Preprocessing for Treewidth: A Combinatorial Analysis through Kernelization

The notion of treewidth plays an important role in theoretical and practical studies of graph problems. It has been recognized that, especially in practical environments, when computing the treewidth of a graph it is invaluable to first apply an array of preprocessing rules that simplify and shrink it. This work seeks to prove rigorous performance guarantees for such preprocessing rules, both known and new ones, by studying them in the framework of kernelization from parameterized complexity. It is known that the NP-complete problem of determining whether a given graph G has treewidth at most k admits no polynomial-time preprocessing algorithm that reduces any input instance to size polynomial in k, unless NP is in coNP/poly and the polynomial hierarchy collapses to its third level. In this paper we therefore consider structural graph measures larger than treewidth, and determine whether efficient preprocessing can shrink the instance size to a polynomial in such a parameter value. We prove that given an instance (G,k) of treewidth we can efficiently reduce its size to O(fvs(G)^4) vertices, where fvs(G) is the size of a minimum feedback vertex set in G. We can also prove a size reduction to O(vc(G)^3) vertices, where vc(G) is the size of a minimum vertex cover. Phrased in the language of parameterized complexity, we show that Treewidth has a polynomial kernel when parameterized by the size of a given feedback vertex set, and also by the size of a vertex cover. In contrast we show that Treewidth parameterized by the vertex-deletion distance to a single clique, and Weighted Treewidth parameterized by the size of a vertex cover, do not admit polynomial kernelizations unless NP is in coNP/poly.