Convex Recoloring of General Graphs: Formulations, Polyhedra, and Computational Experiments
TL;DR
Four mixed-integer linear programming formulations are proposed, including a compact flow-based model and a representatives model, and corresponding solution methods are designed, and the polytopes associated with the linear relaxation of the proposed formulations are compared.
Abstract
A vertex coloring of a graph is convex if the vertices of each color induce a connected subgraph. In the convex recoloring problem (CR), the goal is to find a convex coloring while minimizing the weight of recolored vertices, i.e., vertices assigned a color different from their original one. This problem was originally motivated by the study of phylogenetic trees in bioinformatics and is NP-hard even on paths. Most existing research focuses on trees, with only limited results available for general graphs. We advance the state of the art by developing exact solution methods for CR on general graphs. In particular, we propose four mixed-integer linear programming formulations, including a compact flow-based model and a representatives model, and design corresponding solution methods. We compare the polytopes associated with the linear relaxation of the proposed formulations. Computational experiments on benchmark instances and on new synthetic instances show that a branch-and-cut algorithm based on the representatives formulation performs best overall.