Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems. Recently, it has been shown that Large Neighborhood Search (LNS), as a heuristic algorithm, can find high quality solutions to ILPs faster than Branch and Bound. However, how to find the right heuristics to maximize the performance of LNS remains an open problem. In this paper, we propose a novel approach, CL-LNS, that delivers state-of-the-art anytime performance on several ILP benchmarks measured by metrics including the primal gap, the primal integral, survival rates and the best performing rate. Specifically, CL-LNS collects positive and negative solution samples from an expert heuristic that is slow to compute and learns a new one with a contrastive loss. We use graph attention networks and a richer set of features to further improve its performance.

翻译：内部线性程序(ILPs)是建模和解决大量组合优化问题的有力工具。 最近,据显示,大型邻里搜索(LNS)作为一种超光速算法,可以比分科和邦德更快地找到高品质的 ILPs 解决方案。然而,如何找到合适的超光速方法以最大限度地提高LNS的性能仍然是一个尚未解决的问题。在本文中,我们建议采用新的方法(CL-LNS),即随时能够提供以包括原始差距、原始集成率、生存率和最佳性能等计量的数项ILP基准的最新性能。具体地说,CL-LNS从专家的超光速模型中收集正负的解决方案样本,该样本可缓慢地计算和学习具有对比性损失的新解决方案。我们使用图表关注网络和较丰富的特征来进一步改进其性能。