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• W2892002891 abstract "This paper considers nonconvex distributed constrained optimization over networks, modeled as directed (possibly time-varying) graphs. We introduce the first algorithmic framework for the minimization of the sum of a smooth nonconvex (nonseparable) function—the agent’s sum-utility—plus a difference-of-convex function (with nonsmooth convex part). This general formulation arises in many applications, from statistical machine learning to engineering. The proposed distributed method combines successive convex approximation techniques with a judiciously designed perturbed push-sum consensus mechanism that aims to track locally the gradient of the (smooth part of the) sum-utility. Sublinear convergence rate is proved when a fixed step-size (possibly different among the agents) is employed whereas asymptotic convergence to stationary solutions is proved using a diminishing step-size. Numerical results show that our algorithms compare favorably with current schemes on both convex and nonconvex problems." @default.
• W2892002891 created "2018-09-27" @default.
• W2892002891 creator A5035701991 @default.
• W2892002891 creator A5041765559 @default.
• W2892002891 date "2019-02-16" @default.
• W2892002891 modified "2023-10-04" @default.
• W2892002891 title "Distributed nonconvex constrained optimization over time-varying digraphs" @default.
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• W2892002891 doi "https://doi.org/10.1007/s10107-018-01357-w" @default.
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