgko::Composition< default_precision >
  Cgko::matrix::Coo< ValueType, IndexType >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Diagonal< ValueType >
  Cgko::matrix::Fbcsr< ValueType, IndexType >
  Cgko::matrix::Identity< ValueType >
  Cgko::matrix::SparsityCsr< value_type, index_type >
  Cgko::matrix::SparsityCsr< IndexType, ValueType >
  Cgko::matrix::SparsityCsr< ValueType, IndexType >
  Cgko::preconditioner::Ic< LSolverTypeOrValueType, IndexType >
  Cgko::preconditioner::Isai< IsaiType==isai_type::general ? isai_type::general :IsaiType==isai_type::spd ? isai_type::spd :IsaiType==isai_type::lower ? isai_type::upper :isai_type::lower, ValueType, IndexType >
  Cgko::preconditioner::Isai< isai_type::lower, ValueType, IndexType >
  Cgko::preconditioner::Isai< isai_type::upper, ValueType, IndexType >
  Cgko::preconditioner::Isai< isai_type::general, ValueType, IndexType >
  Cgko::preconditioner::Isai< isai_type::spd, ValueType, IndexType >
  Cgko::preconditioner::Jacobi< ValueType, IndexType >
  Cgko::solver::Bicg< ValueType >
  Cgko::solver::Bicgstab< ValueType >
  Cgko::solver::Cg< ValueType >
  Cgko::solver::Cgs< ValueType >
  Cgko::solver::Chebyshev< ValueType >
  Cgko::solver::Fcg< ValueType >
  Cgko::solver::Gcr< ValueType >
  Cgko::solver::Gmres< ValueType >
  Cgko::solver::Idr< ValueType >
  Cgko::solver::LowerTrs< value_type, index_type >
  Cgko::solver::LowerTrs< ValueType, IndexType >
  Cgko::solver::Minres< ValueType >
  Cgko::solver::PipeCg< ValueType >
  Cgko::solver::UpperTrs< value_type, index_type >
  Cgko::solver::UpperTrs< ValueType, IndexType >
  Cgko::Combination< ValueType >The Combination class can be used to construct a linear combination of multiple linear operators c1 * op1 + c2 * op2 + ... + ck * opk
  Cgko::Composition< ValueType >The Composition class can be used to compose linear operators op1, op2, ..., opn and obtain the operator op1 * op2 * ... * opn
  Cgko::experimental::solver::Direct< ValueType, IndexType >A direct solver based on a factorization into lower and upper triangular factors (with an optional diagonal scaling)
  Cgko::matrix::Coo< ValueType, IndexType >COO stores a matrix in the coordinate matrix format
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
  Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
  Cgko::matrix::FftThis LinOp implements a 1D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft2This LinOp implements a 2D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft3This LinOp implements a 3D Fourier matrix using the FFT algorithm
  Cgko::matrix::Identity< ValueType >This class is a utility which efficiently implements the identity matrix (a linear operator which maps each vector to itself)
  Cgko::matrix::SparsityCsr< ValueType, IndexType >SparsityCsr is a matrix format which stores only the sparsity pattern of a sparse matrix by compressing each row of the matrix (compressed sparse row format)
  Cgko::preconditioner::Ic< LSolverTypeOrValueType, IndexType >The Incomplete Cholesky (IC) preconditioner solves the equation $LL^H*x = b$ for a given lower triangular matrix L and the right hand side b (can contain multiple right hand sides)
  Cgko::preconditioner::Ilu< LSolverTypeOrValueType, USolverTypeOrValueType, ReverseApply, IndexType >The Incomplete LU (ILU) preconditioner solves the equation $LUx = b$ for a given lower triangular matrix L, an upper triangular matrix U and the right hand side b (can contain multiple right hand sides)
  Cgko::preconditioner::Isai< IsaiType, ValueType, IndexType >The Incomplete Sparse Approximate Inverse (ISAI) Preconditioner generates an approximate inverse matrix for a given square matrix A, lower triangular matrix L, upper triangular matrix U or symmetric positive (spd) matrix B
  Cgko::preconditioner::Jacobi< ValueType, IndexType >A block-Jacobi preconditioner is a block-diagonal linear operator, obtained by inverting the diagonal blocks of the source operator
  Cgko::solver::Bicg< ValueType >BICG or the Biconjugate gradient method is a Krylov subspace solver
  Cgko::solver::Bicgstab< ValueType >BiCGSTAB or the Bi-Conjugate Gradient-Stabilized is a Krylov subspace solver
  Cgko::solver::Cg< ValueType >CG or the conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods
  Cgko::solver::Cgs< ValueType >CGS or the conjugate gradient square method is an iterative type Krylov subspace method which is suitable for general systems
