https://github.com/adeyemiadeoye/pyscsopt
A Python library for large-scale convex composite optimization.
https://github.com/adeyemiadeoye/pyscsopt
convex-optimization l-bfgs lbfgs nonsmooth-optimization optimization python quasi-newton
Last synced: 5 months ago
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A Python library for large-scale convex composite optimization.
- Host: GitHub
- URL: https://github.com/adeyemiadeoye/pyscsopt
- Owner: adeyemiadeoye
- License: other
- Created: 2025-04-18T08:10:44.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-12-05T14:32:21.000Z (7 months ago)
- Last Synced: 2025-12-08T01:46:34.919Z (7 months ago)
- Topics: convex-optimization, l-bfgs, lbfgs, nonsmooth-optimization, optimization, python, quasi-newton
- Language: Python
- Homepage:
- Size: 47.9 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pySCSOpt: Self-Concordant Smooth Optimization in Python
This package is a Python port of most parts of the Julia package [SelfConcordantSmoothOptimization.jl](https://github.com/adeyemiadeoye/SelfConcordantSmoothOptimization.jl). It includes:
- `ProxLQNSCORE` a limited-memory version of ProxQNSCORE of the Julia package
- `ProxGGNSCORE`
- `ProxNSCORE`
- Smoothing and regularization (utility) functions
## Installation
Install with pip:
```sh
pip install pyscsopt
```
## Usage
See the [`examples/`](https://github.com/adeyemiadeoye/pySCSOpt/tree/main/examples) directory for a usage example.
For more information on how to set up problems (especially choosing regularizers), see Julia's [SelfConcordantSmoothOptimization.jl](https://github.com/adeyemiadeoye/SelfConcordantSmoothOptimization.jl).
## Tests
Run tests with:
```sh
pytest pyscsopt/test/
```
## Citation
If you use this package for research, please cite:
```bibtex
@article{adeoye2023self,
title={Self-concordant Smoothing for Large-Scale Convex Composite Optimization},
author={Adeoye, Adeyemi D and Bemporad, Alberto},
journal={arXiv preprint arXiv:2309.01781},
year={2024}
}
```