Combinatorial optimization finds an optimal solution within a discrete set of variables and constraints. The field has seen tremendous progress both in research and industry. With the success of deep learning in the past decade, a recent trend in combinatorial optimization has been to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning (ML) models. In this paper, we investigate two essential aspects of machine learning algorithms for combinatorial optimization: temporal characteristics and attention. We argue that for the task of variable selection in the branch-and-bound (B&B) algorithm, incorporating the temporal information as well as the bipartite graph attention improves the solver's performance. We support our claims with intuitions and numerical results over several standard datasets used in the literature and competitions. Code is available at: https://developer.huaweicloud.com/develop/aigallery/notebook/detail?id=047c6cf2-8463-40d7-b92f-7b2ca998e935

翻译：组合优化在离散的变量和约束集合中寻找最优解。该领域在研究和工业应用中均取得了巨大进展。随着深度学习在过去十年的成功，组合优化的最新趋势是通过用机器学习模型替代关键启发式组件来改进最先进的组合优化求解器。本文研究了机器学习算法在组合优化中的两个关键方面：时间特性与注意力机制。我们认为，在分支定界算法中执行变量选择任务时，融合时间信息与二分图注意力能够提升求解器的性能。我们通过直觉论证以及在文献和竞赛中常用的多个标准数据集上的数值结果支持上述观点。代码获取地址：https://developer.huaweicloud.com/develop/aigallery/notebook/detail?id=047c6cf2-8463-40d7-b92f-7b2ca998e935