Cut Selection

Cut selection in integer linear programming (ILP) and mixed-integer linear programming (MILP) focuses on efficiently choosing a subset of generated cutting planes to accelerate problem solving. Current research emphasizes machine learning approaches, employing hierarchical sequence models, reinforcement learning, and imitation learning to learn optimal cut selection policies, often surpassing traditional heuristic methods. These advancements aim to improve the performance of MILP solvers, impacting various fields that rely on these optimization techniques, such as operations research, logistics, and AI model verification. The ultimate goal is to develop more efficient and robust algorithms for solving complex optimization problems.