Vertex Cover Kernelization Revisited: Upper and Lower Bounds for a Refined Parameter
Bart M. P. Jansen, Hans L. Bodlaender

TL;DR
This paper explores kernelization bounds for Vertex Cover when parameterized by the size of a minimum feedback vertex set, providing new polynomial kernels and contrasting results for weighted variants.
Contribution
It introduces a cubic kernel for Vertex Cover based on feedback vertex set size and demonstrates the non-existence of polynomial kernels for weighted Vertex Cover under certain complexity assumptions.
Findings
Cubic kernel for Vertex Cover with feedback vertex set parameter
Polynomial-time reduction to smaller equivalent instances
Weighted Vertex Cover lacks polynomial kernel unless NP in coNP/poly
Abstract
An important result in the study of polynomial-time preprocessing shows that there is an algorithm which given an instance (G,k) of Vertex Cover outputs an equivalent instance (G',k') in polynomial time with the guarantee that G' has at most 2k' vertices (and thus O((k')^2) edges) with k' <= k. Using the terminology of parameterized complexity we say that k-Vertex Cover has a kernel with 2k vertices. There is complexity-theoretic evidence that both 2k vertices and Theta(k^2) edges are optimal for the kernel size. In this paper we consider the Vertex Cover problem with a different parameter, the size fvs(G) of a minimum feedback vertex set for G. This refined parameter is structurally smaller than the parameter k associated to the vertex covering number vc(G) since fvs(G) <= vc(G) and the difference can be arbitrarily large. We give a kernel for Vertex Cover with a number of vertices…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
