Distributed subgradient methods and quantization effects

• Published in: 2008 47th IEEE Conference on Decision and Control pp. 4177 - 4184
• Main Authors: Nedic, A., Olshevsky, A., Ozdaglar, A., Tsitsiklis, J.N.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2008
• Subjects: Communication industry; Computer industry; Control systems; Convergence; Electrical equipment industry; Network topology; Optimization methods; Quantization; Resource management; Upper bound
• ISBN: 9781424431236; 1424431239
• ISSN: 0191-2216
• DOI: 10.1109/CDC.2008.4738860

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.