2023/11/07 by Yongzheng Dai, Chen Chen, Dai, Yongzheng +1
Computer Science · #90C10 #Advanced Graph Neural Networks #FOS: Mathematics #Graph Theory and Algorithms #Optimization and Control (math.OC) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2311.03706
openalex publication_date 2023/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A conflict graph represents logical relations between binary variables, and effective use of the graph can significantly accelerate branch-and-cut solvers for mixed-integer programming (MIP). In this paper we develop efficient parallel conflict graph management: conflict detection; maximal clique generation; clique extension; and clique merging. We leverage parallel computing in order to intensify computational effort on the conflict graph, thereby generating a much larger pool of cutting planes than what can be practically achieved in serial. Computational experiments demonstrate that the expanded pool of cuts enabled by parallel computing lead to substantial reductions in total MIP solve time, especially for more challenging cases.