detkit 0.12.3

Matrix determinant toolkit

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detKit is a Python package for computing determinant functions of matrices.

Install

Install with pip

pip install detkit

Install with conda

conda install s-ameli::detkit

Docker Image

docker pull sameli/detkit

Supported Platforms

Successful installation and tests performed on the following operating systems, architectures, and Python and PyPy versions:

Platform	Arch	Python Version	PyPy Version 1	Continuous Integration
3.9	3.10	3.11	3.12	3.13	3.8	3.9	3.10
Linux	X86-64	✔	✔	✔	✔	✔	✔	✔	✔
	AARCH-64	✔	✔	✔	✔	✔	✔	✔	✔
macOS	X86-64	✔	✔	✔	✔	✔	✔	✔	✔
	ARM-64	✔	✔	✔	✔	✔	✔	✔	✔
Windows	X86-64	✔	✔	✔	✔	✔	✔	✔	✔
	ARM-64 2	✔	✔	✔	✔	✔	✔	✔	✔

Python wheels for detkit for all supported platforms and versions in the above are available through PyPI and Anaconda Cloud. If you need detkit on other platforms, architectures, and Python or PyPy versions, raise an issue on GitHub and we build its Python Wheel for you.

^{1. Wheels for PyPy are exclusively available for installation through pip and cannot be installed using conda.}

^{2. Wheels for Windows on ARM-64 architecture are exclusively available for installation through pip and cannot be installed using conda.}

Documentation

See documentation of the package.

Benchmark Test

Read about the benchmark test of detkit in practical applications.

How to Contribute

We welcome contributions via GitHub’s pull request. If you do not feel comfortable modifying the code, we also welcome feature requests and bug reports as GitHub issues.

How to Cite

If you publish work that uses detkit, please consider citing the manuscripts available here.

[1]

Ameli, S., and Shadden. S. C. (2023). A Singular Woodbury and Pseudo-Determinant Matrix Identities and Application to Gaussian Process Regression. Applied Mathematics and Computation 452, 128032. doi

@article{amc-2023,
    title = {A singular Woodbury and pseudo-determinant matrix identities and application to Gaussian process regression},
    journal = {Applied Mathematics and Computation},
    volume = {452},
    pages = {128032},
    year = {2023},
    issn = {0096-3003},
    doi = {https://doi.org/10.1016/j.amc.2023.128032},
    author = {Siavash Ameli and Shawn C. Shadden},
}

[2]

Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney (2025). Determinant Estimation under Memory Constraints and Neural Scaling Laws. Forty-second International Conference on Machine Learning. doi

@inproceedings{
    ameli2025determinant,
    title={Determinant Estimation under Memory Constraints and Neural Scaling Laws},
    author={Siavash Ameli and Chris van der Heide and Liam Hodgkinson and Fred Roosta and Michael W. Mahoney},
    booktitle={Forty-second International Conference on Machine Learning},
    year={2025},
    url={https://openreview.net/forum?id=nkV9PPp8R8}
}

License

This project uses a BSD 3-clause license, in hopes that it will be accessible to most projects. If you require a different license, please raise an issue and we will consider a dual license.