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Event-triggered asynchronous distributed optimization algorithm with heterogeneous time-varying step-sizes

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Abstract

This paper concerns distributed convex optimization problems over time-varying undirected graphs, in which the global objective function is expressed as the sum of individual objective functions of the agents. Each agent only knows its local objective functions. To figure out such problems, an event-triggered asynchronous distributed optimization algorithm (termed as EV-ADOA) with time-varying heterogeneous step-sizes is proposed, which is suitable for undirected graphs changing over time. Under two standard assumptions on strongly convex and smoothness of local objective functions, the EV-ADOA can achieve linear convergence with a proper upper bound of the heterogeneous time-varying step-sizes. EV-ADOA with event-triggered scheme can decrease network communication, and the Zeno-like behavior strictly is excluded. The efficiency of EV-ADOA is demonstrated by experiments.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0800601, in part by Australian Research Council under Grant DE160100675, in part by the National Natural Science Foundation of China under Grant 61472331, 61772434, and 61503310, and in part by Fundamental Research Funds for the Central Universities under Grant XDJK2016C103.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, People’s Republic of China

    Tangtang Xie & Xiaofeng Liao

  2. School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW, 2052, Australia

    Tangtang Xie & Guo Chen

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  1. Tangtang Xie
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  2. Guo Chen
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  3. Xiaofeng Liao
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Correspondence to Guo Chen or Xiaofeng Liao.

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Xie, T., Chen, G. & Liao, X. Event-triggered asynchronous distributed optimization algorithm with heterogeneous time-varying step-sizes. Neural Comput & Applic 32, 6175–6184 (2020). https://doi.org/10.1007/s00521-019-04116-w

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