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Convergence rate analysis of a subgradient averaging algorithm for distributed optimisation with different constraint sets

Abstract:
We consider a multi-agent setting with agents exchanging information over a network to solve a convex constrained optimisation problem in a distributed manner. We analyse a new algorithm based on local subgradient exchange under undirected time-varying communication. First, we prove asymptotic convergence of the iterates to a minimum of the given optimisation problem for time-varying step-sizes of the form c(k)=ηk+1, for some η > 0. We then restrict attention to step-size choices c(k)=ηk+1√,η>0, and establish a convergence of (ln(k)k√) in objective value. Our algorithm extends currently available distributed subgradient/proximal methods by: (i) accounting for different constraint sets at each node, and (ii) enhancing the convergence speed thanks to a subgradient averaging step performed by the agents. A numerical example demonstrates the efficacy of the proposed algorithm.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/CDC40024.2019.9029252

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-3565-8967


Publisher:
Institute of Electrical and Electronics Engineers
Host title:
2019 IEEE 58th Conference on Decision and Control (CDC)
Pages:
7448 - 7453
Publication date:
2020-03-12
Acceptance date:
2019-07-19
Event title:
2019 IEEE 58th Conference on Decision and Control (CDC)
Event location:
Nice, France
Event website:
https://cdc2019.ieeecss.org
Event start date:
2019-12-11
Event end date:
2019-12-13
DOI:
EISSN:
2576-2370
ISSN:
0743-15469
EISBN:
9781728113982
ISBN:
9781728113999


Language:
English
Keywords:
Pubs id:
pubs:1046519
UUID:
uuid:d20cd528-1d55-45ec-81ad-3b6b5b4102ad
Local pid:
pubs:1046519
Source identifiers:
1046519
Deposit date:
2019-08-20

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