rslab (Python bindings)
NumPy/SciPy bindings for RSLAB, a
pure-Rust sparse direct solver: symmetric and complex-symmetric LDL^T
(Bunch-Kaufman), unsymmetric LU, and a KLU path for circuit-shaped matrices.
All numeric work happens in Rust. Supported dtypes: float64, float32,
complex128, complex64.
pip install rslab
Usage
import numpy as np
import scipy.sparse as sp
import rslab
x = rslab.spsolve(A, b) # one-shot, on the automatic path
f = rslab.ldlt(A) # symmetric or complex-symmetric
x = f.solve(b)
X = f.solve_many(B) # n x nrhs
print(f.factor_nnz, f.inertia, f.diagnostics()["summary"])
f = rslab.lu(A_general) # unsymmetric
y = f.solve_transpose(b) # A.T @ y = b, on every handle
Circuit-shaped matrices (MNA / SPICE class) take the KLU path, with a numeric-only refactor for fixed-pattern sweeps and its factors exported:
f = rslab.klu(A)
x = f.solve(b)
f.refactor(A_next.data) # same pattern, new values, no pivot search
y = f.solve_transpose(b) # A.T @ y = b (conjugate b and y for A^H)
L, U, F = f.L, f.U, f.F # (R A)[perm_r][:, perm_c] = L @ U + F
The automatic path picks among the three by the work the analysis predicts: KLU for sparse work, LDL^T for symmetric values or LU for dense work.
f = rslab.auto(A)
print(f.backend) # 'klu', 'ldlt' or 'lu'
f.refactor(A_next.data) # in place, on the path the values take
Symbolic reuse
The analysis depends on the pattern only; pay it once per sweep:
sym = rslab.analyze(A) # AutoSymbolic; path='ldlt', 'lu' or 'klu' for one path
plan = sym.memory_plan(A.dtype.name, threads=8) # heap before any numeric work
assert plan["peak_bytes"] < available_bytes
for omega in frequencies:
f = sym.factor(K + 1j * omega * C) # the matrix or its data array
x = f.solve(b)
Preconditioners and Krylov solvers
Static pivoting never fails; refinement or a Krylov method recovers the
accuracy. gmres, gmres_block, cocg and cocr take any factor handle as
preconditioner:
f = rslab.ldlt(A, preconditioner=1e-4)
x = f.solve(b, refine=20)
M = rslab.lu(A, factor_dtype="complex64") # complex128 A, half the factor memory
x, converged, iters, res, stop = rslab.gmres(A, b, M, tol=1e-10)
x = M.solve(b, refine=5) # mixed-precision refinement, full accuracy
Settings
Factor knobs are keyword arguments of auto, ldlt, lu, spsolve and analyze,
or a rslab.Settings object (rslab.KluSettings for klu). Every tuning
constant of the solver is one (nd_fm_passes, race_candidates,
solve_block, ...), with the tuned value as default;
rslab.Settings().to_dict() lists them all. Common overrides:
f = rslab.lu(A, ordering="metis", threads=8, pivot_threshold=0.5)
s = rslab.Settings(preconditioner=1e-6, drop_tol=1e-3)
Settings a path does not read are listed under
diagnostics()["warnings"]. Every keyword, handle and method is documented
in the generated reference docs/api.md (help(rslab.lu)
shows the same text).
Logging
rslab.set_log_level("info") # or the RLA_LOG environment variable
log = logging.getLogger("rslab")
rslab.set_log_sink(lambda level, msg: log.log(logging.getLevelName(level.upper()), msg))
License
MIT.
1 maintainer
Metadata
1 maintainer
Release files for rslab 1.6.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| rslab-1.6.0.tar.gz | 593.6 kB | With provenance | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| rslab-1.6.0-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| rslab-1.6.0-cp39-abi3-manylinux_2_28_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.28+ x86-64 | Details |
| rslab-1.6.0-cp39-abi3-manylinux_2_28_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.28+ ARM64 | Details |
| rslab-1.6.0-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
| rslab-1.6.0-cp39-abi3-macosx_10_12_x86_64.whl | CPython 3.9 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 15.6 MB
Release files / rslab-1.6.0.tar.gz
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