License: Creative Commons Attribution 3.0 Unported license (CC-BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICALP.2018.68
URN: urn:nbn:de:0030-drops-90727
URL: https://drops.dagstuhl.de/opus/volltexte/2018/9072/
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Guo, Heng ; Jerrum, Mark

A Polynomial-Time Approximation Algorithm for All-Terminal Network Reliability

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LIPIcs-ICALP-2018-68.pdf (0.5 MB)


Abstract

We give a fully polynomial-time randomized approximation scheme (FPRAS) for the all-terminal network reliability problem, which is to determine the probability that, in a undirected graph, assuming each edge fails independently, the remaining graph is still connected. Our main contribution is to confirm a conjecture by Gorodezky and Pak (Random Struct. Algorithms, 2014), that the expected running time of the "cluster-popping" algorithm in bi-directed graphs is bounded by a polynomial in the size of the input.

BibTeX - Entry

@InProceedings{guo_et_al:LIPIcs:2018:9072,
  author =	{Heng Guo and Mark Jerrum},
  title =	{{A Polynomial-Time Approximation Algorithm for All-Terminal Network Reliability}},
  booktitle =	{45th International Colloquium on Automata, Languages, and  Programming (ICALP 2018)},
  pages =	{68:1--68:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-076-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{107},
  editor =	{Ioannis Chatzigiannakis and Christos Kaklamanis and D{\'a}niel Marx and Donald Sannella},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9072},
  URN =		{urn:nbn:de:0030-drops-90727},
  doi =		{10.4230/LIPIcs.ICALP.2018.68},
  annote =	{Keywords: Approximate counting, Network Reliability, Sampling, Markov chains}
}

Keywords: Approximate counting, Network Reliability, Sampling, Markov chains
Collection: 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)
Issue Date: 2018
Date of publication: 04.07.2018


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