Fast and Perfect Sampling of Subgraphs and Polymer Systems

Authors Antonio Blanca, Sarah Cannon, Will Perkins



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Author Details

Antonio Blanca
  • Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA
Sarah Cannon
  • Department of Mathematical Sciences, Claremont McKenna College, CA, USA
Will Perkins
  • Department of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA

Acknowledgements

This work was carried out as part of the AIM SQuaRE workshop "Connections between computational and physical phase transitions." We thank Tyler Helmuth, Alexandre Stauffer, and Izabella Stuhl for many helpful conversations.

Cite AsGet BibTex

Antonio Blanca, Sarah Cannon, and Will Perkins. Fast and Perfect Sampling of Subgraphs and Polymer Systems. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 245, pp. 4:1-4:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2022.4

Abstract

We give an efficient perfect sampling algorithm for weighted, connected induced subgraphs (or graphlets) of rooted, bounded degree graphs. Our algorithm utilizes a vertex-percolation process with a carefully chosen rejection filter and works under a percolation subcriticality condition. We show that this condition is optimal in the sense that the task of (approximately) sampling weighted rooted graphlets becomes impossible in finite expected time for infinite graphs and intractable for finite graphs when the condition does not hold. We apply our sampling algorithm as a subroutine to give near linear-time perfect sampling algorithms for polymer models and weighted non-rooted graphlets in finite graphs, two widely studied yet very different problems. This new perfect sampling algorithm for polymer models gives improved sampling algorithms for spin systems at low temperatures on expander graphs and unbalanced bipartite graphs, among other applications.

Subject Classification

ACM Subject Classification
  • Theory of computation → Randomness, geometry and discrete structures
  • Theory of computation → Design and analysis of algorithms
  • Theory of computation → Graph algorithms analysis
  • Theory of computation → Random walks and Markov chains
Keywords
  • Random Sampling
  • perfect sampling
  • graphlets
  • polymer models
  • spin systems
  • percolation

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