There is no abstract available for this article.
This work was supported by Science and Technology Facilities Council (Grant No. ST/N006852/1).
[1] Li Z H, Ding Z T, Sun J Y. Distributed adaptive convex optimization on directed graphs via continuous-time algorithms. IEEE Trans Autom Control, 2018, 63: 1434-1441 CrossRef Google Scholar
[2] Gharesifard B, Cortes J. Distributed continuous-time convex optimization on weight-balanced digraphs. IEEE Trans Autom Control, 2014, 59: 781-786 CrossRef Google Scholar
[3] Kia S S, Cortés J, Martínez S. Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication. Automatica, 2015, 55: 254-264 CrossRef Google Scholar
[4] Wang J, Elia N. A control perspective for centralized and distributed convex optimization. In: Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, 2011. 3800--3805. Google Scholar
[5] Lu J, Tang C Y. Zero-gradient-sum algorithms for distributed convex optimization: the continuous-time case. IEEE Trans Autom Control, 2012, 57: 2348-2354 CrossRef Google Scholar
[6] Yang S F, Liu Q S, Wang J. Distributed optimization based on a multiagent system in the presence of communication delays. IEEE Trans Syst Man Cybern Syst, 2017, 47: 717-728 CrossRef Google Scholar
[7] Mei J, Ren W, Chen J. Distributed consensus of second-order multi-agent systems with heterogeneous unknown inertias and control gains under a directed graph. IEEE Trans Autom Control, 2016, 61: 2019-2034 CrossRef Google Scholar
[8] Horn R A, Johnson C R. Matrix Analysis. Cambridge: Cambridge University Press, 1985. Google Scholar
Figure 1
(Color online) (a) Communication structure $\mathcal{G}$; (b) the agent states $x_i$, $i~=~1,2,\ldots,5$.
Download PDF
