| Name | pypardiso JSON |
| Version |
0.4.6
JSON |
| download |
| home_page | None |
| Summary | Python interface to the Intel MKL Pardiso library to solve large sparse linear systems of equations |
| upload_time | 2024-03-28 21:32:54 |
| maintainer | None |
| docs_url | None |
| author | Adrian Haas |
| requires_python | >=3.8 |
| license | Copyright (c) 2016, Adrian Haas and ETH Zürich All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of ETH Zürich nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
pardiso
sparse solver
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/haasad/PyPardisoProject/actions/workflows/tests.yaml)
# PyPardiso
PyPardiso is a python package to solve large sparse linear systems of equations with the [Intel oneAPI Math Kernel Library PARDISO solver](https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-fortran/top/sparse-solver-routines/onemkl-pardiso-parallel-direct-sparse-solver-iface.html), a shared-memory multiprocessing parallel direct sparse solver.
PyPardiso provides the same functionality as SciPy's [scipy.sparse.linalg.spsolve](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.spsolve.html#scipy.sparse.linalg.spsolve) for solving the sparse linear system `Ax=b`. However in many cases it is significantly faster than SciPy's built-in single-threaded SuperLU solver.
PyPardiso is not a python interface to the PARDISO Solver from the [PARDISO 7.2 Solver Project](https://www.pardiso-project.org/) and it also doesn't currently support complex numbers. Check out [JuliaSparse/Pardiso.jl](https://github.com/JuliaSparse/Pardiso.jl/) for these more advanced use cases.
## Installation
PyPardiso runs on Linux, Windows and MacOS. It can be installed with __conda__ or __pip__. It is recommended to install PyPardiso using a virtual environment.
conda-forge | PyPI
:---:|:---:
[](https://anaconda.org/conda-forge/pypardiso) | [](https://pypi.org/project/pypardiso/)
`conda install -c conda-forge pypardiso` | `pip install pypardiso`
## Basic usage
How to solve the sparse linear system `Ax=b` for `x`, where `A` is a square, sparse matrix in CSR (or CSC) format and `b` is a vector (or matrix):
```python
In [1]: import pypardiso
In [2]: import numpy as np
In [3]: import scipy.sparse as sp
In [4]: A = sp.rand(10, 10, density=0.5, format='csr')
In [5]: A
Out[5]:
<10x10 sparse matrix of type '<class 'numpy.float64'>'
with 50 stored elements in Compressed Sparse Row format>
In [6]: b = np.random.rand(10)
In [7]: x = pypardiso.spsolve(A, b)
In [8]: x
Out[8]:
array([ 0.02918389, 0.59629935, 0.33407289, -0.48788966, 3.44508841,
0.52565687, -0.48420646, 0.22136413, -0.95464127, 0.58297397])
```
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"description": "[](https://github.com/haasad/PyPardisoProject/actions/workflows/tests.yaml)\n# PyPardiso\n\nPyPardiso is a python package to solve large sparse linear systems of equations with the [Intel oneAPI Math Kernel Library PARDISO solver](https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-fortran/top/sparse-solver-routines/onemkl-pardiso-parallel-direct-sparse-solver-iface.html), a shared-memory multiprocessing parallel direct sparse solver.\n\nPyPardiso provides the same functionality as SciPy's [scipy.sparse.linalg.spsolve](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.spsolve.html#scipy.sparse.linalg.spsolve) for solving the sparse linear system `Ax=b`. However in many cases it is significantly faster than SciPy's built-in single-threaded SuperLU solver.\n\nPyPardiso is not a python interface to the PARDISO Solver from the [PARDISO 7.2 Solver Project](https://www.pardiso-project.org/) and it also doesn't currently support complex numbers. Check out [JuliaSparse/Pardiso.jl](https://github.com/JuliaSparse/Pardiso.jl/) for these more advanced use cases.\n\n## Installation\n\nPyPardiso runs on Linux, Windows and MacOS. It can be installed with __conda__ or __pip__. It is recommended to install PyPardiso using a virtual environment.\n\nconda-forge | PyPI\n:---:|:---:\n[](https://anaconda.org/conda-forge/pypardiso) | [](https://pypi.org/project/pypardiso/)\n`conda install -c conda-forge pypardiso` | `pip install pypardiso`\n\n\n## Basic usage\n\nHow to solve the sparse linear system `Ax=b` for `x`, where `A` is a square, sparse matrix in CSR (or CSC) format and `b` is a vector (or matrix):\n```python\nIn [1]: import pypardiso\n\nIn [2]: import numpy as np\n\nIn [3]: import scipy.sparse as sp\n\nIn [4]: A = sp.rand(10, 10, density=0.5, format='csr')\n\nIn [5]: A\nOut[5]:\n<10x10 sparse matrix of type '<class 'numpy.float64'>'\n\twith 50 stored elements in Compressed Sparse Row format>\n\nIn [6]: b = np.random.rand(10)\n\nIn [7]: x = pypardiso.spsolve(A, b)\n\nIn [8]: x\nOut[8]:\narray([ 0.02918389, 0.59629935, 0.33407289, -0.48788966, 3.44508841,\n 0.52565687, -0.48420646, 0.22136413, -0.95464127, 0.58297397])\n```\n",
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