Distributed smooth optimisation with event-triggered proportional-integral algorithms

References

• Astrom, K. J., & Bernhardsson, B. M. (2002). Comparison of Riemann and Lebesgue sampling for first order stochastic systems. In Proceedings of the 41st IEEE Conference on Decision and Control (Vol. 2, pp. 2011–2016). IEEE.
   Google Scholar
• Bertsekas, D. P., & Tsitsiklis, J. N. (1989). Parallel and distributed computation: numerical methods (Vol. 23). Prentice Hall.
   Google Scholar
• Boyd, S., Parikh, N., & Chu, E. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc.
   Google Scholar
• Chen, W., & Ren, W. (2016). Event-triggered zero-gradient-sum distributed consensus optimization over directed networks. Automatica, 65(5), 90–97. https://doi.org/10.1016/j.automatica.2015.11.015
   Google Scholar
• Dong, X., Hua, Y., Zhou, Y., Ren, Z., & Zhong, Y. (2018). Theory and experiment on formation-containment control of multiple multirotor unmanned aerial vehicle systems. IEEE Transactions on Automation Science and Engineering, 16(1), 229–240. https://doi.org/10.1109/TASE.2018.2792327
   Web of Science ®Google Scholar
• Gharesifard, B., & Cortés, J. (2013). Distributed continuous-time convex optimization on weight-balanced digraphs. IEEE Transactions on Automatic Control, 59(3), 781–786. https://doi.org/10.1109/TAC.2013.2278132
   Web of Science ®Google Scholar
• Girard, A. (2014). Dynamic triggering mechanisms for event-triggered control. IEEE Transactions on Automatic Control, 60(7), 1992–1997. https://doi.org/10.1109/TAC.9
   Web of Science ®Google Scholar
• Godsil, C., & Royle, G. F. (2001). Algebraic graph theory (Vol. 207). Springer Science & Business Media.
   Google Scholar
• Guo, G., Kang, J., Li, R., & Yang, G. (2020). Distributed model reference adaptive optimization of disturbed multiagent systems with intermittent communications. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2020.3032429
   Google Scholar
• Khalil, H. K. (2002). Nonlinear systems (3rd ed.). Prentice hall.
   Google Scholar
• Kia, S. S., Cortés, J., & Martínez, S. (2015). Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication. Automatica, 55(1), 254–264. https://doi.org/10.1016/j.automatica.2015.03.001
   Google Scholar
• Li, N., Chen, L., & Dahleh, M. A. (2015). Demand response using linear supply function bidding. IEEE Transactions on Smart Grid, 6(4), 1827–1838. https://doi.org/10.1109/TSG.2015.2410131
   Web of Science ®Google Scholar
• Li, Z., Shi, W., & Yan, M. (2019). A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates. IEEE Transactions on Signal Processing, 67(17), 4494–4506. https://doi.org/10.1109/TSP.78
   Web of Science ®Google Scholar
• Liu, J., & Xie, J. (2021). Event-triggered zero-gradient-sum distributed optimisation algorithm with time-varying communication delays. International Journal of Systems Science, 52(1), 110–125. https://doi.org/10.1080/00207721.2020.1820622.
   Web of Science ®Google Scholar
• Liu, S., Xie, L., & Quevedo, D. E. (2016). Event-triggered quantized communication-based distributed convex optimization. IEEE Transactions on Control of Network Systems, 5(1), 167–178. https://doi.org/10.1109/TCNS.2016.2585305
   Web of Science ®Google Scholar
• Lu, J., & Tang, C. Y. (2012). Zero-gradient-sum algorithms for distributed convex optimization: The continuous-time case. IEEE Transactions on Automatic Control, 57(9), 2348–2354. https://doi.org/10.1109/TAC.2012.2184199
   Web of Science ®Google Scholar
• Madan, R., & Lall, S. (2006). Distributed algorithms for maximum lifetime routing in wireless sensor networks. IEEE Transactions on Wireless Communications, 5(8), 2185–2193. https://doi.org/10.1109/TWC.2006.1687734
   Web of Science ®Google Scholar
• Mansoori, F., & Wei, E. (2019). A fast distributed asynchronous Newton-based optimization algorithm. IEEE Transactions on Automatic Control, 65(7), 2769–2784. https://doi.org/10.1109/TAC.2019.2933607.
