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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nedic, A. Olshevsky, A. Ozdaglar, A. Tsitsiklis, J.N. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA (Olshevsky, A.; Ozdaglar, A.; Tsitsiklis, J.N.) || Dept. of Ind. & Enterprise Syst. Eng., Univ. of Illinois, Urbana, IL, USA (Nedic, A.) |
| Abstract | We consider a convex unconstrained optimization problem that arises in a network of agents whose goal is to cooperatively optimize the sum of the individual agent objective functions through local computations and communications. For this problem, we use averaging algorithms to develop distributed subgradient methods that can operate over a time-varying topology. Our focus is on the convergence rate of these methods and the degradation in performance when only quantized information is available. Based on our recent results on the convergence time of distributed averaging algorithms, we derive improved upper bounds on the convergence rate of the unquantized subgradient method. We then propose a distributed subgradient method under the additional constraint that agents can only store and communicate quantized information, and we provide bounds on its convergence rate that highlight the dependence on the number of quantization levels. |
| Starting Page | 4177 |
| Ending Page | 4184 |
| File Size | 244595 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424431236 |
| ISSN | 01912216 |
| DOI | 10.1109/CDC.2008.4738860 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-09 |
| Publisher Place | Mexico |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Quantization Convergence Optimization methods Network topology Upper bound Resource management Control systems Communication industry Computer industry Electrical equipment industry |
| Content Type | Text |
| Resource Type | Article |
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