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PACE Solver Description: PID^⋆

Authors Max Bannach , Sebastian Berndt , Martin Schuster, Marcel Wienöbst



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Author Details

Max Bannach
  • Institute for Theoretical Computer Science, Universität zu Lübeck, Germany
Sebastian Berndt
  • Institute for IT Security, Universität zu Lübeck, Germany
Martin Schuster
  • Institute for Epidemiology, Kiel University, Germany
Marcel Wienöbst
  • Institute for Theoretical Computer Science, Universität zu Lübeck, Germany

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Max Bannach, Sebastian Berndt, Martin Schuster, and Marcel Wienöbst. PACE Solver Description: PID^⋆. In 15th International Symposium on Parameterized and Exact Computation (IPEC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 180, pp. 28:1-28:4, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.IPEC.2020.28

Abstract

This document provides a short overview of our treedepth solver PID^{⋆} in the version that we submitted to the exact track of the PACE challenge 2020. The solver relies on the positive-instance driven dynamic programming (PID) paradigm that was discovered in the light of earlier iterations of the PACE in the context of treewidth. It was recently shown that PID can be used to solve a general class of vertex pursuit-evasion games - which include the game theoretic characterization of treedepth. Our solver PID^{⋆} is build on top of this characterization.

Subject Classification

ACM Subject Classification
  • Theory of computation → Parameterized complexity and exact algorithms
Keywords
  • treedepth
  • positive-instance driven

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References

  1. Max Bannach and Sebastian Berndt. Positive-instance driven dynamic programming for graph searching. In Proceedings of the 16th International Symposium on Algorithms and Data Structures, 2019. URL: https://doi.org/10.1007/978-3-030-24766-9_4.
  2. Robert Ganian, Neha Lodha, Sebastian Ordyniak, and Stefan Szeider. SAT-Encodings for Treecut Width and Treedepth. In Proceedings of the 21th Workshop on Algorithm Engineering and Experiments, 2019. URL: https://doi.org/10.1137/1.9781611975499.10.
  3. Archontia C. Giannopoulou, Paul Hunter, and Dimitrios M. Thilikos. Lifo-search: A min-max theorem and a searching game for cycle-rank and tree-depth. Discret. Appl. Math., 160(15):2089-2097, 2012. URL: https://doi.org/10.1016/j.dam.2012.03.015.
  4. Yasuaki Kobayashi and Hisao Tamaki. Treedepth Parameterized by Vertex Cover Number. In Proceedings of the 11th International Symposium on Parameterized and Exact Computation, 2016. URL: https://doi.org/10.4230/LIPIcs.IPEC.2016.18.
  5. Hisao Tamaki. Positive-instance driven dynamic programming for treewidth. J. Comb. Optim., 37(4):1283-1311, 2019. Google Scholar
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