Polynomial Kernelizations for MIN F^+Pi_1 and MAX NP

TL;DR

This paper demonstrates that decision problems within the classes MIN F^+Pi_1 and MAX NP, which are constant-factor approximable, admit polynomial kernels, extending known fixed-parameter tractability results and contrasting with problems lacking polynomial kernels.

Contribution

It proves that all problems in MIN F^+Pi_1 and MAX NP have polynomial problem kernels, broadening the understanding of kernelization in these classes.

Findings

Problems in MAX SNP admit kernels with a linear base set.

Extends fixed-parameter tractability results for MAX SNP and MIN F^+Pi_1.

Contrasts with problems that do not admit polynomial kernels.

Abstract

It has been observed in many places that constant-factor approximable problems often admit polynomial or even linear problem kernels for their decision versions, e.g., Vertex Cover, Feedback Vertex Set, and Triangle Packing. While there exist examples like Bin Packing, which does not admit any kernel unless P = NP, there apparently is a strong relation between these two polynomial-time techniques. We add to this picture by showing that the natural decision versions of all problems in two prominent classes of constant-factor approximable problems, namely MIN F^+\Pi_1 and MAX NP, admit polynomial problem kernels. Problems in MAX SNP, a subclass of MAX NP, are shown to admit kernels with a linear base set, e.g., the set of vertices of a graph. This extends results of Cai and Chen (JCSS 1997), stating that the standard parameterizations of problems in MAX SNP and MIN F^+\Pi_1 are fixed-parameter tractable, and complements recent research on problems that do not admit polynomial kernelizations (Bodlaender et al. JCSS 2009).