Exponentially slow mixing in the mean-field Swendsen-Wang dynamics

被引:3
|
作者
Gheissari, Reza [1 ]
Lubetzky, Eyal [1 ]
Peres, Yuval [2 ]
机构
[1] NYU, Courant Inst, 251 Mercer St, New York, NY 10012 USA
[2] Microsoft Res, 1 Microsoft Way, Redmond, WA 98052 USA
关键词
Potts model; Swendsen-Wang; Mixing time; FK model; Random graphs; RANDOM-CLUSTER MODEL;
D O I
10.1214/18-AIHP955
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Swendsen-Wang dynamics for the Potts model was proposed in the late 1980's as an alternative to single-site heat-bath dynamics, in which global updates allow this MCMC sampler to switch between metastable states and ideally mix faster. Gore and Jerrum (J. Stat. Phys. 97 (1999) 67-86) found that this dynamics may in fact exhibit slow mixing: they showed that, for the Potts model with q >= 3 colors on the complete graph on n vertices at the critical point beta(c) (q), Swendsen-Wang dynamics has t(mix) >= exp(c root n). Galanis et al. (In Proc. of the 19th International Workshop on Randomization and Computation (RANDOM 2015) (2015) 815-828) showed that t(mix) >= exp(cn(1/3)) throughout the critical window (beta(s) , beta(s)) around beta(c) , and Blanca and Sinclair (In Proc. of the 19th International Workshop on Randomization and Computation (RANDOM 2015) (2015) 528-543) established that t(mix) >= exp(c root n) in the critical window for the corresponding mean-field FK model, which implied the same bound for Swendsen-Wang via known comparison estimates. In both cases, an upper bound of t(mix )<= exp(c'n) was known. Here we show that the mixing time is truly exponential in n: namely, t(mix) >= exp(cn) for Swendsen-Wang dynamics when q >= 3 and beta is an element of (beta(s) , beta(s)), and the same bound holds for the related MCMC samplers for the mean-field FK model when q > 2.
引用
收藏
页码:68 / 86
页数:19
相关论文
共 50 条
  • [11] ENTROPY DECAY IN THE SWENDSEN-WANG DYNAMICS ON Zd
    Blanca, Antonio
    Caputo, Pietro
    Parisi, Daniel
    Sinclair, Alistair
    Vigoda, Eric
    ANNALS OF APPLIED PROBABILITY, 2022, 32 (02): : 1018 - 1057
  • [12] Entropy Decay in the Swendsen-Wang Dynamics on Zd
    Blanca, Antonio
    Caputo, Pietro
    Parisi, Daniel
    Sinclair, Alistair
    Vigoda, Eric
    STOC '21: PROCEEDINGS OF THE 53RD ANNUAL ACM SIGACT SYMPOSIUM ON THEORY OF COMPUTING, 2021, : 1551 - 1564
  • [13] Comparison of Swendsen-Wang and heat-bath dynamics
    Ullrich, Mario
    RANDOM STRUCTURES & ALGORITHMS, 2013, 42 (04) : 520 - 535
  • [14] A VECTORIZED ALGORITHM FOR CLUSTER FORMATION IN THE SWENDSEN-WANG DYNAMICS
    MINO, H
    COMPUTER PHYSICS COMMUNICATIONS, 1991, 66 (01) : 25 - 30
  • [15] Swendsen-Wang dynamics for the ferromagnetic Ising model with external fields
    Feng, Weiming
    Guo, Heng
    Wang, Jiaheng
    INFORMATION AND COMPUTATION, 2023, 294
  • [16] Mixing properties of the Swendsen-Wang process on the complete graph and narrow grids
    Cooper, C
    Dyer, ME
    Frieze, AM
    Rue, R
    JOURNAL OF MATHEMATICAL PHYSICS, 2000, 41 (03) : 1499 - 1527
  • [17] Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models
    Park, Sejun
    Jang, Yunhun
    Galanis, Andreas
    Shin, Jinwoo
    Stefankovic, Daniel
    Vigoda, Eric
    ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 54, 2017, 54 : 440 - 449
  • [18] Swendsen-Wang dynamics for general graphs in the tree uniqueness region
    Blanca, Antonio
    Chen, Zongchen
    Vigoda, Eric
    RANDOM STRUCTURES & ALGORITHMS, 2020, 56 (02) : 373 - 400
  • [19] SWENDSEN-WANG IS FASTER THAN SINGLE-BOND DYNAMICS
    Ullrich, Mario
    SIAM JOURNAL ON DISCRETE MATHEMATICS, 2014, 28 (01) : 37 - 48
  • [20] Tight bounds for mixing of the Swendsen-Wang algorithm at the Potts transition point
    Borgs, Christian
    Chayes, Jennifer T.
    Tetali, Prasad
    PROBABILITY THEORY AND RELATED FIELDS, 2012, 152 (3-4) : 509 - 557