Approximation algorithms for the random field ising model

Approximating the partition function of the ferromagnetic Ising model with general external fields is known to be #BIS-hard in the worst case, even for bounded-degree graphs, and it is widely believed that no polynomial-time approximation scheme exists. This motivates an averagecase question: are there classes of instances for which polynomial-time approximation schemes exist? We investigate this question for the random field Ising model on graphs with maximum degree Delta. We establish the existence of fully polynomial-time approximation schemes and samplers with high probability over the random fields if the external fields are independent and identically distributed Gaussians with variance larger than a constant depending only on the inverse temperature and Delta. Our methods work more generally for external fields that are large in absolute value with high probability. The main challenge is that such a hypothesis does not rule out the existence of a positive density of vertices at which the external field is small. These regions, which may have connected components of size Theta(log n), are a barrier to algorithms based on establishing zero-free regions of partition functions and cause worst-case analyses of Glauber dynamics to fail. The analysis of our algorithm is based on percolation on a self-avoiding walk tree.