This talk contributes to the theoretical understanding of deep learning by considering global convergence for nonconvex phase retrieval.  Specifically,  we consider the problem of solving systems of quadratic equations, namely, recovering an object of interest x from m quadratic equations or samples y_i = (a^T_i x)^2, 1 ≤ i ≤ m. This problem, also dubbed as phase retrieval, spans multiple domains including physical sciences and machine learning. We investigate the efficiency of gradient descent (or Wirtinger flow) designed for the nonconvex least squares problem. We prove that under Gaussian designs, gradient descent — when randomly initialized — yields an ε-accurate solution in O􏰀 (log n + log(1/ε)􏰁 )iterations given nearly minimal samples, thus achieving near-optimal computational and sample complexities at once. This provides the first global convergence guarantee concerning vanilla gradient descent for phase retrieval, without the need of (i) carefully-designed initialization, (ii) sample splitting, or (iii) sophisticated saddle-point escaping schemes. All of these are achieved by exploiting the statistical models in analyzing optimization algorithms, via a leave-one-out approach that enables the decoupling of certain statistical dependency between the gradient descent iterates and the data.

(Joint work with Yuxin Chen, Yuejie Chi, and Cong Ma)