  Given in the title are two algorithms to compute the extreme eigenstate of a
high-dimensional Hermitian matrix using the tensor train (TT) / matrix product
states (MPS) representation. Both methods empower the traditional alternating
direction scheme with the auxiliary (e.g. gradient) information, which
substantially improves the convergence in many difficult cases. Being
conceptually close, these methods have different derivation, implementation,
theoretical and practical properties. We emphasize the differences, and
reproduce the numerical example to compare the performance of two algorithms.
