Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates

arXiv:2307.08816v2 Announce Type: replace
Abstract: Discrete optimization belongs to the set of $\mathcal{NP}$-hard problems, spanning fields such as mixed-integer programming and combinatorial optimization. A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms, which reach optimal solutions by iteratively adding inequalities known as \textit{cuts} to refine a feasible set. Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability. In this work, we propose a …

abstract algorithms arxiv cs.ai cs.lg current fields math.oc mixed optimization plane programming refine reinforcement reinforcement learning set solutions standard type via