[1]
|
N. S. Aybat and E. Y. Hamedani, Distributed primal-dual method for multi-agent sharing problem with conic constraints, 2016 50th Asilomar Conference on Signals, Systems and Computers, (2016), 777–782.
doi: 10.1109/ACSSC.2016.7869152.
|
[2]
|
N. S. Aybat and E. Y. Hamedani, A distributed ADMM-like method for resource sharing over time-varying networks, SIAM J. Optim., 29 (2019), 3036-3068.
doi: 10.1137/17M1151973.
|
[3]
|
B. Baingana, G. Mateos and G. B. Giannakis, Proximal-gradient algorithms for tracking cascades over social networks, IEEE Journal of Selected Topics in Signal Processing, 8 (2014), 563-575.
doi: 10.1109/JSTSP.2014.2317284.
|
[4]
|
A. Beck, A. Nedić, A. Ozdaglar and M. Teboulle, An $O(1/k)$ gradient method for network resource allocation problems, IEEE Trans. Control Netw. Syst., 1 (2014), 64-73.
doi: 10.1109/TCNS.2014.2309751.
|
[5]
|
S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, Cambridge, 2004.
doi: 10.1017/CBO9780511804441.
|
[6]
|
T.-H. Chang, A. Nedić and A. Scaglione, Distributed constrained optimization by consensus-based primal-dual perturbation method, IEEE Trans. Automat. Control, 59 (2014), 1524-1538.
doi: 10.1109/TAC.2014.2308612.
|
[7]
|
T.-H. Chang, M. Hong and X. Wang, Multi-agent distributed optimization via inexact consensus ADMM, IEEE Trans. Signal Process., 63 (2015), 482-497.
doi: 10.1109/TSP.2014.2367458.
|
[8]
|
T.-H. Chang, A proximal dual consensus ADMM method for multi-agent constrained optimization, IEEE Trans. Signal Process., 64 (2016), 3719-3734.
doi: 10.1109/TSP.2016.2544743.
|
[9]
|
J. C. Duchi, A. Agarwal and M. J. Wainwright, Dual averaging for distributed optimization: Convergence analysis and network scaling, IEEE Trans. Automat. Control, 57 (2012) 592–606.
doi: 10.1109/TAC.2011.2161027.
|
[10]
|
A. Falsone, K. Margellos, S. Garatti and M. Prandini, Dual decomposition for multi-agent distributed optimization with coupling constraints, Automatica J. IFAC, 84 (2017), 149-158.
doi: 10.1016/j.automatica.2017.07.003.
|
[11]
|
X. S. Han, H. B. Gooi and D. S. Kirschen, Dynamic economic dispatch: Feasible and optimal solutions, 2001 Power Engineering Society Summer Meeting. Conference Proceedings, 16 (2001), 22-28.
doi: 10.1109/PESS.2001.970332.
|
[12]
|
J. Li, C. Wu, Z. Wu and Q. Long, Gradient-free method for nonsmooth distributed optimization, J. Global Optim., 61 (2015), 325-340.
doi: 10.1007/s10898-014-0174-2.
|
[13]
|
J. Li, G. Chen, Z. Dong and Z. Wu, A fast dual proximal-gradient method for separable convex optimization with linear coupled constraints, Comput. Optim. Appl., 64 (2016), 671-697.
doi: 10.1007/s10589-016-9826-0.
|
[14]
|
J. Li, C. Gu, Z. Wu and C. Wu, Distributed optimization methods for nonconvex problems with inequality constraints over time-varying networks, Complexity, 2017 (2017), Article ID 3610283, 10 pp.
doi: 10.1155/2017/3610283.
|
[15]
|
P. Di Lorenzo and G. Scutari, NEXT: In-network nonconvex optimization, IEEE Trans. Signal Inform. Process. Netw., 2 (2016), 120-136.
doi: 10.1109/TSIPN.2016.2524588.
|
[16]
|
G. Mateos and G. B. Giannakis, Distributed recursive least-squares: Stability and performance analysis, IEEE Trans. Signal Process., 60 (2012), 3740-3754.
doi: 10.1109/TSP.2012.2194290.
