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Entropic Uniform Sampling of Linear Extensions in Series-Parallel Posets

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Computer Science – Theory and Applications (CSR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10304))

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Abstract

In this paper, we introduce a uniform random sampler for linear extensions of Series-Parallel posets. The algorithms we present ensure an essential property of random generation algorithms: entropy. They are in a sense optimal in their consumption of random bits.

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Notes

  1. 1.

    The combinatorial embedding allows to distinguish the two successors of a node: the left one and the right one.

References

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Author information

Authors and Affiliations

  1. Laboratoire d’Informatique de Paris-Nord, CNRS UMR 7030 - Institut Galilée - Université Paris-Nord, 99, avenue Jean-Baptiste Clément, 93430, Villetaneuse, France

    Olivier Bodini

  2. Sorbonne Universités, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606, 4 place Jussieu, 75005, Paris, France

    Matthieu Dien, Antoine Genitrini & Frédéric Peschanski

Authors
  1. Olivier Bodini
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  2. Matthieu Dien
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  3. Antoine Genitrini
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  4. Frédéric Peschanski
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Corresponding author

Correspondence to Matthieu Dien .

Editor information

Editors and Affiliations

  1. University of Bordeaux , Talence, France

    Pascal Weil

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Bodini, O., Dien, M., Genitrini, A., Peschanski, F. (2017). Entropic Uniform Sampling of Linear Extensions in Series-Parallel Posets. In: Weil, P. (eds) Computer Science – Theory and Applications. CSR 2017. Lecture Notes in Computer Science(), vol 10304. Springer, Cham. https://doi.org/10.1007/978-3-319-58747-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-58747-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58746-2

  • Online ISBN: 978-3-319-58747-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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