  We propose an efficient Markov Chain Monte Carlo method for sampling
equilibrium distributions for stochastic lattice models, capable of handling
correctly long and short-range particle interactions. The proposed method is a
Metropolis-type algorithm with the proposal probability transition matrix based
on the coarse-grained approximating measures introduced in a series of works of
M. Katsoulakis, A. Majda, D. Vlachos and P. Plechac, L. Rey-Bellet and
D.Tsagkarogiannis,. We prove that the proposed algorithm reduces the
computational cost due to energy differences and has comparable mixing
properties with the classical microscopic Metropolis algorithm, controlled by
the level of coarsening and reconstruction procedure. The properties and
effectiveness of the algorithm are demonstrated with an exactly solvable
example of a one dimensional Ising-type model, comparing efficiency of the
single spin-flip Metropolis dynamics and the proposed coupled Metropolis
algorithm.
