Rate analysis of dual averaging for nonconvex distributed optimization

QC 20240223Part of ISBN 9781713872344
This work studies nonconvex distributed constrained optimization over stochastic communication networks. We revisit the distributed dual averaging algorithm, which is known to converge for convex problems. We start from the centralized case, for which the change of two consecutive updates is taken as the suboptimality measure. We validate the use of such a measure by showing that it is closely related to stationarity. This equips us with a handle to study the convergence of dual averaging in nonconvex optimization. We prove that the squared norm of this suboptimality measure converges at rate O(1/t). Then, for the distributed setup we show convergence to the stationary point at rate O(1/t). Finally, a numerical example is given to illustrate our theoretical results.

Wu, XuyangKTH,Reglerteknik,Digital Futures(Swepub:kth)u14o48de (författare)
Yi, XinleiKTH,Reglerteknik,Digital Futures(Swepub:kth)u14392qg (författare)
Shi, YangDepartment of Mechanical Engineering, University of Victoria, Victoria, B.C. V8W 3P6, Canada (författare)
Johansson, Karl H.,1967-KTH,Reglerteknik,Digital Futures(Swepub:kth)u1h0d9k2 (författare)
KTHReglerteknik (creator_code:org_t)

Av författaren/redakt...: Liu, Changxin; Wu, Xuyang; Yi, Xinlei; Shi, Yang; Johansson, Karl ...

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