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Parameterized Distributed Algorithms

Authors Ran Ben-Basat , Ken-ichi Kawarabayashi , Gregory Schwartzman

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Author Details

Ran Ben-Basat
  • Harvard University, Cambridge, MA, United States
Ken-ichi Kawarabayashi
  • National Institute of Informatics, Tokyo, Japan
Gregory Schwartzman
  • National Institute of Informatics, Tokyo, Japan

Funding

  • Ben-Basat, Ran: Supported by the Zuckerman Institute, the Technion Hiroshi Fujiwara Cyber Security Research center, and the Israel Cyber Directorate.
  • Kawarabayashi, Ken-ichi: Supported by JSPS Kakenhi Grant Number JP18H05291.
  • Schwartzman, Gregory: Supported by JSPS Kakenhi Grant Number JP19K20216 and JP18H05291.

Acknowledgements

The authors thank the anonymous reviewers for their helpful remarks. We also thank Keren Censor-Hillel, Guy Even, Seri Khoury, and Ariel Kulik for helpful discussion and comments.

Cite AsGet BibTex

Ran Ben-Basat, Ken-ichi Kawarabayashi, and Gregory Schwartzman. Parameterized Distributed Algorithms. In 33rd International Symposium on Distributed Computing (DISC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 146, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.DISC.2019.6

Abstract

In this work, we initiate a thorough study of graph optimization problems parameterized by the output size in the distributed setting. In such a problem, an algorithm decides whether a solution of size bounded by k exists and if so, it finds one. We study fundamental problems, including Minimum Vertex Cover (MVC), Maximum Independent Set (MaxIS), Maximum Matching (MaxM), and many others, in both the LOCAL and CONGEST distributed computation models. We present lower bounds for the round complexity of solving parameterized problems in both models, together with optimal and near-optimal upper bounds. Our results extend beyond the scope of parameterized problems. We show that any LOCAL (1+epsilon)-approximation algorithm for the above problems must take Omega(epsilon^{-1}) rounds. Joined with the (epsilon^{-1}log n)^{O(1)} rounds algorithm of [Ghaffari et al., 2017] and the Omega (sqrt{(log n)/(log log n)}) lower bound of [Fabian Kuhn et al., 2016], the lower bounds match the upper bound up to polynomial factors in both parameters. We also show that our parameterized approach reduces the runtime of exact and approximate CONGEST algorithms for MVC and MaxM if the optimal solution is small, without knowing its size beforehand. Finally, we propose the first o(n^2) rounds CONGEST algorithms that approximate MVC within a factor strictly smaller than 2.

Subject Classification

ACM Subject Classification
  • Theory of computation → Distributed algorithms
Keywords
  • Distributed Algorithms
  • Approximation Algorithms
  • Parameterized Algorithms

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References

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