  Cgko::solver::Chebyshev< ValueType >Chebyshev iteration is an iterative method for solving nonsymmetric problems based on some knowledge of the spectrum of the (preconditioned) system matrix
  Cgko::solver::Fcg< ValueType >FCG or the flexible conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods
  Cgko::solver::Gcr< ValueType >GCR or the generalized conjugate residual method is an iterative type Krylov subspace method similar to GMRES which is suitable for nonsymmetric linear systems
  Cgko::solver::Gmres< ValueType >GMRES or the generalized minimal residual method is an iterative type Krylov subspace method which is suitable for nonsymmetric linear systems
  Cgko::solver::Idr< ValueType >IDR(s) is an efficient method for solving large nonsymmetric systems of linear equations
  Cgko::solver::LowerTrs< ValueType, IndexType >LowerTrs is the triangular solver which solves the system L x = b, when L is a lower triangular matrix
  Cgko::solver::Minres< ValueType >Minres is an iterative type Krylov subspace method, which is suitable for indefinite and full-rank symmetric/hermitian operators
  Cgko::solver::PipeCg< ValueType >PIPE_CG or the pipelined conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods
  Cgko::solver::UpperTrs< ValueType, IndexType >UpperTrs is the triangular solver which solves the system U x = b, when U is an upper triangular matrix
 Cgko::accessor::transpose_operation< Accessor >
 Cstd::true_type
  Cgko::are_all_integral< First, Args... >
  Cgko::are_all_integral< Args >Evaluates if all template arguments Args fulfill std::is_integral
 Cgko::truncated< typename, size_t, size_t >
 Cstd::conditional::type
  Cgko::are_all_integral< First, Args... >
 Cgko::config::type_descriptorThis class describes the value and index types to be used when building a Ginkgo type from a configuration file
 Cgko::xstd::type_identity< T >
 Cgko::experimental::mpi::type_impl< T >A struct that is used to determine the MPI_Datatype of a specified type
 Cgko::experimental::mpi::type_impl< char >
 Cgko::experimental::mpi::type_impl< double >
 Cgko::experimental::mpi::type_impl< float >
 Cgko::experimental::mpi::type_impl< int >
 Cgko::experimental::mpi::type_impl< long >
 Cgko::experimental::mpi::type_impl< long double >
 Cgko::experimental::mpi::type_impl< long long >
 Cgko::experimental::mpi::type_impl< unsigned >
 Cgko::experimental::mpi::type_impl< unsigned char >
 Cgko::experimental::mpi::type_impl< unsigned long >
 Cgko::experimental::mpi::type_impl< unsigned long long >
 Cgko::experimental::mpi::type_impl< unsigned short >
 Cgko::syn::type_list< Types >Type_list records several types in template
 Cgko::stop::Criterion::UpdaterServes for convenient argument passing to the Criterion's check function
 Cgko::UseComposition< ValueType >The UseComposition class can be used to store the composition information in LinOp
 Cgko::UseComposition< default_precision >
  Cgko::multigrid::EnableMultigridLevel< default_precision >
 Cgko::UseComposition< ValueType >
  Cgko::multigrid::EnableMultigridLevel< ValueType >The EnableMultigridLevel gives the default implementation of MultigridLevel with composition and provides set_multigrid_level function
 Cgko::syn::value_list< T, Values >Value_list records several values with the same type in template
 Cgko::versionThis structure is used to represent versions of various Ginkgo modules
 Cgko::version_infoGinkgo uses version numbers to label new features and to communicate backward compatibility guarantees:
 Cgko::experimental::mpi::window< ValueType >This class wraps the MPI_Window class with RAII functionality
 Cgko::solver::workspace_traits< Solver >Traits class providing information on the type and location of workspace vectors inside a solver
 Cgko::solver::workspace_traits< Bicg< ValueType > >
 Cgko::solver::workspace_traits< Bicgstab< ValueType > >
 Cgko::solver::workspace_traits< Cg< ValueType > >
 Cgko::solver::workspace_traits< Cgs< ValueType > >
 Cgko::solver::workspace_traits< Chebyshev< ValueType > >
 Cgko::solver::workspace_traits< Fcg< ValueType > >
 Cgko::solver::workspace_traits< Gcr< ValueType > >
 Cgko::solver::workspace_traits< gko::experimental::solver::Direct< ValueType, IndexType > >
 Cgko::solver::workspace_traits< Gmres< ValueType > >
 Cgko::solver::workspace_traits< Idr< ValueType > >
