Shalev-Shwartz S. Online learning and online convex optimization. Found Trends in Mach Learn. 2012;4(2):107–94.
Zhang L, Lu S, Zhou Z. Adaptive online learning in dynamic environments. In: Advances in Neural Information Processing Systems, Montréal, 2018;1330–40
Awerbuch B, Kleinberg R. Online linear optimization and adaptive routing. J Comput Syst Sci. 2008;74(1):97–114.
Article MathSciNet Google Scholar
He T, Goeckel D, Raghavendra R, Towsley D. Endhost-based shortest path routing in dynamic networks: an online learning approach. In: Proceedings of the IEEE INFOCOM, Turin, 2013;2202–10
Bitran GR, Caldentey R. An overview of pricing models for revenue management. Manufact Serv Operation Manage. 2003;5(3):203–29.
Balseiro SR, Gur Y. Learning in repeated auctions with budgets: regret minimization and equilibrium. Manage Sci. 2019;65(9):3952–68.
Feldman J, Mehta A, Mirrokni VS, Muthukrishnan S. Online stochastic matching: beating 1-1/e. In: 50th Annual IEEE Symposium on Foundations of Computer Science, Atlanta, 2009;117–26
Hazan E. Introduction to online convex optimization. Found Trends Optimization. 2016;2(3–4):157–325.
Iusem A. On the convergence properties of the projected gradient method for convex optimization. Comput Appl Math. 2003;22(1):37–52.
Hazan E, Agarwal A, Kale S. Logarithmic regret algorithms for online convex optimization. Mach Learn. 2007;69:169–92.
Frank M, Wolfe P. An algorithm for quadratic programming. Naval Res Logistic Quart. 1956;3(1–2):95–110.
Article MathSciNet Google Scholar
Wan Y, Zhang L. Projection-free online learning over strongly convex sets. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, Virtual Event, 2021;10076–84
Chen L, Zhang M, Karbasi A. Projection-free bandit convex optimization. In: the 22nd International Conference on Artificial Intelligence and Statistics, Okinawa, 2019;2047–56
Sayed A, Tu S, Chen J, Zhao X, Towfic ZJ. Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior. IEEE Signal Process Magaz. 2013;30(3):155–71.
Liu S, Qiu Z, Xie L. Convergence rate analysis of distributed optimization with projected subgradient algorithm. Automatic. 2017;83:162–9.
Article MathSciNet Google Scholar
Hosseini S, Chapman A, Mesbahi M. Online distributed optimization via dual averaging. In: Proceedings of the 52nd IEEE Conference on Decision and Control, Florence, 2013;1484–89
Zhang W, Zhao P, Zhu W, Hoi SCH, Zhang T. Projection-free distributed online learning in networks. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, 2017;4054–62
Zhu J, Wu Q, Zhang M, Zheng R, Li K. Projection-free decentralized online learning for submodular maximization over time-varying networks. J Mach Learn Res. 2021;22:51–15142.
Zhang M, Hao B, Ge Q, Zhu J, Zheng R, Wu Q. Distributed adaptive subgradient algorithms for online learning over time-varying networks. IEEE Trans Syst, Man, Cybern: Syst. 2022;52(7):4518–29.
Zhou Y, Huang K, Cheng C, Wang X, Liu X. LightAdam: towards a fast and accurate adaptive momentum online algorithm. Cogn Comput. 2022;14(2):764–79.
Dimarogonas DV, Frazzoli E, Johansson KH. Distributed event-triggered control for multi-agent systems. IEEE Trans Automat Contr. 2012;57(5):1291–7.
Article MathSciNet Google Scholar
Kajiyama Y, Hayashi N, Takai S. Distributed subgradient method with edge-based event-triggered communication. IEEE Trans Automat Contr. 2018;63(7):2248–55.
Article MathSciNet Google Scholar
Liu S, Xie L, Quevedo DE. Event-triggered quantized communication based distributed convex optimization. IEEE Trans Contr Netw Syst. 2018;5(1):167–78.
Article MathSciNet Google Scholar
Yamashita M, Hayashi N, Takai S. Dynamic regret analysis for event-triggered distributed online optimization algorithm. IEICE Trans Fundament Electron, Commun Comput Sci 2021;104-A(2):430–37
Cao X, Basar T. Decentralized online convex optimization with event-triggered communications. IEEE Trans Signal Process. 2021;69:284–99.
Article MathSciNet Google Scholar
Okamoto K, Hayashi N, Takai S. Distributed online adaptive gradient descent with event-triggered communication. IEEE Trans Contr Netw Syst. 2024;11(2):610–22.
Article MathSciNet Google Scholar
Yang T, Lin Q, Zhang L. A richer theory of convex constrained optimization with reduced projections and improved rates. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, 2017;3901–10
Zhang L, Yang T, Jin R, He X. O(logt) projections for stochastic optimization of smooth and strongly convex functions. In: Proceedings of the 30th International Conference on Machine Learning, Atlanta, 2013; 1121–29
Hazan E, Kale S. Projection-free online learning. In: Proceedings of the 29th International Conference on Machine Learning, Edinburgh 2012
Levy K, Krause A. Projection free online learning over smooth sets. In: the 22nd International Conference on Artificial Intelligence and Statistics, Naha, 2019;1458–66
Ram S, Nedic A, Veeravalli V. Distributed stochastic subgradient projection algorithms for convex optimization. J Optimiz Theory Appl. 2010;147(3):516–45.
Article MathSciNet Google Scholar
Lu K, Wang L. Online distributed optimization with nonconvex objective functions: sublinearity of first-order optimality condition-based regret. IEEE Trans Automat Contr. 2022;67(6):3029–35.
Article MathSciNet Google Scholar
Jiang X, Zeng X, Xie L, Sun J, Chen J. Distributed stochastic projection-free solver for constrained optimization. 2022, arXiv:2204.10605
Wu Q, Zhu J, Ge Q, Zhang M. An accelerated distributed online learning algorithm based on conditional gradient. Acta Automat Sinica 2022;45
Liu C, Li H, Shi Y, Xu D. Distributed event-triggered gradient method for constrained convex minimization. IEEE Trans Automat Contr. 2020;65(2):778–85.
Article MathSciNet Google Scholar
Watts DJ, Strogatz SH. Collective dynamics of “small-world” networks. Nature. 1998;393:440–2.
Wai H, Lafond J, Scaglione A, Moulines E. Decentralized Frank-Wolfe algorithm for convex and nonconvex problems. IEEE Trans Automat Contr. 2017;62(11):5522–37.
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