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Rapid Mixing of Glauber Dynamics of Gibbs Ensembles via Aggregate Path Coupling and Large Deviations Methods

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Abstract

In this paper, we present a novel extension to the classical path coupling method to statistical mechanical models which we refer to as aggregate path coupling. In conjunction with large deviations estimates, we use this aggregate path coupling method to prove rapid mixing of Glauber dynamics for a large class of statistical mechanical models, including models that exhibit discontinuous phase transitions which have traditionally been more difficult to analyze rigorously. The parameter region for rapid mixing for the generalized Curie–Weiss–Potts model is derived as a new application of the aggregate path coupling method.

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References

  1. Bhatnagar, N., Randall, D.: Torpid mixing of simulated tempering on the Potts model. In: Proceedings of the 15th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 478–487 (2004)

  2. Bubley, R., Dyer, M.E.: Path coupling: a technique for proving rapid mixing in Markov chains. In: Proceedings of the 38th IEEE Symposium on Foundations of Computer Science (FOCS), pp. 223–231 (1997)

  3. Costeniuc, M., Ellis, R.S., Touchette, H.: Complete analysis of phase transitions and ensemble equivalence for the Curie-Weiss-Potts model. J. Math. Phys. 46, 063301 (2005)

    Article  MathSciNet  ADS  Google Scholar 

  4. Cuff, P., Ding, J., Louidor, O., Lubetzy, E., Peres, Y., Sly, A.: Glauber dynamics for the mean-field Potts model. J. Stat. Phys. 149(3), 432–477 (2012)

    Article  MATH  MathSciNet  ADS  Google Scholar 

  5. Ding, J., Lubetzky, E., Peres, Y.: The mixing time evolution of Glauber dynamics for the mean-field Ising model. Commun. Math. Phys. 289(2), 725–764 (2009)

    Article  MATH  MathSciNet  ADS  Google Scholar 

  6. Ding, J., Lubetzky, E., Peres, Y.: Censored Glauber dynamics for the mean-field Ising model. J. Stat. Phys. 137(1), 161–207 (2009)

    MathSciNet  Google Scholar 

  7. Ellis, R.S., Haven, K., Turkington, B.: Large deviation principles and complete equivalence and nonequivalence results for pure and mixed ensembles. J. Stat. Phys. 101(5–6), 999–1064 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  8. Ebbers, M., Knöpfel, H., Löwe, M., Vermet, F.: Mixing times for the swapping algorithm on the Blume-Emery-Griffiths model. Random Struct. Algorithms 45, 38–77 (2012). doi:10.1002/rsa.20461

    Article  Google Scholar 

  9. Galanis, A., Stefankovic, D., Vigoda, E.: Swendsen-Wang algorithm on the mean-field Potts model. Preprint arXiv:1502.06593v1

  10. Jahnel, B., Külske, C., Rudelli, E., Wegener, J.: Gibbsian and non-Gibbsian properties of the generalized mean-field fuzzy Potts-model. Markov Proc. Relat. Fields 20, 601–632 (2014)

    Google Scholar 

  11. Kovchegov, Y., Otto, P.T., Titus, M.: Mixing times for the mean-field Blume-Capel model via aggregate path coupling. J. Stat. Phys. 144(5), 1009–1027 (2011)

    Article  MATH  MathSciNet  ADS  Google Scholar 

  12. Levin, D.A., Luczak, M.: Glauber dynamics of the mean-field Ising model: cut-off, critical power law, and metastability. Probab. Theory Relat. Fields 146(1), 223–265 (2010)

    Article  MathSciNet  Google Scholar 

  13. Levin, D., Peres, Y., Wilmer, E.: Markov Chains and Mixing Times. American Mathematical Society, Providence (2009)

    MATH  Google Scholar 

  14. Lindvall, T.: Lectures on the Coupling Method. Wiley, New York (1992). Dover paperback edition, Reprint (2002)

    MATH  Google Scholar 

  15. Luczak, M.J.: Concentration of measure and mixing times of Markov chainss. Discrete Mathematics and Theoretical Computer Science. In: Proceedings of the 5th Colloquium on Mathematics and Computer Science, pp. 95–120 (2008)

  16. Wu, F.Y.: The Potts model. Rev. Mod. Phys. 54, 235–268 (1982)

    Article  ADS  Google Scholar 

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Acknowledgments

This work was partially supported by a grant from the Simons Foundation (#284262 to Yevgeniy Kovchegov).

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Correspondence to Peter T. Otto.

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Kovchegov, Y., Otto, P.T. Rapid Mixing of Glauber Dynamics of Gibbs Ensembles via Aggregate Path Coupling and Large Deviations Methods. J Stat Phys 161, 553–576 (2015). https://doi.org/10.1007/s10955-015-1345-3

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  • DOI: https://doi.org/10.1007/s10955-015-1345-3

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