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Distributed gradient-based sampling algorithm for least-squares in switching multi-agent networks

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References

  1. Nedic A, Ozdaglar A. Distributed subgradient methods for multi-agent optimization. IEEE Trans Autom Contr, 2009, 54: 48–61

    Article  MathSciNet  Google Scholar 

  2. Lou Y C, Hong Y G, Wang S Y. Distributed continuous-time approximate projection protocols for shortest distance optimization problems. Automatica, 2016, 69: 289–297

    Article  MathSciNet  Google Scholar 

  3. Yi P, Hong Y G, Liu F. Initialization-free distributed algorithms for optimal resource allocation with feasibility constraints and application to economic dispatch of power systems. Automatica, 2016, 74: 259–269

    Article  MathSciNet  Google Scholar 

  4. Bishop C M. Pattern Recognition and Machine Learning. New York: Springer, 2006

    MATH  Google Scholar 

  5. Wang Y H, Lin P, Hong Y G. Distributed regression estimation with incomplete data in multi-agent networks. Sci China Inf Sci, 2018, 61: 092202

    Article  MathSciNet  Google Scholar 

  6. Sagiroglu S, Sinanc D. Big data: a review. In: Proceedings of International Conference on Collaboration Technologies and Systems, California, 2013. 42–47

    Google Scholar 

  7. Zhu R. Gradient-based sampling: an adaptive importance sampling for least-squares. 2016. ArXiv: 1803.00841

    Google Scholar 

  8. Nedic A, Ozdaglar A, Parrilo P A. Constrained consensus and optimization in multi-agent networks. IEEE Trans Autom Contr, 2010, 55: 922–938

    Article  MathSciNet  Google Scholar 

  9. Gholami M R, Jansson M, Strom E G, et al. Diffusion estimation over cooperative multi-agent networks with missing data. IEEE Trans Signal Inf Process Netw, 2016, 2: 276–289

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2018YFA0703800) and National Natural Science Foundation of China (Grant Nos. 61873262, 61733018, 61333001)

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Authors and Affiliations

  1. Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Peng Lin & Hongsheng Qi

  2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

    Peng Lin & Hongsheng Qi

Authors
  1. Peng Lin
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  2. Hongsheng Qi
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Correspondence to Hongsheng Qi.

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Lin, P., Qi, H. Distributed gradient-based sampling algorithm for least-squares in switching multi-agent networks. Sci. China Inf. Sci. 63, 199203 (2020). https://doi.org/10.1007/s11432-018-9731-1

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  • DOI: https://doi.org/10.1007/s11432-018-9731-1

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