Clause Learning and New Bounds for Graph Coloring

Clause Learning and New Bounds for Graph Coloring

Emmanuel Hebrard, George Katsirelos

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Best Sister Conferences. Pages 6166-6170. https://doi.org/10.24963/ijcai.2019/856

Graph coloring is a major component of numerous allocation and scheduling problems. We introduce a hybrid CP/SAT approach to graph coloring based on exploring Zykov’s tree: for two non-neighbors, either they take a different color and there might as well be an edge between them, or they take the same color and we might as well merge them. Branching on whether two neighbors get the same color yields a symmetry-free tree with complete graphs as leaves, which correspond to colorings of the original graph. We introduce a new lower bound for this problem based on Mycielskian graphs; a method to produce a clausal explanation of this bound for use in a CDCL algorithm; and a branching heuristic emulating Brelaz on the Zykov tree. The combination of these techniques in a branch- and-bound search outperforms Dsatur and other SAT-based approaches on standard benchmarks both for finding upper bounds and for proving lower bounds.
Keywords:
Constraints and SAT: Constraint Satisfaction
Constraints and SAT: SAT: Solvers and Tools
Constraints and SAT: Global Constraints