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A Near-Linear-Time Algorithm for Weak Bisimilarity on Markov Chains

Authors David N. Jansen , Jan Friso Groote , Ferry Timmers, Pengfei Yang

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Author Details

David N. Jansen
  • State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
Jan Friso Groote
  • Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands
Ferry Timmers
  • Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands
Pengfei Yang
  • State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
  • University of Chinese Academy of Sciences, Beijing, China

Funding

  • Jansen, David N.: This research is partly done during a visit to Eindhoven University of Technology, The Netherlands. This author is supported by the National Natural Science Foundation of China, Grants No. 61761136011 and 61532019.
  • Groote, Jan Friso: This research is partly done during a visit to the State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China.

Cite As Get BibTex

David N. Jansen, Jan Friso Groote, Ferry Timmers, and Pengfei Yang. A Near-Linear-Time Algorithm for Weak Bisimilarity on Markov Chains. In 31st International Conference on Concurrency Theory (CONCUR 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 171, pp. 8:1-8:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.CONCUR.2020.8

Abstract

This article improves the time bound for calculating the weak/branching bisimulation minimisation quotient on state-labelled discrete-time Markov chains from O(m n) to an expected-time O(m log⁴ n), where n is the number of states and m the number of transitions. For these results we assume that the set of state labels AP is small (|AP| ∈ O(m/n log⁴ n)). It follows the ideas of Groote et al. (ACM ToCL 2017) in combination with an efficient algorithm to handle decremental strongly connected components (Bernstein et al., STOC 2019).

Subject Classification

ACM Subject Classification
  • Theory of computation → Random walks and Markov chains
  • Theory of computation → Formal languages and automata theory
  • Theory of computation → Probabilistic computation
  • Software and its engineering → Formal software verification
Keywords
  • Behavioural Equivalence
  • weak Bisimulation
  • Markov Chain

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References

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