2026-08-10T23:23:56Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18863 2024-03-06T11:02:46Z ddc:004 open_access

Perfect Sampling for Hard Spheres from Strong Spatial Mixing Anand, Konrad Göbel, Andreas Pappik, Marcus Perkins, Will perfect sampling hard-sphere model Gibbs point processes 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Konrad Anand and Andreas Göbel and Marcus Pappik and Will Perkins 2023 Is Part Of LIPIcs, Volume 275, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.APPROX/RANDOM.2023.38 urn:nbn:de:0030-drops-188638 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2023.38 eng https://creativecommons.org/licenses/by/4.0/legalcode