gko::reorder#

Permutation generators for sparse matrices. Each reordering algorithm takes a system matrix and produces a Permutation (or ScaledPermutation) that can be applied to the matrix to reduce fill-in (for sparse direct solvers), improve numerical stability, or expose more parallelism. For background and a comparison table see the Reordering concept page.

Reduce fill-in for direct / incomplete factorisations#

  • Rcm — Reverse Cuthill–McKee bandwidth reduction (gko::experimental::reorder::Rcm).

  • Amd — Approximate Minimum Degree (gko::experimental::reorder::Amd).

  • NestedDissection — METIS-based nested dissection (gko::experimental::reorder::NestedDissection, available when Ginkgo is built with METIS).

Improve numerical stability#

  • Mc64 — MC64 row permutation with optional row scaling (gko::experimental::reorder::Mc64); returns a ScaledPermutation.

Composition#

  • ScaledReordered — wraps a system matrix in a reordering + scaling sandwich so a solver works on the permuted system while the application sees the original (gko::experimental::reorder::ScaledReordered).