arXiv:2604.13870v2 Announce Type: replace-cross
Abstract: We consider the well-studied setting of minimizing a convex Lipschitz function using either gradient descent (GD) or its stochastic variant (...
By Guy Kornowski, Ohad Shamir
arXiv:2606. 24879v1 Announce Type: cross Abstract: We study the last iterate of the stochastic subgradient method for one-dimensional convex Lipschitz objectives.
By Guglielmo Beretta, Tommaso Cesari, Roberto Colomboni, Andrea Paudice
arXiv:2609.08537v1 Announce Type: cross
Abstract: We study the time-uniform convergence of the raw iterate of standard stochastic gradient descent (SGD) for unconstrained smooth convex objectives. We...
By Ruijie Li, Kang Chen, Tianyu Wang
arXiv:2610.19444v1 Announce Type: cross
Abstract: Random reshuffling stochastic gradient descent (RR-SGD) is widely used for finite-sum optimization in machine learning. For convex $L$-smooth compone...
By Zijun Chen, Zihan Zhang
arXiv:2606. 01764v1 Announce Type: cross Abstract: We revisit the convergence guarantees of the Extragradient (EG) method for unconstrained biaffine min-max optimization.
By Yue Wu, Weiqiang Zheng, Yang Cai, Haipeng Luo
arXiv:2504. 09951v2 Announce Type: replace-cross Abstract: We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed to grow as fast as the squared norm of the optimization variable.
By Ahmet Alacaoglu, Yura Malitsky, Stephen J. Wright