Polynomial kernels for weighted problems

Michael Etscheid, Stefan Kratsch, Matthias Mnich, Heiko Röglin

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Kernelization is a formalization of efficient preprocessing for NP-hard problems using the framework of parameterized complexity. It has been asked many times whether there are deterministic polynomial kernelizations for SUBSET SUM and KNAPSACK. We answer both questions affirmatively by using an algorithm for compressing numbers due to Frank and Tardos (Combinatorica 1987). We further illustrate its applicability by giving polynomial kernels for weighted versions of several well-studied parameterized problems. Furthermore, when parameterized by the different item sizes we obtain a polynomial kernelization for SUBSET SUM and an exponential kernelization for KNAPSACK. Finally, we obtain kernelization results for polynomial integer programs. (C) 2016 Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalJournal of Computer and System Sciences
Volume84
DOIs
Publication statusPublished - Mar 2017

Keywords

  • Kernelization for weighted parameterized problems
  • FPT
  • Knapsack
  • Subset Sum
  • Integer Linear Programming with bounded variables
  • APPROXIMATION
  • FIXED NUMBER

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