Polynomial kernelizations for MIN F+π1 and MAX NP

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 polynomialtime 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+π1 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 (J. Comput. Syst. Sci. 54(3): 465-474, 1997), stating that the standard parameterizations of problems in MAX SNP and MIN F+π1 are fixed-parameter tractable, and complements recent research on problems that do not admit polynomial kernelizations (Bodlaender et al. in J. Comput. Syst. Sci. 75(8): 423-434, 2009). © Springer Science+Business Media, LLC 2011.