Reproducible materials

This repository contains scripts to reproduce the numerical results analysis described in "skscope: Fast Sparsity-Constrained Optimization in Python". 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

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