A New Inexact Proximal Linear Algorithm With Adaptive Stopping Criteria for Robust Phase Retrieval

Zhong Zheng, Shiqian Ma, Lingzhou Xue

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1081-1093
Number of pages13
JournalIEEE Transactions on Signal Processing
StatePublished - 2024

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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