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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
Version:
Accepted Manuscript

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Publisher copy:
10.1109/TAC.2017.2747505

Authors


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Department:
Oxford, MPLS, Engineering Science
Falsone, A More by this author
Garatti, S More by this author
Prandini, M More by this 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
Pubs id:
pubs:722821
URN:
uri:3d5614e6-d5e0-4a29-9d1a-f5824108352c
UUID:
uuid:3d5614e6-d5e0-4a29-9d1a-f5824108352c
Local pid:
pubs:722821

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