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Pseudodeterministic Algorithms for Minimum Cut Problems

Authors Aryan Agarwala , Nithin Varma

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LIPIcs.ITCS.2026.4.pdf
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Subject Classification

ACM Subject Classification
  • Mathematics of computing → Graph algorithms
  • Theory of computation → Streaming, sublinear and near linear time algorithms
  • Theory of computation → Pseudorandomness and derandomization
Keywords
  • Minimum Cut
  • Pseudodeterministic Algorithms

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Abstract

In this paper we present efficient pseudodeterministic algorithms for both the global minimum cut and minimum s-t cut problems. The running time of our algorithm for the global minimum cut problem is asymptotically better than the fastest sequential deterministic global minimum cut algorithm (Henzinger, Li, Rao, Wang; SODA 2024).
Furthermore, we implement our algorithm in streaming, PRAM, and cut-query models, where no efficient deterministic global minimum cut algorithms are known.

Cite As Get BibTex

Aryan Agarwala and Nithin Varma. Pseudodeterministic Algorithms for Minimum Cut Problems. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.ITCS.2026.4

Author Details

Aryan Agarwala
  • Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
Nithin Varma
  • University of Cologne, Germany

Acknowledgements

The authors thank Danupon Nanongkai for extensive discussions.

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