论文标题
通过向前看来学习切割：通过模仿学习切割飞机选择
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
论文作者
论文摘要
切割平面对于解决混合企业线性问题（MILP）至关重要，因为它们促进了最佳解决方案值的界限。为了选择切割，现代求解器依靠手动设计的启发式方法来评估切割的潜在有效性。我们表明，一项贪婪的选择规则明确地寻求选择的剪裁，从而产生最佳的界限可以为切割选择提供强大的决策 - 但太昂贵了，无法在实践中部署。作为回应，我们提出了一种新的神经体系结构（神经曲），以模仿LookAhead专家。我们的模型优于标准基准，用于在几个合成的MILP基准上进行切割选择。使用B＆C求解器进行神经网络验证的实验进一步验证了我们的方法，并在这种情况下展示了学习方法的潜力。
Cutting planes are essential for solving mixed-integer linear problems (MILPs), because they facilitate bound improvements on the optimal solution value. For selecting cuts, modern solvers rely on manually designed heuristics that are tuned to gauge the potential effectiveness of cuts. We show that a greedy selection rule explicitly looking ahead to select cuts that yield the best bound improvement delivers strong decisions for cut selection - but is too expensive to be deployed in practice. In response, we propose a new neural architecture (NeuralCut) for imitation learning on the lookahead expert. Our model outperforms standard baselines for cut selection on several synthetic MILP benchmarks. Experiments with a B&C solver for neural network verification further validate our approach, and exhibit the potential of learning methods in this setting.
