[38] Awi Federgruen and Lijian Lu. Price competition based on relative prices. *Columbia Business School Research Paper*, (13-9), 2016.
[39] Luca Colombo and Paola Labrecciosa. Dynamic oligopoly pricing with reference-price effects. European Journal of Operational Research, 288(3):1006-1016, 2021.
[40] Tianyi Lin, Zhengyuan Zhou, Wenjia Ba, and Jiawei Zhang. Optimal no-regret learning in strongly monotone games with bandit feedback. arXiv preprint arXiv:2112.02856, 2021.
[41] Noah Golowich, Sarath Pattathil, and Constantinos Daskalakis. Tight last-iterate convergence rates for no-regret learning in multi-player games. Advances in neural information processing systems, 33:20766–20778, 2020.
[42] Daniel McFadden. Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics, pages 105-142, 1974.
[43] Martin Zinkevich. Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the 20th international conference on machine learning (icml-03), pages 928–936, 2003.
[44] Elad Hazan, Amit Agarwal, and Satyen Kale. Logarithmic regret algorithms for online convex optimization. Machine Learning, 69(2-3):169-192, 2007.
[45] Jacob Abernethy, Peter L Bartlett, and Elad Hazan. Blackwell approachability and no-regret learning are equivalent. In Proceedings of the 24th Annual Conference on Learning Theory, pages 27-46. JMLR Workshop and Conference Proceedings, 2011.
[46] Yurii Nesterov. Introductory lectures on convex optimization: A basic course, volume 87. Springer Science & Business Media, 2003.
[47] Anna Nagurney and Ding Zhang. Projected dynamical systems and variational inequalities with applications, volume 2. Springer Science & Business Media, 1995.
[48] Gesualdo Scutari, Daniel P Palomar, Francisco Facchinei, and Jong-Shi Pang. Convex optimization, game theory, and variational inequality theory. IEEE Signal Processing Magazine, 27(3):35-49, 2010.
[49] David K Arrowsmith, Colin M Place, CH Place, et al. An introduction to dynamical systems. Cambridge university press, 1990.
[50] Peter J Olver. Nonlinear systems. Lecture Notes, University of Minnesota, 2015.
[51] H.K. Khalil. Nonlinear Systems. Pearson Education. Prentice Hall, 2002.
[52] Panayotis Mertikopoulos, Christos Papadimitriou, and Georgios Piliouras. Cycles in adversarial regularized learning. SODA '18: Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 2703-2717, 2018.
[53] Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, and Georgios Piliouras. Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile. In ICLR 2019-7th International Conference on Learning Representations, pages 1-23, 2019.
[54] Gerasimos Palaiopanos, Ioannis Panageas, and Georgios Piliouras. Multiplicative weights update with constant step-size in congestion games: Convergence, limit cycles and chaos. Advances in Neural Information Processing Systems, 30, 2017.
[55] W. Rudin. Real and Complex Analysis. Mathematics series. McGraw-Hill, 1987.
[56] Zhengyuan Zhou, Panayotis Mertikopoulos, Aris L Moustakas, Nicholas Bambos, and Peter Glynn. Mirror descent learning in continuous games. In 2017 IEEE 56th Annual Conference on Decision and Control (CDC), pages 5776-5783. IEEE, 2017.
[57] Steven CH Hoi, Doyen Sahoo, Jing Lu, and Peilin Zhao. Online learning: A comprehensive survey. Neurocomputing, 459:249-289, 2021.