  Tensor network algorithms have been remarkably successful solving a variety
of problems in quantum many-body physics. However, algorithms to optimize
two-dimensional tensor networks known as PEPS lack many of the aspects that
make the seminal density matrix renormalization group (DMRG) algorithm so
powerful for optimizing one-dimensional tensor networks known as matrix product
states. We implement a framework for optimizing two-dimensional PEPS tensor
networks which includes all of steps that make DMRG so successful for
optimizing one-dimension tensor networks. We present results for several 2D
spin models and discuss possible extensions and applications.
