apxchol 0.1.1

CPU approximate-Cholesky preconditioner for graph-Laplacian / SDDM linear systems

apxchol (Python, CPU)

Approximate-Cholesky preconditioner for graph-Laplacian / SDDM linear systems.

pip install apxchol

Prebuilt wheels: Linux x86_64 (manylinux), CPython 3.10–3.14. CPU only.

import apxchol

solver = apxchol.solver(A)              # scipy sparse SPD Laplacian or SDDM; factor once
res = solver.solve(b, tol=1e-8, maxiter=500)
res.x, res.iters, res.residual, res.converged

z = solver.apply(r)                     # M^{-1} r
M = solver.aslinearoperator()           # use as M= in scipy.sparse.linalg.cg

res = apxchol.solve(A, b)               # one-shot convenience

Laplacian vs SDDM is auto-detected: singular Laplacians get a rank-(n−1) factor with native null-space handling; SDDM systems get the full-rank factor. The factor is built once per solver(A) and reused across right-hand sides; solve runs the library's OpenMP-parallel PCG (threads via OMP_NUM_THREADS).

Defaults only — tuning parameters, GPU, and 64-bit indices (n or nnz ≥ 2³¹) are not exposed in this version.

License

Interim academic-evaluation license: free for academic research and evaluation; redistribution and commercial use prohibited. See LICENSE.

From source (requires repository access)

The wheel build compiles the library's two core translation units directly; building from a repo checkout works the same way:

pip install -e python          # from the repository root
pytest python/tests -v

Source builds use -O3 -march=native (the distributed wheels are built portable). If your environment requires --no-build-isolation, first pip install pybind11 scikit-build-core.