John Duchi, Stanford University

Abstract

Many iterative methods for optimization use first- and second-order information to iteratively construct, then minimize, local models of the objective to be minimized. In this talk, I will discuss work my group has been doing on stochastic and non-stochastic optimization, in which we leverage alternative structure than standard first- or second-order information, which can often yield dramatic improvements in convergence and optimization accuracy. As particular applications, I will demonstrate the best-known empirical results (with strong theoretical guarantees) for solving phase retrieval problems, among others.
Based on joint work with Hilal Asi and Feng Ruan.

Video