Eigenvalue analysis of an irreversible random walk with skew detailed balance conditions

Abstract

An irreversible Markov-chain Monte Carlo (MCMC) algorithm with skew detailed balance conditions originally proposed by Turitsyn et al. is extended to general discrete systems on the basis of the Metropolis-Hastings scheme. To evaluate the efficiency of our proposed method, the relaxation dynamics of the slowest mode and the asymptotic variance are studied analytically in a random walk on one dimension. It is found that the performance in irreversible MCMC methods violating the detailed balance condition is improved by appropriately choosing parameters in the algorithm.

14 pages, 6 figures

References in corpus (2)

• Markov Chain Monte Carlo Method without Detailed Balance
• Stochastic gradient method with accelerated stochastic dynamics

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