Convergence of Metropolis-type algorithms for a large canonical ensemble

In this paper, we study the convergence of Metropolis-type algorithms used in modeling statistical systems with a fluctuating number of particles located in a finite volume. We justify the use of Metropolis algorithms for a particular class of such statistical systems. We prove a theorem on the geometric ergodicity of the Markov process modeling the behavior of an ensemble with a fluctuating number of particles in a finite volume whose interaction is described by a potential bounded below and decreasing according to the law r−3−α, α ≥ 0, as r → 0.