Flat histogram method comparison on 2D Ising Model

We examine the convergence of the Stochastic Approximation with a Dynamic update factor (SAD) algorithm applied to the 2D ising model. Comparison with SAMC and WL methods show that SAD performs robustly and without user input knowledge of an energy range. We confirm that SAD is more powerful in the common case in which the range of energies is not known in advance.