[AJJ+22] Sepehr Assadi, Arun Jambulapati, Yujia Jin, Aaron Sidford, and Kevin Tian. Semi-streaming bipartite matching in fewer passes and optimal space. In Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, SODA 2022, pages 627-669. SIAM, 2022.
[AK07] Sanjeev Arora and Satyen Kale. A combinatorial, primal-dual approach to semidefinite programs. In Proceedings of the 39th Annual ACM Symposium on Theory of Computing, 2007, pages 227-236, 2007.
[AL17] Zeyuan Allen-Zhu and Yuanzhi Li. Follow the compressed leader: Faster online learning of eigenvectors and faster MMWU. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, pages 116-125, 2017.
[ALdOW17] Zeyuan Allen-Zhu, Yuzhhi Li, Rafael Mendes de Oliveira, and Avi Wigderson. Much faster algorithms for matrix scaling. In 58th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2017, pages 890-901. IEEE Computer Society, 2017.
[AP20] Jason M. Altschuler and Pablo A. Parrilo. Approximating min-mean-cycle for low-diameter graphs in near-optimal time and memory. CoRR, abs/2004.03114v1, 2020.
[AW21] Josh Alman and Virginia Vassilevska Williams. A refined laser method and faster matrix multiplication. In Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, SODA 2021, pages 522-539. SIAM, 2021.
[AWR17] Jason M. Altschuler, Jonathan Weed, and Philippe Rigollet. Near-linear time approximation algorithms for optimal transport via sinkhorn iteration. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pages 1964-1974, 2017.
[BBN13] Michel Baes, Michael Bürgisser, and Arkadi Nemirovski. A randomized mirror-prox method for solving structured large-scale matrix saddle-point problems. SIAM J. Optim., 23(2):934-962, 2013.
[BBT17] Heinz H. Bauschke, Jérôme Bolte, and Marc Teboulle. A descent lemma beyond lipschitz gradient continuity: First-order methods revisited and applications. Math. Oper. Res., 42(2):330-348, 2017.
[BJKS18] Jose H. Blanchet, Arun Jambulapati, Carson Kent, and Aaron Sidford. Towards optimal running times for optimal transport. CoRR, abs/1810.07717, 2018.
[BSW19] Digvijay Boob, Saurabh Sawlani, and Di Wang. Faster width-dependent algorithm for mixed packing and covering lps. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, pages 15253-15262, 2019.
[CDG19] Yu Cheng, Ilias Diakonikolas, and Rong Ge. High-dimensional robust mean estimation in nearly-linear time. In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019, pages 2755-2771. SIAM, 2019.
[CDST19] Yair Carmon, John C. Duchi, Aaron Sidford, and Kevin Tian. A rank-1 sketch for matrix multiplicative weights. In Conference on Learning Theory, COLT 2019, volume 99 of Proceedings of Machine Learning Research, pages 589–623. PMLR, 2019.
[CG18] Yu Cheng and Rong Ge. Non-convex matrix completion against a semi-random adversary. In Conference On Learning Theory, COLT 2018, volume 75 of Proceedings of Machine Learning Research, pages 1362–1394. PMLR, 2018.
$\left[\mathrm{CKL}^{+}22\right]$  Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, and Sushant Sachdeva. Maximum flow and minimum-cost flow in almost-linear time. In 63rd IEEE Annual Symposium on Foundations of Computer Science, FOCS 2022, pages 612-623. IEEE, 2022.