Distributed optimization for Generalized Phase Retrieval over Networks

Ziping Zhao, Songtao Lu, Mingyi Hong, Daniel P. Palomar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we will solve the generalized phase retrieval (PR) problem over a network, where each agent only has a subset of the measurements. The problem is formulated as minimizing the squared loss between the measurements and linear sensing intensity. To solve the problem in a distributed setting, an algorithm named distributed Wirtinger flow (DWF) is proposed. Theoretical analyses show that the proposed DWF algorithm converges to the (approximate) KKT points of the original problem globally in a sublinear rate. The performance of the DWF algorithm is numerically compared with the state-of-the-art method. Simulation results show that DWF is able to recover a high-quality solution for the original PR problem.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages48-52
Number of pages5
ISBN (Electronic)9781538692189
DOIs
StatePublished - Feb 19 2019
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/TerritoryUnited States
CityPacific Grove
Period10/28/1810/31/18

Bibliographical note

Funding Information:
This work was supported in part by the University of Minnesota and the Hong Kong University of Science and Technology via bilateral collaboration funds, and in part by the Sponsorship Scheme for Targeted Strategic Partnership FP602 of the Hong Kong University of Science and Technology. The work of Z. Zhao was supported by the Hong Kong PhD Fellowship Scheme.

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Quadratic systems
  • decentralized optimization
  • distributed learning
  • nonconvex optimization
  • statistical learning over networks

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