ACCELERATION OF CONVERGENCE TO THE THERMODYNAMIC-EQUILIBRIUM BY INTRODUCING SHUFFLING OPERATIONS TO THE METROPOLIS ALGORITHM OF MONTE-CARLOSIMULATIONS

This paper introduces a broad class of operations, called ''shuffling trials'', used to design nonphysical pathways. It is shown that in gen eral the equilibrium distribution of a system can be attained when phy sically possible pathways are interrupted regularly by nonphysical shu ffling trials. Including properly chosen shuffling trials in the commo nly applied Metropolis algorithm often considerably accelerates the co nvergence to the equilibrium distribution. Shuffling trials are usuall y global changes in the system, sampling efficiently every set of the metastable configurations of the system. Shuffling trials are generate d by symmetric stochastic matrices. Since ergodicity is not a required property, it is particularly easy to construct these matrices in acco rdance with the specificity of the system. The design, application, an d efficiency of the shuffling trials in Monte Carlo simulations are de monstrated on an Ising model of two-dimensional spin lattices.