   Web of Science ®Google Scholar
• Meng, X., Xie, L., & Soh, Y. C. (2017). Asynchronous periodic event-triggered consensus for multi-agent systems. Automatica, 84(2), 214–220. https://doi.org/10.1016/j.automatica.2017.07.008
   Google Scholar
• Meng, X., Xie, L., Soh, Y. C., Nowzari, C., & Pappas, G. J. (2015, December). Periodic event-triggered average consensus over directed graphs. In 2015 54th IEEE Conference on Decision and Control (CDC) (pp. 4151–4156). IEEE.
   Google Scholar
• Nedić, A., & Liu, J. (2018). Distributed optimization for control. Annual Review of Control, Robotics, and Autonomous Systems, 1(1), 77–103. https://doi.org/10.1146/annurev-control-060117-105131
   Google Scholar
• Nedić, A., & Olshevsky, A. (2014). Distributed optimization over time-varying directed graphs. IEEE Transactions on Automatic Control, 60(3), 601–615. https://doi.org/10.1109/TAC.2014.2364096
   Web of Science ®Google Scholar
• Nedić, A., & Ozdaglar, A. (2009). Distributed subgradient methods for multi-agent optimization. IEEE Transactions on Automatic Control, 54(1), 48–61. https://doi.org/10.1109/TAC.2008.2009515
   Web of Science ®Google Scholar
• Pu, S., Shi, W., Xu, J., & Nedić, A. (2020). Push-pull gradient methods for distributed optimization in networks. IEEE Transactions on Automatic Control, 66(1), 1–16. https://doi.org/10.1109/TAC.2020.2972824.
   Web of Science ®Google Scholar
• Qu, G., & Li, N. (2017). Harnessing smoothness to accelerate distributed optimization. IEEE Transactions on Control of Network Systems, 5(3), 1245–1260. https://doi.org/10.1109/TCNS.2017.2698261
   Web of Science ®Google Scholar
• Shi, X., Lin, Z., Zheng, R., & Wang, X. (2020). Distributed dynamic event-triggered algorithm with positive minimum inter-event time for convex optimization problem. International Journal of Control 1–8. https://doi.org/10.1080/00207179.2020.1854866
   Google Scholar
• Shi, W., Ling, Q., Wu, G., & Yin, W. (2015). Extra: An exact first-order algorithm for decentralized consensus optimization. SIAM Journal on Optimization, 25(2), 944–966. https://doi.org/10.1137/14096668X
   Web of Science ®Google Scholar
• Tabuada, P. (2007). Event-triggered real-time scheduling of stabilizing control tasks. IEEE Transactions on Automatic Control, 52(9), 1680–1685. https://doi.org/10.1109/TAC.2007.904277
   Web of Science ®Google Scholar
• Tran, N. T., Wang, Y. W., Liu, X. K., Xiao, J. W., & Lei, Y. (2019). Distributed optimization problem for second-order multi-agent systems with event-triggered and time-triggered communication. Journal of the Franklin Institute, 356(17), 10196–10215. https://doi.org/10.1016/j.jfranklin.2018.02.009
   Web of Science ®Google Scholar
• Tsitsiklis, J. N. (1984). Problems in decentralized decision making and computation (No. LIDS-TH-1424). Massachusetts Inst of Tech Cambridge Lab for Information and Decision Systems.