 Cgko::solver::workspace_traits< LowerTrs< ValueType, IndexType > >
 Cgko::solver::workspace_traits< Minres< ValueType > >
 Cgko::solver::workspace_traits< Multigrid >
 Cgko::solver::workspace_traits< PipeCg< ValueType > >
 Cgko::solver::workspace_traits< UpperTrs< ValueType, IndexType > >
 Cgko::WritableToMatrixData< ValueType, IndexType >A LinOp implementing this interface can write its data to a matrix_data structure
  Cgko::matrix::Coo< ValueType, IndexType >
  Cgko::matrix::Csr< value_type, index_type >
  Cgko::matrix::Csr< ValueType, IndexType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< ValueType >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< value_type >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< to_complex< ValueType > >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< absolute_type >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Dense< remove_complex< ValueType > >
  Cgko::matrix::Diagonal< ValueType >
  Cgko::matrix::Diagonal< ValueType >
  Cgko::matrix::Ell< value_type, index_type >
  Cgko::matrix::Ell< ValueType, IndexType >
  Cgko::matrix::Fbcsr< ValueType, IndexType >
  Cgko::matrix::Permutation< index_type >
  Cgko::matrix::Permutation< IndexType >
  Cgko::matrix::SparsityCsr< value_type, index_type >
  Cgko::matrix::SparsityCsr< IndexType, ValueType >
  Cgko::matrix::SparsityCsr< ValueType, IndexType >
  Cgko::preconditioner::Jacobi< ValueType, IndexType >
 Cgko::WritableToMatrixData< default_precision, int32 >
  Cgko::matrix::Coo< ValueType, IndexType >COO stores a matrix in the coordinate matrix format
  Cgko::matrix::Csr< ValueType, IndexType >CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format)
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
  Cgko::matrix::Ell< ValueType, IndexType >ELL is a matrix format where stride with explicit zeros is used such that all rows have the same number of stored elements
  Cgko::matrix::Fbcsr< ValueType, IndexType >Fixed-block compressed sparse row storage matrix format
  Cgko::matrix::Hybrid< ValueType, IndexType >HYBRID is a matrix format which splits the matrix into ELLPACK and COO format
  Cgko::matrix::Permutation< IndexType >Permutation is a matrix format that represents a permutation matrix, i.e
  Cgko::matrix::ScaledPermutation< ValueType, IndexType >ScaledPermutation is a matrix combining a permutation with scaling factors
  Cgko::matrix::Sellp< ValueType, IndexType >SELL-P is a matrix format similar to ELL format
  Cgko::matrix::SparsityCsr< ValueType, IndexType >SparsityCsr is a matrix format which stores only the sparsity pattern of a sparse matrix by compressing each row of the matrix (compressed sparse row format)
  Cgko::preconditioner::Jacobi< ValueType, IndexType >A block-Jacobi preconditioner is a block-diagonal linear operator, obtained by inverting the diagonal blocks of the source operator
 Cgko::WritableToMatrixData< default_precision, int64 >
  Cgko::matrix::Dense< ValueType >Dense is a matrix format which explicitly stores all values of the matrix
  Cgko::matrix::Diagonal< ValueType >This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise)
 Cgko::WritableToMatrixData< std::complex< double >, int32 >
  Cgko::matrix::FftThis LinOp implements a 1D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft2This LinOp implements a 2D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft3This LinOp implements a 3D Fourier matrix using the FFT algorithm
 Cgko::WritableToMatrixData< std::complex< double >, int64 >
  Cgko::matrix::FftThis LinOp implements a 1D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft2This LinOp implements a 2D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft3This LinOp implements a 3D Fourier matrix using the FFT algorithm
 Cgko::WritableToMatrixData< std::complex< float >, int32 >
  Cgko::matrix::FftThis LinOp implements a 1D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft2This LinOp implements a 2D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft3This LinOp implements a 3D Fourier matrix using the FFT algorithm
 Cgko::WritableToMatrixData< std::complex< float >, int64 >
  Cgko::matrix::FftThis LinOp implements a 1D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft2This LinOp implements a 2D Fourier matrix using the FFT algorithm
  Cgko::matrix::Fft3This LinOp implements a 3D Fourier matrix using the FFT algorithm