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On the hardness of sampling independent sets beyond the tree threshold
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  • Published: 08 January 2008

On the hardness of sampling independent sets beyond the tree threshold

  • Elchanan Mossel1,
  • Dror Weitz2 &
  • Nicholas Wormald3 

Probability Theory and Related Fields volume 143, pages 401–439 (2009)Cite this article

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Abstract

We consider local Markov chain Monte–Carlo algorithms for sampling from the weighted distribution of independent sets with activity λ, where the weight of an independent set I is λ|I|. A recent result has established that Gibbs sampling is rapidly mixing in sampling the distribution for graphs of maximum degree d and λ < λ c (d), where λ c (d) is the critical activity for uniqueness of the Gibbs measure (i.e., for decay of correlations with distance in the weighted distribution over independent sets) on the d-regular infinite tree. We show that for d ≥ 3, λ just above λ c (d) with high probability over d-regular bipartite graphs, any local Markov chain Monte–Carlo algorithm takes exponential time before getting close to the stationary distribution. Our results provide a rigorous justification for “replica” method heuristics. These heuristics were invented in theoretical physics and are used in order to derive predictions on Gibbs measures on random graphs in terms of Gibbs measures on trees. A major theoretical challenge in recent years is to provide rigorous proofs for the correctness of such predictions. Our results establish such rigorous proofs for the case of hard-core model on bipartite graphs. We conjecture that λ c is in fact the exact threshold for this computational problem, i.e., that for λ > λ c it is NP-hard to approximate the above weighted sum over independent sets to within a factor polynomial in the size of the graph.

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Author information

Authors and Affiliations

  1. Statistics and Computer Science, University of California, 367 Evans Hall, Berkeley, CA, 94720-3860, USA

    Elchanan Mossel

  2. DIMACS Center, Rutgers University, Piscataway, NJ, 08854, USA

    Dror Weitz

  3. Combinatorics and Optimization, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Nicholas Wormald

Authors
  1. Elchanan Mossel
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  2. Dror Weitz
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  3. Nicholas Wormald
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Corresponding author

Correspondence to Elchanan Mossel.

Additional information

Elchanan Mossel was supported by an Alfred Sloan fellowship in Mathematics and by NSF grants DMS-0528488, DMS-0504245 and DMS-0548249 (CAREER) and ONR award N0014-07-1-05-06. Nicholas Wormald was supported by the Canada Research Chairs Program and NSERC. Most of this work was done while the authors were visiting Microsoft Research.

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Mossel, E., Weitz, D. & Wormald, N. On the hardness of sampling independent sets beyond the tree threshold. Probab. Theory Relat. Fields 143, 401–439 (2009). https://doi.org/10.1007/s00440-007-0131-9

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  • Received: 18 January 2007

  • Revised: 05 December 2007

  • Published: 08 January 2008

  • Issue Date: March 2009

  • DOI: https://doi.org/10.1007/s00440-007-0131-9

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Mathematics Subject Classification (2000)

  • 60J10
  • 82J20
  • 68Q25
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