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Brief Announcement: Distributed Derandomization Revisited

Authors Sameep Dahal , Francesco d'Amore , Henrik Lievonen , Timothé Picavet , Jukka Suomela



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Author Details

Sameep Dahal
  • Aalto University, Finland
Francesco d'Amore
  • Aalto University, Finland
Henrik Lievonen
  • Aalto University, Finland
Timothé Picavet
  • Aalto University, Finland
  • ENS de Lyon, France
Jukka Suomela
  • Aalto University, Finland

Acknowledgements

We thank the participants in our reading group at Aalto University for helpful discussions.

Cite AsGet BibTex

Sameep Dahal, Francesco d'Amore, Henrik Lievonen, Timothé Picavet, and Jukka Suomela. Brief Announcement: Distributed Derandomization Revisited. In 37th International Symposium on Distributed Computing (DISC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 281, pp. 40:1-40:5, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.DISC.2023.40

Abstract

One of the cornerstones of the distributed complexity theory is the derandomization result by Chang, Kopelowitz, and Pettie [FOCS 2016]: any randomized LOCAL algorithm that solves a locally checkable labeling problem (LCL) can be derandomized with at most exponential overhead. The original proof assumes that the number of random bits is bounded by some function of the input size. We give a new, simple proof that does not make any such assumptions - it holds even if the randomized algorithm uses infinitely many bits. While at it, we also broaden the scope of the result so that it is directly applicable far beyond LCL problems.

Subject Classification

ACM Subject Classification
  • Theory of computation → Distributed computing models
  • Theory of computation → Pseudorandomness and derandomization
Keywords
  • Distributed algorithm
  • Derandomization
  • LOCAL model

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References

  1. Yi-Jun Chang, Tsvi Kopelowitz, and Seth Pettie. An exponential separation between randomized and deterministic complexity in the local model. SIAM Journal on Computing, 48(1):122-143, 2019. URL: https://doi.org/10.1137/17M1117537.
  2. Nathan Linial. Locality in distributed graph algorithms. SIAM Journal on Computing, 21(1):193-201, 1992. URL: https://doi.org/10.1137/0221015.
  3. Moni Naor and Larry J. Stockmeyer. What can be computed locally? SIAM Journal on Computing, 24(6):1259-1277, 1995. URL: https://doi.org/10.1137/S0097539793254571.
  4. David Peleg. Distributed Computing: A Locality-Sensitive Approach. Society for Industrial and Applied Mathematics, 2000. URL: https://doi.org/10.1137/1.9780898719772.
  5. Jukka Suomela. Landscape of locality (invited talk). In 17th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020), 2020. URL: https://doi.org/10.4230/LIPIcs.SWAT.2020.2.
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