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Streaming Kernelization

  • Stefan Fafianie
  • Stefan Kratsch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8635)

Abstract

Kernelization is a formalization of preprocessing for combinatorially hard problems. We modify the standard definition for kernelization, which allows any polynomial-time algorithm for the preprocessing, by requiring instead that the preprocessing runs in a streaming setting and uses \(\mathcal{O}(poly(k)\log|x|)\) bits of memory on instances (x,k). We obtain several results in this new setting, depending on the number of passes over the input that such a streaming kernelization is allowed to make. Edge Dominating Set turns out as an interesting example because it has no single-pass kernelization but two passes over the input suffice to match the bounds of the best standard kernelization.

Keywords

Local Memory Vertex Cover Input Stream Vertex Deletion Problem Kernel 
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 2014

Authors and Affiliations

  • Stefan Fafianie
    • 1
  • Stefan Kratsch
    • 1
  1. 1.TU BerlinGermany

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