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• W3014345690 abstract "In this paper, we consider the problem of distributed consensus optimization over multi-agent networks with directed network topology. Assuming each agent has a local cost function that is smooth and strongly convex, the global objective is to minimize the average of all the local cost functions. To solve the problem, we introduce a robust gradient tracking method (R-Push-Pull) adapted from the recently proposed Push-Pull/AB algorithm. R-Push-Pull inherits the advantages of Push-Pull and enjoys linear convergence to the optimal solution with exact communication. Under noisy information exchange, R-Push-Pull is more robust than the existing gradient tracking based algorithms; the solutions obtained by each agent reach a neighborhood of the optimum in expectation exponentially fast under a constant stepsize policy. We provide a numerical example that demonstrate the effectiveness of R-Push-Pull." @default.
• W3014345690 created "2020-04-10" @default.
• W3014345690 creator A5032734653 @default.
• W3014345690 date "2020-03-31" @default.
• W3014345690 modified "2023-09-23" @default.
• W3014345690 title "A Robust Gradient Tracking Method for Distributed Optimization over Directed Networks" @default.
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• W3014345690 doi "https://doi.org/10.48550/arxiv.2003.13980" @default.
• W3014345690 hasPublicationYear "2020" @default.
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