A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method

10 December 2012

Papers citing "A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method"

6 / 6 papers shown

Title
Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization Benjamin Grimmer Danlin Li 74 6 0 31 Dec 2024
Simplifying Deep Temporal Difference Learning Matteo Gallici Mattie Fellows Benjamin Ellis B. Pou Ivan Masmitja Jakob Foerster Mario Martin OffRL 101 25 0 05 Jul 2024
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality Ziyang Wei Wanrong Zhu Wei Biao Wu 59 5 0 13 Jul 2023
Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems M. Yemini Angelia Nedić Andrea J. Goldsmith Stephanie Gil 65 8 0 05 Dec 2022
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 146 574 0 08 Dec 2012
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization Alexander Rakhlin Ohad Shamir Karthik Sridharan 143 767 0 26 Sep 2011