Kernelization of Maximum Minimal Vertex Cover

Júlio Araújo, Marin Bougeret, Victor A. Campos, Ignasi Sau

In the Maximum Minimal Vertex Cover (MMVC) problem, we are given a graph $G$ and a positive integer $k$, and the objective is to decide whether $G$ contains a minimal vertex cover of size at least $k$. This problem has been considered in several articles in the last years. We focus on its kernelization, which had been almost unexplored so far. We prove that MMVC does not admit polynomial kernels parameterized by the size of a minimum vertex cover, even on bipartite graphs, unless ${\sf NP} \subseteq {\sf coNP} / {\sf poly}$. Motivated by a question of Boria et al. [Discret. Appl. Math. 2015] about the existence of subquadratic kernels for MMVC parameterized by $k$, we rule out their existence unless $P=NP$, if we restrict the kernelization algorithms to apply only a type of natural reduction rules that we call "large optimal preserving rules". In particular, these rules contain the typical reduction rules to obtain linear kernels for Vertex Cover. On the positive side, we provide subquadratic kernels on $H$-free graphs for several graphs $H$, such as the bull, the paw, or the complete graphs, by making use of the Erd\H{o}s-Hajnal property in order to find an appropriate decomposition.

Knowledge Graph



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