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Distributed constrained optimization and consensus in uncertain networks via proximal minimization

Abstract:

We provide a unifying framework for distributed convex optimization over time-varying networks, in the presence of constraints and uncertainty, features that are typically treated separately in the literature. We adopt a proximal minimization perspective and show that this set-up allows us to bypass the difficulties of existing algorithms while simplifying the underlying mathematical analysis. We develop an iterative algorithm and show convergence of the resulting scheme to some optimizer of ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/TAC.2017.2747505

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Grant:
H2020, under the project UnCoVerCPS, grant number 643921
Publisher:
IEEE Publisher's website
Journal:
IEEE Transactions on Automatic Control Journal website
Volume:
63
Issue:
5
Pages:
1372-1387
Publication date:
2017-08-30
Acceptance date:
2017-08-09
DOI:
EISSN:
1558-2523
ISSN:
0018-9286
Source identifiers:
722821
Keywords:
Pubs id:
pubs:722821
UUID:
uuid:3d5614e6-d5e0-4a29-9d1a-f5824108352c
Local pid:
pubs:722821
Deposit date:
2017-08-20

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