rslab (Python bindings)

NumPy/SciPy bindings for RSLAB, a pure-Rust sparse direct solver and preconditioner: complex/real symmetric LDLᵀ (Bunch-Kaufman) plus unsymmetric LU. A thin wrapper, all numeric work happens in Rust.

Install

pip install rslab

Usage

import numpy as np
import scipy.sparse as sp
import rslab

# Symmetric system (real or complex; the dtype selects the path).
A = sp.random(5000, 5000, density=1e-3, format="csc") + sp.eye(5000) * 10
A = A + A.T
b = np.random.rand(5000)

# One-shot solve.
x = rslab.spsolve(A, b)

# Factor once, solve many right-hand sides.
f = rslab.ldlt(A)
x1 = f.solve(b)
X = f.solve_many(np.random.rand(5000, 8))   # n x nrhs

print(f.n, f.factor_nnz, f.inertia, f.dtype)

Complex-symmetric matrices (EM/FEM, PARDISO mtype 6) work identically:

A = A.astype(np.complex128); A.data += 1j * 0.3 * A.data.real
x = rslab.ldlt(A).solve(np.ones(A.shape[0], dtype=np.complex128))

Unsymmetric matrices use the LU path:

f = rslab.lu(A_general)
x = f.solve(b)

Circuit-shaped matrices (MNA / SPICE-class: very sparse, unsymmetric, near-triangularizable) use the KLU path, bit-deterministic, with a numeric-only refactor for fixed-pattern sweeps:

f = rslab.klu(A_circuit)
x = f.solve(b)
A_circuit.data *= 1.5            # frequency sweep: same pattern, new values
f.refactor(A_circuit.data)       # no symbolic work, no pivot search
x2 = f.solve(b)
y = f.solve_transpose(b)         # A.T @ y = b on the same factors (adjoint)

solve_transpose is the plain transpose; for the conjugate-transpose adjoint use f.solve_transpose(b.conj()).conj().

Preconditioner mode

Never-fail static pivoting plus iterative refinement for hard/indefinite systems:

f = rslab.ldlt(A, preconditioner=1e-4)
x = f.solve(b, refine=20)        # refine against the original A

Configuration (keyword arguments)

By default ldlt, lu and spsolve use RSLAB's deterministic heuristic pick, the adaptive ordering plus an exact nested-dissection bakeoff on large systems (adopted only on a clear predicted win with no fill/memory regression). A one-time rslab.install_diagnose() measures this machine's throughput and speedup curve and caches it; afterwards the default also picks its worker count from the calibration (until then the conservative capped default applies). Keyword arguments override the pick:

kwarg default meaning
threads None (auto) None = calibrated/structural per-matrix pick; int = fixed (0 = all)
preconditioner None static-pivot floor (e.g. 1e-4); never-fail, refine to solve
drop_tol None incomplete-factor threshold (preconditioner)
method "left_looking" "left_looking" or "multifrontal"
memory "low" "low" or "eager" factor emit strategy
force_accept False accept tiny pivots in exact mode instead of failing

klu accepts:

kwarg default meaning
pivot_tol 1e-3 diagonal-preference threshold; 1.0 = plain partial pivoting
row_scaling True divide each row by its max-magnitude entry before factoring
btf True permute to block upper triangular form first (keep it on)

Supported dtypes: float64, float32, complex128, complex64.

License

MIT.

Project links

Data verified by PyPI on Aug 1, 2026
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Key dates

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  • Released:
Newer release available (0.27.0)

1 maintainer

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Avatar for mrother from gravatar.com mrother

Credits

Author: Milan Rother

GitHub Statistics

Data verified by PyPI on Aug 1, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.9

Tags

sparse solver linear-algebra ldlt lu preconditioner pardiso scipy

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