Distributed stochastic nonsmooth nonconvex optimization |
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Institution: | Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Karlovo namesti 13 Prague, 12000, Czech Republic |
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Abstract: | Distributed consensus optimization has received considerable attention in recent years and several distributed consensus-based algorithms have been proposed for (nonsmooth) convex and (smooth) nonconvex objective functions. However, the behavior of these distributed algorithms on nonconvex, nonsmooth and stochastic objective functions is not understood. Such class of functions and distributed setting are motivated by several applications, including problems in machine learning and signal processing. This paper presents the first convergence analysis of the decentralized stochastic subgradient method for such classes of problems, over networks modeled as undirected, fixed, graphs. |
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Keywords: | Distributed optimization Nonsmooth optimization Nonconvex optimization Stochastic optimization |
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