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CMI Seminar: Guannan Qu

Tuesday, October 22, 2019
4:00pm to 5:00pm
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Annenberg 314
Distributed optimization in multi-agent systems: bridging the gap between centralized and distributed algorithms
Guannan Qu, CMI Postdoc, Caltech,

We consider the distributed optimization problem over a network of agents, where the objective is to optimize a global function formed by a sum of local functions, with each agent only allowed to use local information and local communication with neighbors. This is a problem of fundamental interest in multi-agent systems and has found various applications in sensor networks, multi-robot systems, distributed machine learning, etc. To solve this problem, various distributed gradient methods have been proposed, but in this talk, we show there is a gap in convergence rate between the existing distributed gradient algorithms and the optimal centralized gradient algorithm. We then develop a gradient tracking technique that results in two algorithms that can partially bridge the gap. In particular, one of our algorithms achieves Nesterov-type acceleration in the realm of distributed optimization for the first time.

Based on joint work with Na Li.

For more information, please contact Linda Taddeo by phone at 626-395-6704 or by email at [email protected] or visit Mathematics of Information Seminar - Upcoming Events.