We survey recent work on machine learning (ML) techniques for selecting cutting planes (or cuts) in mixed-integer linear programming (MILP). Despite the availability of various classes of cuts, the task of choosing a set of cuts to add to the linear programming (LP) relaxation at a given node of the branch-and-bound (B&B) tree has defied both formal and heuristic solutions to date. ML offers a promising approach for improving the cut selection process by using data to identify promising cuts that accelerate the solution of MILP instances. This paper presents an overview of the topic, highlighting recent advances in the literature, common approaches to data collection, evaluation, and ML model architectures. We analyze the empirical results in the literature in an attempt to quantify the progress that has been made and conclude by suggesting avenues for future research.

翻译：摘要： 本文综述了近年来利用机器学习（ML）技术选取混合整数线性规划（MILP）中割平面（或割）的相关工作。尽管已有多种类型的割可用，但在分支定界（B&B）树给定节点处选择一组割添加到线性规划（LP）松弛中的任务，迄今仍未得到形式化或启发式方法的解决。机器学习提供了一种有前景的方法，通过利用数据识别能加速MILP实例求解的有效割，从而改进割平面选取过程。本文概述了这一主题，重点介绍了文献中的最新进展、数据收集与评估的常见方法以及机器学习模型架构。我们分析了文献中的实证结果，试图量化已取得的进展，并在结论部分提出了未来研究方向。