On the Infinite Swapping Limit for Parallel Tempering

Parallel tempering, also known as replica exchange sampling, is an important method for simulating complex systems. In this algorithm simulations are conducted in parallel at a series of temperatures, and the key feature of the algorithm is a swap mechanism that exchanges configurations between the parallel simulations at a given rate. The mechanism is designed to allow the low temperature system of interest to escape from deep local energy minima where it might otherwise be trapped via those swaps with the higher temperature components. In this paper we introduce a performance criterion for such schemes based on large deviation theory and argue that the rate of convergence is a monotone increasing function of the swap rate. This motivates the study of the limit process as the swap rate goes to infinity. We construct a scheme which is equivalent to this limit in a distributional sense but which involves no swapping at all. Instead, the effect of the swapping is captured by a collection of weights that inf...

[1]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[2]  C. Geyer Markov Chain Monte Carlo Maximum Likelihood , 1991 .

[3]  Wang,et al.  Replica Monte Carlo simulation of spin glasses. , 1986, Physical review letters.

[4]  Michel Loève,et al.  Probability Theory I , 1977 .

[5]  Bruce E. Hajek,et al.  Review of 'Approximation and Weak Convergence Methods for Random Processes, with Applications to Stochastic Systems Theory' (Kushner, H.J.; 1984) , 1985, IEEE Transactions on Information Theory.

[6]  S. Ethier,et al.  Markov Processes: Characterization and Convergence , 2005 .

[7]  C. Predescu,et al.  The incomplete beta function law for parallel tempering sampling of classical canonical systems. , 2003, The Journal of chemical physics.

[8]  Daniel J. Sindhikara,et al.  Exchange Often and Properly in Replica Exchange Molecular Dynamics. , 2010, Journal of chemical theory and computation.

[9]  S. Varadhan,et al.  Asymptotic evaluation of certain Markov process expectations for large time , 1975 .

[10]  Daniel J. Sindhikara,et al.  Exchange frequency in replica exchange molecular dynamics. , 2008, The Journal of chemical physics.

[11]  C. Predescu,et al.  On the efficiency of exchange in parallel tempering monte carlo simulations. , 2004, The journal of physical chemistry. B.

[12]  P. Peskun,et al.  Optimum Monte-Carlo sampling using Markov chains , 1973 .

[13]  A. Heuer Energy Landscapes. Applications to Clusters, Biomolecules and Glasses. By David J. Wales. , 2005 .

[14]  J. D. Doll,et al.  Brownian dynamics as smart Monte Carlo simulation , 1978 .

[15]  S. Rosenthal,et al.  Asymptotic Variance and Convergence Rates of Nearly-Periodic MCMC Algorithms , 2002 .

[16]  Harold J. Kushner,et al.  Approximation and Weak Convergence Methods for Random Processes , 1984 .

[17]  Michael W Deem,et al.  Parallel tempering: theory, applications, and new perspectives. , 2005, Physical chemistry chemical physics : PCCP.

[18]  J. Lynch,et al.  A weak convergence approach to the theory of large deviations , 1997 .

[19]  P. Dupuis,et al.  An infinite swapping approach to the rare-event sampling problem. , 2011, The Journal of chemical physics.

[20]  David A. Kofke,et al.  ARTICLES On the acceptance probability of replica-exchange Monte Carlo trials , 2002 .

[21]  Ross G. Pinsky,et al.  ON EVALUATING THE DONSKER-VARADHAN I-FUNCTION , 1985 .

[22]  S. Varadhan,et al.  Central limit theorem for additive functionals of reversible Markov processes and applications to simple exclusions , 1986 .

[23]  Richard L. Tweedie,et al.  Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.

[24]  L. Tierney A note on Metropolis-Hastings kernels for general state spaces , 1998 .

[25]  D. Woodard,et al.  Conditions for Rapid and Torpid Mixing of Parallel and Simulated Tempering on Multimodal Distributions , 2009, 0906.2341.