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QBF as an Alternative to Courcelle’s Theorem

  • Michael Lampis
  • Stefan Mengel
  • Valia Mitsou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10929)

Abstract

We propose reductions to quantified Boolean formulas (QBF) as a new approach to showing fixed-parameter linear algorithms for problems parameterized by treewidth. We demonstrate the feasibility of this approach by giving new algorithms for several well-known problems from artificial intelligence that are in general complete for the second level of the polynomial hierarchy. By reduction from QBF we show that all resulting algorithms are essentially optimal in their dependence on the treewidth. Most of the problems that we consider were already known to be fixed-parameter linear by using Courcelle’s Theorem or dynamic programming, but we argue that our approach has clear advantages over these techniques: on the one hand, in contrast to Courcelle’s Theorem, we get concrete and tight guarantees for the runtime dependence on the treewidth. On the other hand, we avoid tedious dynamic programming and, after showing some normalization results for CNF-formulas, our upper bounds often boil down to a few lines.

Notes

Acknowledgments

Most of the research in this paper was performed during a stay of the first and third authors at CRIL that was financed by the project PEPS INS2I 2017 CODA. The second author is thankful for many valuable discussions with members of CRIL, in particular Jean-Marie Lagniez, Emmanuel Lonca and Pierre Marquis, on the topic of this article.

Moreover, the authors would like to thank the anonymous reviewers whose numerous helpful remarks allowed to improve the presentation of the paper.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Université Paris-Dauphine, PSL Research University, CNRS, UMR 7243ParisFrance
  2. 2.CNRS, CRIL UMR 8188ParisFrance
  3. 3.Université Paris-Diderot, IRIF, CNRS, UMR 8243ParisFrance

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