GenericSparsityPattern.h¶

Note

The documentation on this page was automatically extracted from the DOLFIN C++ code and may need to be edited or expanded.

class GenericSparsityPattern¶

Parent class(es)

Variable

Base class (interface) for generic tensor sparsity patterns. Currently, this interface is mostly limited to matrices.

GenericSparsityPattern(std::size_t primary_dim)¶: Create empty sparsity pattern

void init(const MPI_Comm mpi_comm, const std::vector<std::size_t> &dims, const std::vector<std::pair<std::size_t, std::size_t>> &local_range, const std::vector<ArrayView<const std::size_t>> &local_to_global, const std::vector<ArrayView<const int>> &off_process_owner, const std::vector<std::size_t> &block_sizes) = 0¶: Initialize sparsity pattern for a generic tensor

void insert_global(const std::vector<ArrayView<const dolfin::la_index>> &entries) = 0¶: Insert non-zero entries using global indices

void insert_local(const std::vector<ArrayView<const dolfin::la_index>> &entries) = 0¶: Insert non-zero entries using local (process-wise) entries

std::size_t rank() const = 0¶: Return rank

std::size_t primary_dim() const¶: Return primary dimension (e.g., 0=row partition, 1=column partition)

std::pair<std::size_t, std::size_t> local_range(std::size_t dim) const = 0¶: Return local range for dimension dim

std::size_t num_nonzeros() const = 0¶: Return total number of nonzeros in local_range

void num_nonzeros_diagonal(std::vector<std::size_t> &num_nonzeros) const = 0¶: Fill vector with number of nonzeros for diagonal block in local_range for primary dimension

void num_nonzeros_off_diagonal(std::vector<std::size_t> &num_nonzeros) const = 0¶: Fill vector with number of nonzeros for off-diagonal block in local_range for primary dimension

void num_local_nonzeros(std::vector<std::size_t> &num_nonzeros) const = 0¶: Fill vector with number of nonzeros in local_range for primary dimension

std::vector<std::vector<std::size_t>> diagonal_pattern(Type type) const = 0¶: Return underlying sparsity pattern (diagonal). Options are ‘sorted’ and ‘unsorted’.

std::vector<std::vector<std::size_t>> off_diagonal_pattern(Type type) const = 0¶: Return underlying sparsity pattern (off-diagonal). Options are ‘sorted’ and ‘unsorted’.

void apply() = 0¶: Finalize sparsity pattern

MPI_Comm mpi_comm() const = 0¶: Return MPI communicator