Slow Mixing of Markov Chains Using Fault Lines and Fat Contours

We show that local dynamics require exponential time for two sampling problems: independent sets on the triangular lattice (the hard-core lattice gas model) and weighted even orientations of the Cartesian lattice (the 8-vertex model). For each problem, there is a parameter i¾?known as the fugacity such that local Markov chains are expected to be fast when i¾?is small and slow when i¾?is large. However, establishing slow mixing for these models has been a challenge because standard contour arguments typically used to show that a chain has small conductance do not seem sufficient. We modify this approach by introducing the notion of fat contoursthat can have nontrivial d-dimensional volume and use these to establish slow mixing of local chains defined for these models.

[1]  A. Sokal,et al.  Bounds on the ² spectrum for Markov chains and Markov processes: a generalization of Cheeger’s inequality , 1988 .

[2]  M. Luby,et al.  Fast convergence of the Glauber dynamics for sampling independent sets , 1999 .

[3]  Dana Randall,et al.  Markov Chain Algorithms for Planar Lattice Structures , 2001, SIAM J. Comput..

[4]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[5]  Mark Jerrum,et al.  Approximate Counting, Uniform Generation and Rapidly Mixing Markov Chains , 1987, International Workshop on Graph-Theoretic Concepts in Computer Science.

[6]  Mark Jerrum,et al.  The Markov chain Monte Carlo method: an approach to approximate counting and integration , 1996 .

[7]  Dana Randall,et al.  Torpid mixing of local Markov chains on 3-colorings of the discrete torus , 2007, SODA '07.

[8]  L. Thomas,et al.  Bound on the mass gap for finite volume stochastic ising models at low temperature , 1989 .

[9]  Dana Randall,et al.  Slow mixing of glauber dynamics via topological obstructions , 2006, SODA '06.

[10]  S. K. Tsang,et al.  Hard-square lattice gas , 1980 .

[11]  P. Tetali,et al.  Analyzing Glauber dynamics by comparison of Markov chains , 2000 .

[12]  R. Dobrushin The problem of uniqueness of a gibbsian random field and the problem of phase transitions , 1968 .

[13]  Jeff Kahn,et al.  On Phase Transition in the Hard-Core Model on ${\mathbb Z}^d$ , 2004, Combinatorics, Probability and Computing.

[14]  D. Aldous Random walks on finite groups and rapidly mixing markov chains , 1983 .

[15]  Alan M. Frieze,et al.  Torpid mixing of some Monte Carlo Markov chain algorithms in statistical physics , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).