Group splicing numerical experiments

This repository contains scripts to run the synthetic datasets and real-world dataset analysis described in A Splicing Approach to Best Subset of Groups Selection.

Codes

• Synthetic_dataset_analysis/Synthetic_dataset_analysis.R : R script used to run the synthetic datasets analysis.
• Real-world_dataset_analysis/Real-world_dataset_analysis.R : R script used to run the real-world dataset analysis.
• Real-world_dataset_analysis/trim32.rda : Dataset used in the real-world dataset analysis.
• gomp/gomp.R : R interface of gomp.cpp.
• gomp/gomp.cpp : C++ implementation of group orthogonal matching pursuit (GOMP).

Softwares

• Group Lasso : R package grpreg (3.4.0).
• Group MCP : R package grpreg (3.4.0).
• GOMP : Implementation in R language with Rcpp modules.
• Group Splicing : R package abess (0.4.0).

Citations

Please cite the following publications if you make use of the material here.

• Yanhang Zhang, Junxian Zhu, Jin Zhu, and Xueqin Wang. A splicing approach to best subset of groups selection. INFORMS Journal on Computing, 35(1):104–119, 2023. doi: 10.1287/ijoc.2022.1241. URL https://doi.org/10.1287/ijoc.2022.1241.

• Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin and Junxian Zhu (2022). abess: A Fast Best-Subset Selection Library in Python and R. Journal of Machine Learning Research, 23(202), 1-7.

The corresponding BibteX entries:

@article{doi:10.1287/ijoc.2022.1241,
author = {Zhang, Yanhang and Zhu, Junxian and Zhu, Jin and Wang, Xueqin},
title = {A Splicing Approach to Best Subset of Groups Selection},
journal = {INFORMS Journal on Computing},
volume = {35},
number = {1},
pages = {104-119},
year = {2023},
doi = {10.1287/ijoc.2022.1241},
URL = {https://doi.org/10.1287/ijoc.2022.1241},
eprint = { https://doi.org/10.1287/ijoc.2022.1241}
}

and

@article{JMLR:v23:21-1060,
  author  = {Jin Zhu and Xueqin Wang and Liyuan Hu and Junhao Huang and Kangkang Jiang and Yanhang Zhang and Shiyun Lin and Junxian Zhu},
  title   = {abess: A Fast Best-Subset Selection Library in Python and R},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {202},
  pages   = {1--7},
  url     = {http://jmlr.org/papers/v23/21-1060.html}
}

Contact

Please direct questions and comments to the issues page.