[CMY20] Yeshwanth Cherapanamjeri, Sidhanth Mohanty, and Morris Yau. List decodable mean estimation in nearly linear time. In 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020, Durham, NC, USA, November 16-19, 2020, pages 141-148, 2020.
[DHS11] John C. Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121-2159, 2011.
$\left[\mathrm{DKP}^{+}17\right]$  David Durfee, Rasmus Kyng, John Peebles, Anup B. Rao, and Sushant Sachdeva. Sampling random spanning trees faster than matrix multiplication. In Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017, Montreal, QC, Canada, June 19-23, 2017, pages 730-742, 2017.
[DS08] Samuel I. Daitch and Daniel A. Spielman. Faster approximate lossy generalized flow via interior point algorithms. In Proceedings of the 40th Annual ACM Symposium on Theory of Computing, Victoria, British Columbia, Canada, May 17-20, 2008, pages 451-460, 2008.
[DWZ23] Ran Duan, Hongxun Wu, and Renfei Zhou. Faster matrix multiplication via asymmetric hashing. In 64th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2023. IEEE, 2023.
[FK00] Uriel Feige and Robert Krauthgamer. Finding and certifying a large hidden clique in a semirandom graph. *Random Struct. Algorithms*, 16(2):195–208, 2000.
[FK01] Uriel Feige and Joe Kilian. Heuristics for semirandom graph problems. J. Comput. Syst. Sci., 63(4):639-671, 2001.
[FLS+17] Shaun Fallat, Steffen Lauritzen, Kayvan Sadeghi, Caroline Uhler, Nanny Wermuth, and Piotr Zwiernik. Total positivity in markov structures. Ann. Statist., 45(3):1152-1184, 06 2017.
[FS55] G. E. Forsythe and E. G. Straus. On best conditioned matrices. Proceedings of the American Mathematical Society, 6(3):340-345, 1955.
[Gal14] François Le Gall. Powers of tensors and fast matrix multiplication. In International Symposium on Symbolic and Algebraic Computation, ISSAC '14, Kobe, Japan, July 23-25, 2014, pages 296-303. ACM, 2014.
[GHM15] Dan Garber, Elad Hazan, and Tengyu Ma. Online learning of eigenvectors. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, pages 560-568, 2015.
[GM12] Bernd Gartner and Jiri Matousek. Approximation Algorithms and Semidefinite Programming. Springer, 2012.
[GO18] Ankit Garg and Rafael Oliveira. Recent progress on scaling algorithms and applications. Bulletin of EATCS, 2(125), 2018.
[GR89] A. Greenbaum and G. H. Rodrigue. Optimal preconditioners of a given sparsity pattern. BIT Numerical Mathematics, 29(4):610-634, 1989.
[GW95] Michel X. Goemans and David P. Williamson. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM, 42(6):1115-1145, 1995.
[HJS+22] Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao, and Ruizhe Zhang. Solving SDP faster: A robust IPM framework and efficient implementation. In 63rd IEEE Annual Symposium on Foundations of Computer Science, FOCS 2022, Denver, CO, USA, October 31 - November 3, 2022, pages 233-244. IEEE, 2022.
[HMMT18] Jeremy G. Hoskins, Cameron Musco, Christopher Musco, and Charalampos E. Tsourakakis. Learning networks from random walk-based node similarities. CoRR, abs/1801.07386, 2018.