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- W2337602455 abstract "Distributed Gradient Descent (DGD) is a well established algorithm to solve the minimization of a sum of multi-agents' objective functions in the network, with the assumption that the network is undirected, i.e., requiring the weight matrices to be doubly-stochastic. In this paper, we present a distributed algorithm, called Directed-Distributed Gradient Descent (D-DGD), to solve the same problem over directed graphs. In each iteration of D-DGD, we augment an additional variable at each agent to record the change in the state evolution. The algorithm simultaneously constructs a row-stochastic matrix and a column-stochastic matrix instead of only a doubly-stochastic matrix. The analysis shows that D-DGD converges at a rate of O(ln k/√k)." @default.
- W2337602455 created "2016-06-24" @default.
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- W2337602455 date "2015-09-01" @default.
- W2337602455 modified "2023-10-18" @default.
- W2337602455 title "Directed-Distributed Gradient Descent" @default.
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- W2337602455 doi "https://doi.org/10.1109/allerton.2015.7447120" @default.
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