Abstract

In this talk, we delve into two fundamental problems in data science: phase retrieval and matrix recovery. Our focus is on addressing major challenges frequently encountered in data analysis, including nonlinear measurements, large-scale or high-dimensional data, missing entries, and data corruption. To overcome these difficulties, we develop provably efficient nonconvex optimization algorithms that exploit low-dimensional data structures such as sparsity, low rank, and Hankel structure.