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.

本论文调查了机器学习在选择混合整数线性规划中剪切平面的技术上的应用，通过使用数据来确定有希望的剪辑以加速 MILP 实例的解决方案，并分析文献中的实证结果以量化已经取得的进展，并建议未来的研究方向。