Module sparse_utils 

Sparse matrix utility operations

This module provides fundamental sparse matrix operations including:

• Norms: 1-norm, infinity-norm, Frobenius norm
• SpGEMM: Sparse matrix-matrix multiplication
• Arithmetic: Sparse addition, subtraction, scaling
• Kronecker product: Sparse Kronecker (tensor) product
• Reordering: Reverse Cuthill-McKee bandwidth reduction
• Condition number estimate: Cheap 1-norm-based condition estimate

Structs

RcmResult

Result of the Reverse Cuthill-McKee algorithm.

Enums

SparseNorm

Type of matrix norm to compute.

Functions

condest_1norm

Estimate the 1-norm condition number of a sparse matrix.

permute_matrix

Apply a permutation to a sparse matrix: P * A * P^T.

reverse_cuthill_mckee

Compute the Reverse Cuthill-McKee permutation of a sparse matrix.

sparse_add

Sparse matrix addition: C = alpha * A + beta * B.

sparse_extract_diagonal

Extract the diagonal of a sparse CSR matrix as a dense vector.

sparse_identity

Create an n x n sparse identity matrix in CSR format.

sparse_kronecker

Compute the Kronecker product C = A kron B.

sparse_matrix_norm

Compute a matrix norm of a sparse CSR matrix.

sparse_matrix_trace

Compute the trace of a sparse matrix (sum of diagonal elements).

sparse_scale

Scale a sparse matrix: C = alpha * A.

sparse_sub

Sparse matrix subtraction: C = A - B.

sparse_transpose

Compute the transpose of a sparse CSR matrix (returns a new CSR matrix).

spgemm

Sparse matrix-matrix multiplication: C = A * B.