- Abstract:
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We develop a decentralized algorithm for multiagent, convex optimization programs, subject to separable constraints, where the constraint function of each agent involves only its local decision vector, while the decision vectors of all agents are coupled via a common objective function. We construct a variant of the so called Jacobi algorithm and show that, when the objective function is quadratic, convergence to some minimizer of the centralized problem counterpart is achieved. Our algorithm...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
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(pdf, 267.0KB)
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- Publisher copy:
- 10.1109/CDC.2016.7799197
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- Publisher:
- Institute of Electrical and Electronics Engineers Publisher's website
- Publication date:
- 2016-12-05
- Acceptance date:
- 2016-09-12
- DOI:
- ISSN:
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0743-1546
- Pubs id:
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pubs:642279
- URN:
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uri:5121b8c3-53e3-4da4-82b3-3c3797945323
- UUID:
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uuid:5121b8c3-53e3-4da4-82b3-3c3797945323
- Local pid:
- pubs:642279
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2016
- Notes:
- © 2016 IEEE
Conference item
On decentralized convex optimization in a multi-agent setting with separable constraints and its application to optimal charging of electric vehicles
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