Low-rank matrix estimation plays a central role in various applications across science and engineering. Recently, nonconvex formulations based on factorization are provably solved by simple gradient descent algorithms with strong computational statistical guarantees. However, when the low-rank matrices asymmetric, existing approaches rely adding regularization term to balance scale of two facto...
