Guarantees of riemannian optimization for low rank matrix completion
Ke Wei, Jian‐Feng Cai, Tony F. Chan, Shingyu Leung
Abstract
We establish the exact recovery guarantees for a class of Riemannian optimization methods based on the embedded manifold of low rank matrices for matrix completion. Assume m entries of an n×n rank r matrix are sampled independently and uniformly with replacement. We first show that with high probability the Riemannian gradient descent and conjugate gradient descent algorithms initialized by one step hard thresholding are guaranteed to converge linearly to the measured matrix provided m ≥ C<sub>κ</sub>n<sup>1.5</sup>r log<sup>1.5</sup>(n), where C<sub>κ</sub> is a numerical constant depending on the condition number of the measured matrix. Then the sampling complexity is further improved to m ≥ C<sub>κ</sub>nr<sup>2</sup>log<sup>2</sup>(n) via the resampled Riemannian gradient descent initialization. The analysis of the new initialization procedure relies on an asymmetric restricted isometry property of the sampling operator and the curvature of the low rank matrix manifold. Numerical simulation shows that the algorithms are able to recover a low rank matrix from nearly the minimum number of measurements. © 2020, American Institute of Mathematical Sciences. All rights reserved.