https://github.com/abess-team/skscope-fast-sparsity-constrained-optimization-in-python
Reproducible materials for "skscope: Fast Sparsity-Constrained Optimization in Python"
https://github.com/abess-team/skscope-fast-sparsity-constrained-optimization-in-python
python sparsity-optimization
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Reproducible materials for "skscope: Fast Sparsity-Constrained Optimization in Python"
- Host: GitHub
- URL: https://github.com/abess-team/skscope-fast-sparsity-constrained-optimization-in-python
- Owner: abess-team
- Created: 2025-02-04T18:42:33.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-25T08:56:58.000Z (about 1 year ago)
- Last Synced: 2025-05-25T09:37:26.991Z (about 1 year ago)
- Topics: python, sparsity-optimization
- Language: C++
- Homepage: https://www.jmlr.org/papers/v25/23-1574.html
- Size: 1.13 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Reproducible materials
This repository contains scripts to reproduce the numerical results analysis described in "[skscope: Fast Sparsity-Constrained Optimization in Python](https://www.jmlr.org/papers/volume25/23-1574/23-1574.pdf)".
A step-by-step instruction for reproducing is provided here.
## Instruction
We compare algorithms in ``skscope`` and other well-known methods under several models. Here are the steps for reproducing Table-A3, similar to others.
* First, manually install two python libraries: ``skscope_experiment`` (provided the simulation data production method required for the experiment), ``parallel_experiment_util`` (convenient for running experiments with multiple processes). Note that these libraries are not available in Pypi, users should use `pip install ./skscope_experiment` and `pip install ./parallel_experiment_util`.
* Next, run experiments like `python ./figure_A3/A3_skscope.py`, `python ./figure_A3/A3_gurobi.py`. Note that, gurobi need a license to run.
* Finally, statistic the result by `./figure_A3/plot.ipynb`.
## Citations
Please cite the following publications if you make use of the material here.
- Zezhi Wang, Junxian Zhu, Xueqin Wang, Jin Zhu, Huiyang Pen, Peng Chen, Anran Wang, Xiaoke Zhang (2024). skscope: Fast Sparsity-Constrained Optimization in Python. Journal of Machine Learning Research, 25(290), 1−9.
The corresponding BibteX entries:
```
@article{JMLR:v25:23-1574,
author = {Zezhi Wang and Junxian Zhu and Xueqin Wang and Jin Zhu and Huiyang Pen and Peng Chen and Anran Wang and Xiaoke Zhang},
title = {skscope: Fast Sparsity-Constrained Optimization in Python},
journal = {Journal of Machine Learning Research},
year = {2024},
volume = {25},
number = {290},
pages = {1--9},
url = {http://jmlr.org/papers/v25/23-1574.html}
}
```
## Contact
Please direct questions and comments to the [issues page](https://github.com/abess-team/skscope-Fast-Sparsity-Constrained-Optimization-in-Python/issues).