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CDC
2008
IEEE

Distributed subgradient methods and quantization effects

11 years 4 months ago
Distributed subgradient methods and quantization effects
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 timevarying 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.
Angelia Nedic, Alexander Olshevsky, Asuman E. Ozda
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where CDC
Authors Angelia Nedic, Alexander Olshevsky, Asuman E. Ozdaglar, John N. Tsitsiklis
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