Abstract
We present a highly effective, parallelized random-walk–based algorithm to calculate the density of states of complex physical systems. Random walkers' attempted moves from one energy level to another are represented in a stochastic matrix, giving estimates for the transition matrix at infinite temperature. The eigenvector corresponding to the largest eigenvalue is the density of states up to a normalization. We verify the performance on selected examples of Ising spin systems with random coupling constants drawn uniformly from [ − 1,1], of which the exact density of states has been calculated by a branch-and-bound approach.