PyKLU
Python bindings for the SuiteSparse KLU sparse linear solver (CPU only).
PyKLU factors a sparse matrix once and lets you efficiently solve multiple right-hand sides using the same factorization.
Status: 0.2.0 (beta), Linux/macOS/Windows(Experimental), Python ≥ 3.9
Features
- Thin, typed wrapper around SuiteSparse KLU for sparse linear systems.
- Works with
scipy.sparse.csc_matrixas the system matrix. - Reuses the factorization for multiple solves (fast repeated solves).
- Supports:
- 1D right-hand sides (
shape == (n,)) - 2D right-hand sides (
shape == (n, k), multiple RHS) - In-place batched solves for performance-sensitive workloads.
- 1D right-hand sides (
- Ships with an embedded copy of the relevant SuiteSparse components (KLU, AMD, COLAMD, BTF, CHOLMOD, SuiteSparse_config).
Installation
Requirements
- Python: 3.9 or later
- OS: Linux, macOS, Windows (experimental)
- Runtime dependencies:
numpyscipy
- Build tools (when building from source):
- A C/C++ compiler toolchain
- CMake (≥ 3.18 recommended)
- BLAS implementation (e.g.
libblas, OpenBLAS, MKL)
PyKLU builds SuiteSparse from source during installation, and statically links the resulting libraries into the Python extension.
(Recommended) Installation from PyPI
PyKLU can be installed via:
pip install PyKLU
Installation from Source
If no compatible wheel is available through PyPI, it is recommended to install from source. For that, it is recommended to set up a conda environment:
conda create -n pyklu_test python=3.1x # Replace with desired python version
Once inside the environment, install the dependencies.
pip install numpy scipy
Installing PyKLU in this way requires cmake and blas to be installed on the system. If no system-wide installation is present, they must be installed within the environment:
conda install cmake
conda install -c conda-forge libblas
Installing requires cloning the repository with --recursive enabled, so that the SuiteSparse code is also cloned.
git clone --recursive https://github.com/ekatralis/PyKLU.git
If you have already cloned the repository without the SuiteSparse module, you can run:
git submodule update --init --recursive
Then from within the repository folder (cd PyKLU), the package can be installed via pip in editable mode:
pip install -e .
Common issues
During testing, in some environments, there were problems with the FortranCInterface in SuiteSparse. This can be disabled by setting the optional environment variable PYKLU_DISABLE_FORTRAN to 1:
PYKLU_DISABLE_FORTRAN=1 pip install -e .
Installation for development
PyKLU includes optional tests that can verify that the solver is functioning correctly. They can be installed and ran using the following command:
pip install -e .[dev]
pytest
Installation from source on Windows
When installing from source on Windows, PyKLU does not compile SuiteSparse from source. The installation must be done within a conda environment where SuiteSparse is installed through conda-forge. As such, the installation procedure on Windows looks as follows:
Create a conda environment:
conda create -n pyklu_test python=3.1x # Replace with desired python version
Once inside the environment, install the dependencies.
pip install numpy scipy
Install suitesparse and openblas inside the environment:
conda install -c conda-forge suitesparse openblas
Install the Visual Studio Build Tools for C++ from:
https://visualstudio.microsoft.com/visual-cpp-build-tools/
Clone the repository.
git clone --recursive https://github.com/ekatralis/PyKLU.git
Then from within the repository folder (cd PyKLU), the package can be installed via pip in editable mode:
pip install -e .
Short User Guide
❗ PyKLU supports float64 and complex128 datatypes.
- Any complex input (matrix or RHS) is cast to
complex128. - Any real input (matrix or RHS) is cast to
float64.
PyKLU provides detailed type-hints for the various options provided by its methods. It's behavior is designed to mimic scipy.sparse's splu solver, so that it can be used as a drop-in replacement. Compared to splu, KLU can provide improved performance when dealing with very-sparse matrices.
Creating a solver
We create a solver object, which contains the factorization of matrix A. Once the solver has been created, it can be used to solve many RHS (both matrix and vectors).
from PyKLU import Klu
from scipy.sparse import csc_matrix
solver = Klu(csc_matrix(...))
A must be CSC format.
Solving (1D vector or 2D batched)
Once the solver has been created, it can be used to solve either a single vector or multiple vectors which have been batched together as a single matrix.
# 1D vector
b = np.random.rand(A.shape[0])
x = solver.solve(b)
The interface is shared in both cases:
# 2D batched
B = np.random.rand(A.shape[0], 5)
X = solver.solve(B)
The default behavior (copy=True) is to allocate a new array, copy B into it, and then perform the solve in-place on that copy. The function can be used to perform in-place operations instead by setting copy = False. It is worth noting, that if the RHS is incompatible with the format expected (column-major, contiguous and float64), the operation will not be performed in place.
Quickstart
A sample script showcasing the usage of PyKLU can be seen below:
import numpy as np
from scipy.sparse import csc_matrix
from PyKLU import Klu
# Build a simple sparse system A x = b
n = 5
A_dense = np.eye(n)
A_dense[0, 1] = 2.0
A = csc_matrix(A_dense)
b = np.arange(1, n + 1, dtype=np.float64)
# Factor A
solver = Klu(A)
# Solve A x = b
x = solver.solve(b)
print("b:", b)
print("x:", x)
Licensing
© 2015-2025 CERN.
Created by Evangelos Katralis (evangelos.katralis@cern.ch). This library is an extension of the PyKLU library developed by Giovanni Iadarola as a part of the PyCOMPLETE project.
PyKLU is licensed under the GNU Lesser General Public License v2.1 (LGPL-2.1) (see LICENSE).
This project includes the KLU sparse solver from SuiteSparse, distributed under the GNU LGPL v2.1 (or later) with a static-linking exception.
See LICENSE_SuiteSparse.txt for the full SuiteSparse license text.
1 maintainer
Metadata
1 maintainer
Release files for PyKLU 0.2.0
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