Computing Bottom SCCs Symbolically Using Transition Guided Reduction
| Autor: | Nikola Beneš, David Šafránek, Luboš Brim, Samuel Pastva |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Strongly connected component Theoretical computer science Dynamical systems theory Markov chain Computer science Transition (fiction) Reduction (complexity) 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Boolean network Attractor Symbolic algorithm 030217 neurology & neurosurgery |
| Zdroj: | Computer Aided Verification ISBN: 9783030816841 CAV (1) |
| DOI: | 10.1007/978-3-030-81685-8_24 |
| Popis: | Detection of bottom strongly connected components (BSCC) in state-transition graphs is an important problem with many applications, such as detecting recurrent states in Markov chains or attractors in dynamical systems. However, these graphs’ size is often entirely out of reach for algorithms using explicit state-space exploration, necessitating alternative approaches such as the symbolic one.Symbolic methods for BSCC detection often show impressive performance, but can sometimes take a long time to converge in large graphs. In this paper, we provide a symbolic state-space reduction method for labelled transition systems, called interleaved transition guided reduction (ITGR), which aims to alleviate current problems of BSCC detection by efficiently identifying large portions of the non-BSCC states.We evaluate the suggested heuristic on an extensive collection of 125 real-world biologically motivated systems. We show that ITGR can easily handle all these models while being either the only method to finish, or providing at least an order-of-magnitude speedup over existing state-of-the-art methods. We then use a set of synthetic benchmarks to demonstrate that the technique also consistently scales to graphs with more than $$2^{1000}$$ 2 1000 vertices, which was not possible using previous methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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