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Proximal minimization based distributed convex optimization

Abstract:

We provide a novel iterative algorithm for distributed convex optimization over time-varying multi-agent networks, in the presence of heterogeneous agent constraints. 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. At every iteration each agent makes a tentative decision by solving a local optimization program, and then communicates this decision with neig...

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Publication status:
In press
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Falsone, A More by this author
Garatti, S More by this author
Prandini, M More by this author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2016
URN:
uuid:edb48317-e54a-4e3a-9fcf-bca181e9e582
Source identifiers:
629140
Local pid:
pubs:629140

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