A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval
This work addresses the robust phase retrieval problem, which is a nonsmooth and nonconvex optimization challenge, with incremental improvements in efficiency for computational methods.
The paper tackled the robust phase retrieval problem by proposing a new inexact proximal linear algorithm with adaptive stopping criteria, demonstrating through experiments on synthetic and real datasets that it is much more efficient than existing methods like the original proximal linear algorithm and subgradient method.
This paper considers the robust phase retrieval problem, which can be cast as a nonsmooth and nonconvex optimization problem. We propose a new inexact proximal linear algorithm with the subproblem being solved inexactly. Our contributions are two adaptive stopping criteria for the subproblem. The convergence behavior of the proposed methods is analyzed. Through experiments on both synthetic and real datasets, we demonstrate that our methods are much more efficient than existing methods, such as the original proximal linear algorithm and the subgradient method.