Provable Second-Order Riemannian Gauss-Newton Method for Low-Rank Tensor Estimation ‖

In this paper, we consider the estimation of a low Tucker rank tensor from a number of noisy linear measurements. We propose a Riemannian Gauss-Newton (RGN) method with fast implementations for low Tucker rank tensor estimation. Different from the generic (super)linear convergence guarantee of RGN in the literature, we prove the first quadratic convergence guarantee of RGN for low-rank tensor estimation under some mild conditions. A deterministic estimation error lower bound, which matches the upper bound, is provided that demonstrates the statistical optimality of RGN. The merit of RGN is illustrated through applications of tensor regression and tensor SVD.