Safe Approximation and Its Relation to Kernelization

  • Jiong Guo
  • Iyad Kanj
  • Stefan Kratsch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7112)

Abstract

We introduce a notion of approximation, called safe approximation, for minimization problems that are subset problems. We first study the relation between the standard notion of approximation and safe approximation, and show that the two notions are different unless some unlikely collapses in complexity theory occur. We then study the relation between safe approximation and kernelization. We demonstrate how the notion of safe approximation can be useful in designing kernelization algorithms for certain fixed-parameter tractable problems. On the other hand, we show that there are problems that have constant-ratio safe approximation algorithms but no polynomial kernels, unless the polynomial hierarchy collapses to the third level.

Keywords

Vertex Cover Polynomial Kernel Reduction Rule Polynomial Time Approximation Scheme Unit Disk Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jiong Guo
    • 1
  • Iyad Kanj
    • 2
  • Stefan Kratsch
    • 3
  1. 1.Universität des SaarlandesSaarbrückenGermany
  2. 2.DePaul UniversityChicagoUSA
  3. 3.Utrecht UniversityUtrechtThe Netherlands

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