rslab (Python bindings)
NumPy/SciPy bindings for RSLAB, a pure-Rust sparse direct solver and preconditioner: complex/real symmetric LDL^T (Bunch-Kaufman), unsymmetric LU, and a KLU-style path for circuit-shaped matrices. 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) |
parallel |
None |
per-block parallel factor/refactor over the BTF blocks; None = auto gate (>=4 blocks, >=8000 nnz, no dominant block), True/False force on/off; bit-identical result in every mode |
Supported dtypes: float64, float32, complex128, complex64.
API
Everything ships in the flat rslab namespace; full parameter documentation
lives in the docstrings (help(rslab.klu) etc.).
Functions
| function | meaning |
|---|---|
spsolve(A, b, **kw) |
one-shot factor-and-solve; detects symmetry and picks the LDL^T or LU path |
ldlt(A, **kw) -> Ldlt |
factor a real/complex symmetric matrix (Bunch-Kaufman LDL^T) |
lu(A, **kw) -> Lu |
factor a general unsymmetric matrix (supernodal multifrontal LU) |
klu(A, **kw) -> Klu |
factor a circuit-shaped matrix (BTF + per-block Gilbert-Peierls LU) |
install_diagnose() |
one-time machine calibration; caches the measured thread-speedup curve |
Factor handles - factor once, then:
| method / attribute | Ldlt |
Lu |
Klu |
meaning |
|---|---|---|---|---|
solve(b, refine=0) |
yes | yes | yes | solve one RHS, optional iterative-refinement steps against the original A |
solve_many(B) |
yes | yes | yes | solve n x nrhs RHS in one batched pass |
solve_transpose(b) |
- | - | yes | solve A^T y = b on the same factors (plain transpose, not conjugate) |
refactor(data) |
- | - | yes | numeric-only re-factorization for new values on the same pattern (no symbolic work, no pivot search) |
gmres(b, tol=1e-8, maxit=400, restart=None, x0=None, recycle=None) |
yes | yes | yes | GMRES with this factor as preconditioner |
gmres_block(B, tol=1e-8, maxit=400, restart=None, x0=None) |
yes | yes | yes | block GMRES for multiple RHS |
recycle(k) |
yes | yes | yes | a Recycle workspace holding up to k deflation vectors across gmres calls |
n, factor_nnz, n_perturbed, dtype |
yes | yes | yes | dimension, stored factor entries, perturbed pivots (always 0 for Klu), NumPy dtype name |
inertia |
yes | - | - | (n_pos, n_neg, n_zero) eigenvalue counts from LDL^T |
n_blocks |
- | - | yes | number of BTF diagonal blocks |
Recycle - deflation-subspace carrier for sweeps: attributes k,
active, dtype; clear() resets it.
License
MIT.
1 maintainer
Release files for rslab 0.28.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| rslab-0.28.1.tar.gz | 1.2 MB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| rslab-0.28.1-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| rslab-0.28.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| rslab-0.28.1-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| rslab-0.28.1-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
| rslab-0.28.1-cp39-abi3-macosx_10_12_x86_64.whl | CPython 3.9 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 11.4 MB
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