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 language | English (US) |
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Title of host publication | Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 48-52 |
Number of pages | 5 |
ISBN (Electronic) | 9781538692189 |
DOIs | |
State | Published - Jul 2 2018 |
Event | 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States Duration: Oct 28 2018 → Oct 31 2018 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2018-October |
ISSN (Print) | 1058-6393 |
Conference
Conference | 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 10/28/18 → 10/31/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Quadratic systems
- decentralized optimization
- distributed learning
- nonconvex optimization
- statistical learning over networks