https://github.com/basnijholt/pfapack

Efficient numerical computation of the Pfaffian for dense and banded skew-symmetric matrices
https://github.com/basnijholt/pfapack

linear-algebra numpy python scipy

Last synced: 3 months ago
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Efficient numerical computation of the Pfaffian for dense and banded skew-symmetric matrices

• Host: GitHub
• URL: https://github.com/basnijholt/pfapack
• Owner: basnijholt
• License: other
• Created: 2020-03-02T18:47:45.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-11-27T21:27:00.000Z (about 1 year ago)
• Last Synced: 2024-09-30T22:01:22.500Z (4 months ago)
• Topics: linear-algebra, numpy, python, scipy
• Language: Python
• Homepage: https://arxiv.org/abs/1102.3440
• Size: 52.7 KB
• Stars: 15
• Watchers: 4
• Forks: 6
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# `pfapack`: Efficient numerical computation of the Pfaffian for dense and banded skew-symmetric matrices

Code and algorithms are taken from [arXiv:1102.3440](https://arxiv.org/abs/1102.3440) which is authored by [Michael Wimmer](https://michaelwimmer.org/).

[![license](https://img.shields.io/github/license/basnijholt/pfapack)](https://github.com/basnijholt/pfapack/blob/master/LICENSE)

[![tests](https://github.com/basnijholt/pfapack/workflows/tests/badge.svg)](https://github.com/basnijholt/pfapack/actions?query=workflow%3Atests)

[![codecov](https://img.shields.io/codecov/c/github/basnijholt/pfapack)](https://codecov.io/gh/basnijholt/pfapack)

[![docs](https://img.shields.io/readthedocs/pfapack)](https://pfapack.readthedocs.io)

[![version](https://img.shields.io/pypi/v/pfapack)](https://pypi.org/project/pfapack/)

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pfapack)](https://pypi.org/project/pfapack/)

### Install

Recommended way (because it includes faster C/FORTRAN bindings)

```bash

conda install -c conda-forge pfapack

```

Alternatively use

```bash

pip install pfapack

```

## Usage

```python

from pfapack import pfaffian as pf

import numpy.matlib

A = numpy.matlib.rand(100, 100)

A = A - A.T

pfa1 = pf.pfaffian(A)

pfa2 = pf.pfaffian(A, method="H")

pfa3 = pf.pfaffian_schur(A)

print(pfa1, pfa2, pfa3)

```

If installed with `conda`, C/FORTRAN code is included with Python bindings, use it like:

```python

from pfapack.ctypes import pfaffian as cpf

pfa1 = cpf(A)

pfa2 = cpf(A, method="H")

print(pfa1, pfa2)

```

## Citing

If you have used `pfapack` in your research, please cite it using the following `bib` entry:

```

@article{wimmer2012algorithm,

  title={Efficient numerical computation of the pfaffian for dense and banded skew-symmetric matrices},

  author={Michael Wimmer},

  journal={ACM Transactions on Mathematical Software (TOMS)},

  volume={38},

  number={4},

  pages={1--17},

  year={2012},

  publisher={ACM New York, NY, USA}

}

```

## License

MIT License

## Contributions

- Bas Nijholt

- [Michael Wimmer (author of the algorithms)](https://arxiv.org/abs/1102.3440)