2021/06/09 by Runzhong Wang, Wang, Runzhong, Zhigang Hua +16 · 16 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Combinatorial optimization #Combinatorics (math.CO) #Computer science #Constraint Satisfaction and Optimization #FOS: Computer and information sciences #FOS: Mathematics #Graph #Graph Theory and Algorithms #Heuristic #Heuristics #Machine Learning (cs.LG) #Mathematical optimization #Mathematics #Optimization problem #Scheduling (production processes) #Theoretical computer science #Vehicle Routing Optimization Methods #cs.LG #math.CO
paper · pdf · doi:10.48550/arxiv.2106.04927
published in arXiv (Cornell University) (Cornell University) · NeurIPS 2021. Code at https://github.com/Thinklab-SJTU/PPO-BiHyb
openalex publication_date 2021/06/09 · openalex created_date 2021/06/22 · arxiv created 2021/10/25 · arxiv updated 2021/10/26 · openalex updated_date 2026/08/08
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature. Traditionally such problems are approximately solved with heuristic algorithms which are usually fast but may sacrifice the solution quality. Currently, machine learning for combinatorial optimization (MLCO) has become a trending research topic, but most existing MLCO methods treat CO as a single-level optimization by directly learning the end-to-end solutions, which are hard to scale up and mostly limited by the capacity of ML models given the high complexity of CO. In this paper, we propose a hybrid approach to combine the best of the two worlds, in which a bi-level framework is developed with an upper-level learning method to optimize the graph (e.g. add, delete or modify edges in a graph), fused with a lower-level heuristic algorithm solving on the optimized graph. Such a bi-level approach simplifies the learning on the original hard CO and can effectively mitigate the demand for model capacity. The experiments and results on several popular CO problems like Directed Acyclic Graph scheduling, Graph Edit Distance and Hamiltonian Cycle Problem show its effectiveness over manually designed heuristics and single-level learning methods.