A Geometrically Converging Dual Method for Distributed Optimization Over Time-Varying Graphs
نویسندگان
چکیده
In this article, we consider a distributed convex optimization problem over time-varying undirected networks. We propose dual method, primarily averaged network ascent (PANDA), that is proven to converge R-linearly the optimal point given agents' objective functions are strongly and have Lipschitz continuous gradients. Like decomposition, PANDA requires half amount of variable exchanges per iterate methods based on DIGing, can provide with practical improved performance as empirically demonstrated.
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ژورنال
عنوان ژورنال: IEEE Transactions on Automatic Control
سال: 2021
ISSN: ['0018-9286', '1558-2523', '2334-3303']
DOI: https://doi.org/10.1109/tac.2020.3018743