|
[17]
|
D. K. Molzahn, F. Dörfler, H. Sandberg, S. H. Low, S. Chakrabarti, R. Baldick and J. Lavaei, A survey of distributed optimization and control algorithms for electric power systems, IEEE Transactions on Smart Grid, 8 (2017), 2941-2962.
|
[18]
|
A. Nedić and A. Ozdaglar, Distributed subgradient methods for multi-agent optimization, IEEE Trans. Automat. Control, 54 (2009), 48-61.
doi: 10.1109/TAC.2008.2009515.
|
[19]
|
A. Nedić and A. Olshevsky, Distributed optimization over time-varying directed graphs, IEEE Trans. Automat. Control, 60 (2015), 601-615.
doi: 10.1109/TAC.2014.2364096.
|
[20]
|
A. Nedić, A. Olshevsky and W. Shi, Achieving geometric convergence for distributed optimization over time-varying graphs, SIAM J. Optim., 27 (2017), 2597-2633.
doi: 10.1137/16M1084316.
|
[21]
|
B. T. Polyak, Introduction to Optimization, Optimization Software, Inc., Publications Division, New York, 1987.
|
[22]
|
S. S. Ram, A. Nedić and V. V. Veeravalli, Distributed stochastic subgradient projection algorithms for convex optimization, J. Optim. Theory Appl., 147 (2010), 516-545.
doi: 10.1007/s10957-010-9737-7.
|
[23]
|
W. Shi, Q. Ling, K. Yuan, G. Wu and W. Yin, On the linear convergence of the ADMM in decentralized consensus optimization, IEEE Trans. Signal Process., 62 (2014), 1750-1761.
doi: 10.1109/TSP.2014.2304432.
|
[24]
|
W. Shi, Q. Ling, G. Wu and W. Yin, EXTRA: An exact first-order algorithm for decentralized consensus optimization, SIAM J. Optim., 25 (2015), 944-966.
doi: 10.1137/14096668X.
|
[25]
|
K. I. Tsianos, S. Lawlor and M. G. Rabbat, Push-sum distributed dual averaging for convex optimization, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), (2012), 5453–5458.
doi: 10.1109/CDC.2012.6426375.
|
[26]
|
S. Wang and C. Li, Distributed robust optimization in networked system, IEEE Transactions on Cybernetics, 47 (2017), 2321-2333.
doi: 10.1109/TCYB.2016.2613129.
|
[27]
|
X. Xia and A. M. Elaiw, Optimal dynamic economic dispatch of generation: A review, Electric Power Systems Research, 80 (2010), 975-986.
doi: 10.1016/j.epsr.2009.12.012.
|
[28]
|
C. Yang, Y. Xu, L. Zhou and Y. Sun, Model-free composite control of flexible manipulators based on adaptive dynamic programming, Complexity, 2018 (2018), Article ID 9720309, 9 pp.
doi: 10.1155/2018/9720309.
|
[29]
|
D. Yuan, S. Xu and H. Zhao, Distributed primal-dual subgradient method for multiagent optimization via consensus algorithms, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41 (2011), 1715-1724.
|
[30]
|
D. Yuan, D. W. Ho and Y. Hong, On convergence rate of distributed stochastic gradient algorithm for convex optimization with inequality constraints, SIAM J. Control Optim., 54 (2016), 2872-2892.
doi: 10.1137/15M1048896.
|
[31]
|
D. Yuan, D. W. C. Ho and S. Xu, Regularized primal-dual subgradient method for distributed constrained optimization, IEEE Transactions on Cybernetics, 46 (2016), 2109-2118.
doi: 10.1109/TCYB.2015.2464255.
|
[32]
|
Y. Zhang and G. B. Giannakis, Distributed stochastic market clearing with high-penetration wind power, IEEE Transactions on Power Systems, 31 (2016), 895-906.
|
[33]
|
Z. Zhang and M. Y. Chow, Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid, IEEE Transactions on Power Systems, 27 (2012), 1761-1768.
doi: 10.1109/TPWRS.2012.2188912.
|
[34]
|
M. Zhu and S. Martínez, On distributed convex optimization under inequality and equality constraints, IEEE Trans. Automat. Control, 57 (2012), 151-164.
doi: 10.1109/TAC.2011.2167817.
|