License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2023.38
URN: urn:nbn:de:0030-drops-188638
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18863/
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Anand, Konrad ; Göbel, Andreas ; Pappik, Marcus ; Perkins, Will

Perfect Sampling for Hard Spheres from Strong Spatial Mixing

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LIPIcs-APPROX38.pdf (0.8 MB)

Abstract

We provide a perfect sampling algorithm for the hard-sphere model on subsets of R^d with expected running time linear in the volume under the assumption of strong spatial mixing. A large number of perfect and approximate sampling algorithms have been devised to sample from the hard-sphere model, and our perfect sampling algorithm is efficient for a range of parameters for which only efficient approximate samplers were previously known and is faster than these known approximate approaches. Our methods also extend to the more general setting of Gibbs point processes interacting via finite-range, repulsive potentials.

BibTeX - Entry

@InProceedings{anand_et_al:LIPIcs.APPROX/RANDOM.2023.38,
  author =	{Anand, Konrad and G\"{o}bel, Andreas and Pappik, Marcus and Perkins, Will},
  title =	{{Perfect Sampling for Hard Spheres from Strong Spatial Mixing}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)},
  pages =	{38:1--38:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-296-9},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{275},
  editor =	{Megow, Nicole and Smith, Adam},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18863},
  URN =		{urn:nbn:de:0030-drops-188638},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2023.38},
  annote =	{Keywords: perfect sampling, hard-sphere model, Gibbs point processes}
}

Keywords: perfect sampling, hard-sphere model, Gibbs point processes
Collection: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)
Issue Date: 2023
Date of publication: 04.09.2023

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