abess: A Fast Best Subset Selection Library in Python and R
Sun Yat-Sen University（中山大学）；University of Science and Technology of China（中国科学技术大学）；Renmin University of China（中国人民大学）；Peking University（北京大学）；National University of Singapore（新加坡国立大学）
我们介绍一个名为abess的新库，它实现了最佳子集选择的统一框架，用于解决多种机器学习问题，例如线性回归、分类和主成分分析。特别是，abess在线性模型下可在多项式时间内以高概率获得可证明的最优解。我们高效的实现使abess能够以与现有竞争变量（模型）选择工具箱一样快甚至快20倍的速度获得最佳子集选择问题的解。此外，它支持常见变体，如最佳组子集选择和带$\ell_2$正则化的最佳子集选择。该库的核心使用C++编程。为便于使用，设计了一个Python库以方便与scikit-learn集成，并可从Python库索引安装。此外，一个用户友好的R库可在Comprehensive R Archive Network上获取。源代码可在以下地址获得：https://github.com/abess-team/abess。
英文摘要：
We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, the abess certifiably gets the optimal solution within polynomial times with high probability under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 20x faster than existing competing variable (model) selection toolboxes. Furthermore, it supports common variants like best group subset selection and $\ell_2$ regularized best-subset selection. The core of the library is programmed in C++. For ease of use, a Python library is designed for conveniently integrating with scikit-learn, and it can be installed from the Python library Index. In addition, a user-friendly R library is available at the Comprehensive R Archive Network. The source code is available at: https://github.com/abess-team/abess.
skscope: Fast Sparsity-Constrained Optimization in Python
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；London School of Economics and Political Science（伦敦政治经济学院）
UPMC Université Paris 6, Sorbonne Universités（巴黎第六大学（UPMC），索邦大学集团）；École Polytechnique（巴黎综合理工学院）；Athens University of Economics and Business（雅典经济与商业大学）