   Google Scholar
• Tsitsiklis, J., Bertsekas, D., & Athans, M. (1986). Distributed asynchronous deterministic and stochastic gradient optimization algorithms. IEEE Transactions on Automatic Control, 31(9), 803–812. https://doi.org/10.1109/TAC.1986.1104412
   Web of Science ®Google Scholar
• Varagnolo, D., Zanella, F., Cenedese, A., Pillonetto, G., & Schenato, L. (2015). Newton-Raphson consensus for distributed convex optimization. IEEE Transactions on Automatic Control, 61(4), 994–1009. https://doi.org/10.1109/TAC.2015.2449811
   Web of Science ®Google Scholar
• Wang, Q., Chen, J., Xin, B., & Zeng, X. (2019). Distributed optimal consensus for Euler-Lagrange systems based on event-Triggered control. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2019.2944857
   Google Scholar
• Wang, J., & Elia, N. (2010, September). Control approach to distributed optimization. In 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 557–561). IEEE.
   Google Scholar
• Wang, D., Gupta, V., & Wang, W. (2018). An event-triggered protocol for distributed optimal coordination of double-integrator multi-agent systems. Neurocomputing, 319(1), 34–41. https://doi.org/10.1016/j.neucom.2018.08.073
   Web of Science ®Google Scholar
• Xin, R., & Khan, U. A. (2018). A linear algorithm for optimization over directed graphs with geometric convergence. IEEE Control Systems Letters, 2(3), 315–320. https://doi.org/10.1109/LCSYS.2018.2834316
   Google Scholar
• Yang, T., Yi, X., Wu, J., Yuan, Y., Wu, D., Meng, Z., Hong, Y., Wang, H., Lin, Z., & Johansson, K. H. (2019). Network topology and communication-computation tradeoffs in decentralized optimization. A Survey of Distributed Optimization. Annual Reviews in Control, 47(1), 278–305. https://doi.org/10.1016/j.arcontrol.2019.05.006
   Google Scholar
• Yao, L., Yuan, Y., Sundaram, S., & Yang, T. (2018, June). Distributed finite-time optimization. In 2018 IEEE 14th International Conference on Control and Automation (ICCA) (pp. 147–154). IEEE.
   Google Scholar
• Yi, X., Yang, T., Wu, J., & Johansson, K. H. (2019). Distributed event-triggered control for global consensus of multi-agent systems with input saturation. Automatica, 100(8), 1–9. https://doi.org/10.1016/j.automatica.2018.10.032
   Google Scholar
• Yi, X., Yao, L., Yang, T., George, J., & Johansson, K. H. (2018, December). Distributed optimization for second-order multi-agent systems with dynamic event-triggered communication. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 3397–3402). IEEE.
   Google Scholar
• Yi, X., Zhang, S., Yang, T., Johansson, K. H., & Chai, T. (2019). Exponential convergence for distributed smooth optimization under the restricted secant inequality condition. arXiv preprint arXiv:1909.03282.
   Google Scholar
• Yuan, K., Ling, Q., & Yin, W. (2016). On the convergence of decentralized gradient descent. SIAM Journal on Optimization, 26(3), 1835–1854. https://doi.org/10.1137/130943170
   Web of Science ®Google Scholar
• Zhang, H., & Cheng, L. (2015). Restricted convexity and its applications to convergence analysis of gradient-type methods in convex optimization. Optimization Letters, 9(5), 961–979. https://doi.org/10.1007/s11590-014-0795-x
   Web of Science ®Google Scholar
• Zhang, Y., Deng, Z., & Hong, Y. (2017). Distributed optimal coordination for multiple heterogeneous Euler–Lagrangian systems. Automatica, 79(3–4), 207–213. https://doi.org/10.1016/j.automatica.2017.01.004
   Google Scholar
• Zhao, X., Tu, S. Y., & Sayed, A. H. (2012). Diffusion adaptation over networks under imperfect information exchange and non-stationary data. IEEE Transactions on Signal Processing, 60(7), 3460–3475. https://doi.org/10.1109/TSP.2012.2192928
   Web of Science ®Google Scholar
• Zhu, M., & Martínez, S. (2010). Discrete-time dynamic average consensus. Automatica, 46(2), 322–329. https://doi.org/10.1016/j.automatica.2009.10.021
   Web of Science ®Google Scholar