Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 147 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 27 tok/s Pro
GPT-5 High 30 tok/s Pro
GPT-4o 96 tok/s Pro
Kimi K2 188 tok/s Pro
GPT OSS 120B 398 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

A simpler O(m log n) algorithm for branching bisimilarity on labelled transition systems (1909.10824v2)

Published 24 Sep 2019 in cs.LO

Abstract: Branching bisimilarity is a behavioural equivalence relation on labelled transition systems that takes internal actions into account. It has the traditional advantage that algorithms for branching bisimilarity are more efficient than all algorithms for other weak behavioural equivalences, especially weak bisimilarity. With $m$ the number of transitions and $n$ the number of states, the classic $O(m n)$ algorithm was recently replaced by an $O(m (\log \lvert \mathit{Act}\rvert + \log n))$ algorithm, which is unfortunately rather complex. This paper combines its ideas with the ideas from Valmari. This results in a simpler algorithm with complexity $O(m \log n)$. Benchmarks show that this new algorithm is also faster and often far more memory efficient than its predecessors. This makes it the best option for branching bisimulation minimisation and preprocessing for weak bisimulation of LTSs.

Citations (18)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.