  The Density Matrix Renormalization Group (DMRG) method scales exponentially
in the system width for models in two dimensions, but remains one of the most
powerful methods for studying 2D systems with a sign problem. Reviewing past
applications of DMRG in 2D demonstrates its success in treating a wide variety
of problems, although it remains underutilized in this setting. We present
techniques for performing cutting edge 2D DMRG studies including methods for
ensuring convergence, extrapolating finite-size data and extracting gaps and
excited states. Finally, we compare the current performance of a recently
developed tensor network method to 2D DMRG.
