Event-triggered asynchronous distributed optimization algorithm with heterogeneous time-varying step-sizes | Neural Computing and Applications Skip to main content
Springer Nature Link
Log in

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

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Song X, Wang C, Gao J (2013) DLRDG: distributed linear regression-based hierarchical data gathering framework in wireless sensor network. Neural Comput Appl 23(7–8):1999–2013

    Article  Google Scholar 

  2. Chen J, Sayed A (2012) Diffusion adaptation strategies for distributed optimization and learning over networks. IEEE Trans Signal Process 60(8):4289–4305

    Article  MathSciNet  MATH  Google Scholar 

  3. Hasan H, Abdulkareem S (2014) Retraction note to: Human–computer interaction using vision-based hand gesture recognition systems: a survey. Neural Comput Appl 25(2):251–261

    Article  Google Scholar 

  4. Li N, Chen L, Dahleh M (2015) Demand response using linear supply function bidding. IEEE Trans Smart Grid 6(4):1827–1838

    Article  Google Scholar 

  5. Ma A, Gao Y, Huang L (2017) Improved differential search algorithm based dynamic resource allocation approach for cloud application. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3280-5

  6. Predd J, Kulkarni S, Poor H (2009) A collaborative training algorithm for distributed learning. IEEE Trans Inf Theory 55(4):1856–1871

    Article  MathSciNet  MATH  Google Scholar 

  7. Angelov P, Sadeghitehran P, Clarke C (2017) AURORA: autonomous real-time on-board video analytics. Neural Comput Appl 28(5):855–865

    Article  Google Scholar 

  8. Dong X, Zhou Y, Ren Z, Zhong Y (2017) Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying. IEEE Trans Ind Electron 64(6):5014–5024

    Article  Google Scholar 

  9. Dong X, Zhou Y, Ren Z, Zhong Y (2018) Theory and experiment on formation-containment control of multiple multirotor unmanned serial vehicle systems. IEEE Trans Autom Sci Eng. https://doi.org/10.1109/TASE.2018.2792327

  10. Hosseini A (2016) A non-penalty recurrent neural network for solving a class of constrained optimization problems. Neural Netw 73:10–25

    Article  MATH  Google Scholar 

  11. Li G, Yan Z, Wang J (2015) A one-layer recurrent neural network for constrained nonconvex optimization. Neural Netw 61:10–21

    Article  MATH  Google Scholar 

  12. He W, Qian F, James L, Chen G, Han Q, Kurthsf J (2015) Quasi-synchronization of heterogeneous dynamic networks via distributed impulsive control: error estimation, optimization and design. Automatica 62:249–262

    Article  MathSciNet  MATH  Google Scholar 

  13. Xu B, He W (2018) Event-triggered cluster consensus of leader-following linear multi-agent systems. J Artif Intell Soft Comput Res 8(4):293–302

    Article  Google Scholar 

  14. He W, Chen G, Zhong W, Qian F (2018) Secure impulsive synchronization control of multi-agent systems under deception attacks. Inf Sci 459:354–368

    Article  MathSciNet  Google Scholar 

  15. Lv S, He W, Qian F, Cao J (2018) Leaderless synchronization of coupled neural networks with the event-triggered mechanism. Neural Netw 105:316–327

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  17. Nedic A, Olshevsky A (2015) Distributed optimization over time-varying directed graphs. IEEE Trans Autom Control 60(3):601–615

    Article  MathSciNet  MATH  Google Scholar 

  18. Nedic A, Olshevsky A, Shi W, Uribe C (2017) Geometrically convergent distributed optimization with uncoordinated step-sizes. In: American Control Conference, Seattle, WA, USA, pp 3950–3955

  19. Xu J, Zhu S, Soh Y, Xie L (2018) Convergence of asynchronous distributed gradient methods over stochastic networks. IEEE Trans Autom Control 63(2):434–448

    Article  MathSciNet  MATH  Google Scholar 

  20. Srivastava K, Nedic A (2011) Distributed asynchronous constrained stochastic optimization. IEEE J Sel Topics Signal Process 5(4):772–790

    Article  Google Scholar 

  21. Zanella F, Varagnolo D, Cenedese A, Pillonetto G, Schenato L (2012) Asynchronous Newton-Raphson consensus for distributed convex optimization. In: 3rd IFAC workshop on distributed estimation and control in networked systems, Santa Barbara, CA, USA, pp 133–138

  22. Wei E, Ozdaglar A (2013) On the \(O(1/k)\) convergence of asynchronous distributed alternating direction method of multipliers. In: the 2013 IEEE Global Conference on Signal and Information Processing, Austin, TX, USA, pp 551–554

  23. Iutzeler F, Bianchi P, Ciblat P, Hachem W (2013) Asynchronous distributed optimization using a randomized alternating direction method of multipliers. In: IEEE 52th annual conference on decision and control, Florence, pp 3671–3676

  24. Jakovetic D, Moura J, Xavier J (2015) Linear convergence rate of a class of distributed augmented Lagrangian algorithms. IEEE Trans Autom Control 60(4):922–936

    Article  MathSciNet  MATH  Google Scholar 

  25. Zhu W, Jiang Z, Feng G (2015) Event-based consensus of multi-agent systems with general linear models. Automatica 50(2):552–558

    Article  MathSciNet  MATH  Google Scholar 

  26. He W, Xu C, Han Q, Qian F, Lang Z (2018) Finite-time \({\cal{L}_2}\) leader–follower consensus of networked Euler–Lagrange systems with external disturbances. IEEE Trans Syst Man Cybern Syst 48(11):1920–1928

    Article  Google Scholar 

  27. Zhao M, Peng C, He W, Song Y (2017) Event-triggered communication for leader-following consensus of second-order multi-agent systems. IEEE Trans Cybern 48(6):1888–1897

    Article  Google Scholar 

  28. He W, Chen G, Han Q, Du W, Cao J, Qian F (2017) Multiagent systems on multilayer networks: synchronization analysis and network design. IEEE Trans Cybern 47(2):327–338

    Google Scholar 

  29. Chen W, Ren W (2016) Event-triggered zero-gradient-sum distributed consensus optimization over directed networks. Automatica 65:90–97

    Article  MathSciNet  MATH  Google Scholar 

  30. Nedic A, Olshevsky A, Shi W (2017) Achieving geometric convergence for distributed optimization over time-varying graphs. SIAM J Control 27(4):2597–2633

    MathSciNet  MATH  Google Scholar 

  31. Liu C, Yang Z, Sun D, Liu X, Liu W (2018) Stability of switched neural networks with time-varying delays. Neural Comput Appl 30(7):2229–2244

    Article  Google Scholar 

Download references

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.

Author information

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

Authors
  1. Tangtang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaofeng Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Guo Chen or Xiaofeng Liao.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04116-w

Keywords

Search

Navigation