We present BackboneLearn: an open-source software package and framework for scaling mixed-integer optimization (MIO) problems with indicator variables to high-dimensional problems. This optimization paradigm can naturally be used to formulate fundamental problems in interpretable supervised learning (e.g., sparse regression and decision trees), in unsupervised learning (e.g., clustering), and beyond; BackboneLearn solves the aforementioned problems faster than exact methods and with higher accuracy than commonly used heuristics. The package is built in Python and is user-friendly and easily extensible: users can directly implement a backbone algorithm for their MIO problem at hand. The source code of BackboneLearn is available on GitHub (link: https://github.com/chziakas/backbone_learn).

翻译：我们推出BackboneLearn：一个开源软件包与框架，旨在将含指示变量的混合整数优化（MIO）问题扩展到高维场景。该优化范式可自然用于构建可解释监督学习（如稀疏回归与决策树）、无监督学习（如聚类）及其他领域的核心问题；BackboneLearn比精确方法求解上述问题更快，且比常用启发式方法精度更高。本软件包基于Python构建，用户友好且易于扩展：用户可直接为其MIO问题实现骨干算法。BackboneLearn的源代码已发布于GitHub（链接：https://github.com/chziakas/backbone_learn）。