A Complete Anytime Algorithm for Treewidth
This work addresses the treewidth computation problem for applications in graph theory and AI, such as Graph Coloring and Bayesian Networks, representing an incremental improvement over prior methods.
The paper tackles the problem of computing treewidth in undirected graphs by introducing QuickBB, a branch and bound algorithm that uses novel pruning and propagation techniques, resulting in consistently better CPU time than existing complete algorithms like QuickTree and improved anytime performance for generating upper bounds.
In this paper, we present a Branch and Bound algorithm called QuickBB for computing the treewidth of an undirected graph. This algorithm performs a search in the space of perfect elimination ordering of vertices of the graph. The algorithm uses novel pruning and propagation techniques which are derived from the theory of graph minors and graph isomorphism. We present a new algorithm called minor-min-width for computing a lower bound on treewidth that is used within the branch and bound algorithm and which improves over earlier available lower bounds. Empirical evaluation of QuickBB on randomly generated graphs and benchmarks in Graph Coloring and Bayesian Networks shows that it is consistently better than complete algorithms like QuickTree [Shoikhet and Geiger, 1997] in terms of cpu time. QuickBB also has good anytime performance, being able to generate a better upper bound on treewidth of some graphs whose optimal treewidth could not be computed up to now.