[20] R. Fletcher. "A new approach to variable metric algorithms". In: The Computer Journal 13.3 (Jan. 1970), pp. 317-322.
[21] Donald Goldfarb. “A Family of Variable-Metric Methods Derived by Variational Means”. In: Mathematics of Computation 24.109 (1970), pp. 23-26.
[22] Haotian Jiang et al. "An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games, and Its Applications". In: Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing. STOC 2020. Chicago, IL, USA: Association for Computing Machinery, 2020, pp. 944-953.
[23] Jonathan Kelner et al. "Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales". In: Proceedings of Thirty Fifth Conference on Learning Theory. Ed. by Po-Ling Loh and Maxim Raginsky. Vol. 178. Proceedings of Machine Learning Research. PMLR, Feb. 2022, pp. 2431-2540.
[24] Guanghui Lan. "Bundle-level type methods uniformly optimal for smooth and nonsmooth convex optimization". In: Mathematical Programming 149.1 (Feb. 2015), pp. 1-45.
[25] Guanghui Lan, Soomin Lee, and Yi Zhou. "Communication-efficient algorithms for decentralized and stochastic optimization". In: Mathematical Programming 180.1 (Mar. 2020), pp. 237-284.
[26] Quoc Le, Alex Smola, and S.V.N. Vishwanathan. "Bundle Methods for Machine Learning". In: Advances in Neural Information Processing Systems. Ed. by J. Platt et al. Vol. 20. Curran Associates, Inc., 2007.
[27] Yin Tat Lee, Aaron Sidford, and Sam Chiu-wai Wong. "A faster cutting plane method and its implications for combinatorial and convex optimization". In: 2015 IEEE 56th Annual Symposium on Foundations of Computer Science. IEEE. 2015, pp. 1049-1065.
[28] Claude Lemarechal, Arkadi Nemirovski, and Yuri Nesterov. "New variants of bundle methods". In: Mathematical Programming 69.1 (July 1995), pp. 111-147.
[29] Adrian S. Lewis and Michael L. Overton. "Nonsmooth optimization via quasi-Newton methods". In: Mathematical Programming 141.1 (Oct. 2013), pp. 135-163.
[30] Dong C. Liu and Jorge Nocedal. "On the limited memory BFGS method for large scale optimization". In: Mathematical Programming 45.1 (Aug. 1989), pp. 503-528.
[31] Annie Marsden et al. "Efficient convex optimization requires superlinear memory". In: Conference on Learning Theory. PMLR. 2022, pp. 2390-2430.
[32] Dana Moshkovitz and Michal Moshkovitz. “Mixing Implies Lower Bounds for Space Bounded Learning”. In: Proceedings of the 2017 Conference on Learning Theory. PMLR, 2017, pp. 1516–1566.
[33] João F. C. Mota et al. "D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization". In: IEEE Transactions on Signal Processing 61.10 (2013), pp. 2718-2723.
[34] Arkadi Nemirovski and David Borisovich Yudin. Problem Complexity and Method Efficiency in Optimization. A Wiley-Interscience publication. Wiley, 1983.
[35] Yurii Nesterov. "A method of solving a convex programming problem with convergence rate  $O(1 / k^2)$ ". In: Dokl. Akad. Nauk SSSR 269 (3 1983), pp. 543-547.
[36] Yurii Nesterov. Introductory lectures on convex optimization: A basic course. Vol. 87. Springer Science & Business Media, 2003.
[37] Jorge Nocedal. "Updating Quasi-Newton Matrices with Limited Storage". In: Mathematics of Computation 35.151 (1980), pp. 773-782.
[38] Srinivasan Ramaswamy and John E Mitchell. A long step cutting plane algorithm that uses the volumetric barrier. Tech. rep. CiteSeer, 1995.
[39] Ran Raz. "A Time-Space Lower Bound for a Large Class of Learning Problems". In: 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS). 2017, pp. 732-742.
[40] Sashank J. Reddi et al. "AIDE: Fast and Communication Efficient Distributed Optimization". In: ArXiv abs/1608.06879 (2016).
[41] B. Van Scoy, R. A. Freeman, and K. M. Lynch. "The Fastest Known Globally Convergent First-Order Method for Minimizing Strongly Convex Functions". In: IEEE Control Systems Letters PP.99 (2017), pp. 1-1.