TLDR
NuCS is a Python library for solving Constraint Satisfaction and Optimization Problems. Because it is 100% written in Python, NuCS is easy to install and allows to model complex problems in a few lines of code. The NuCS solver is also very fast because it is powered by Numpy and Numba.
Installation
pip install nucs
Documentation
Check out NuCS documentation.
See CHANGELOG.md for what changed between releases, including the 15.0.0 and 16.0.0 migration notes.
With NuCS, in a few seconds you can ...
Find all 14200 solutions to the 12-queens problem
NUMBA_CACHE_DIR=.numba/cache python -m nucs.examples.queens -n 12
Compute the 92 solutions to the BIBD(8,14,7,4,3) problem
NUMBA_CACHE_DIR=.numba/cache python -m nucs.examples.bibd -v 8 -b 14 -r 7 -k 4 -l 3
Demonstrate that the optimal 10-marks Golomb ruler length is 55
NUMBA_CACHE_DIR=.numba/cache python -m nucs.examples.golomb -n 10
1 maintainer
Metadata
1 maintainer
Release files for NuCS 16.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nucs-16.1.0.tar.gz | 175.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nucs-16.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 481.0 kB
Release files / nucs-16.1.0.tar.gz
| Download URL | nucs-16.1.0.tar.gz |
|---|---|
| Size | 175.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / nucs-16.1.0-py3-none-any.whl
| Download URL | nucs-16.1.0-py3-none-any.whl |
|---|---|
| Size | 305.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
498ab59df69b2ab68f16e09c072c86076386189588083236ccca58c230fdefad
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 22, 2026.
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