- Abstract:
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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 ...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
-
(pdf, 2.4mb)
-
- Publisher copy:
- 10.1109/TAC.2017.2747505
-
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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:
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0018-9286
- Pubs id:
-
pubs:722821
- URN:
-
uri:3d5614e6-d5e0-4a29-9d1a-f5824108352c
- UUID:
-
uuid:3d5614e6-d5e0-4a29-9d1a-f5824108352c
- Local pid:
- pubs:722821
- Copyright holder:
- IEEE
- Copyright date:
- 2017
- Notes:
-
Copyright © 2017 IEEE. This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/TAC.2017.2747505
Journal article
Distributed constrained optimization and consensus in uncertain networks via proximal minimization
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