RSG: Beating Subgradient Method without Smoothness and Strong Convexity

9 December 2015

Papers citing "RSG: Beating Subgradient Method without Smoothness and Strong Convexity"

11 / 11 papers shown

Title
Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization Benjamin Grimmer Danlin Li 39 5 0 31 Dec 2024
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization Yan Huang Qihang Lin N. Street Stephen Seung-Yeob Baek FedML 22 9 0 21 Jul 2022
Minimax risk classifiers with 0-1 loss Santiago Mazuelas Mauricio Romero Peter Grünwald 22 6 0 17 Jan 2022
An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives Qi Qi Zhishuai Guo Yi Tian Xu R. L. Jin Tianbao Yang 31 44 0 17 Jun 2020
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization Baojian Zhou F. Chen Yiming Ying 21 7 0 09 May 2019
Adaptive Accelerated Gradient Converging Methods under Holderian Error Bound Condition Mingrui Liu Tianbao Yang 34 15 0 23 Nov 2016
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 139 1,198 0 16 Aug 2016
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates Tianbao Yang Qihang Lin Lijun Zhang 19 25 0 11 Aug 2016
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/ε)$ Yi Tian Xu Yan Yan Qihang Lin Tianbao Yang 52 25 0 13 Jul 2016
Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition Yi Tian Xu Qihang Lin Tianbao Yang 28 11 0 04 Jul 2016
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method Simon Lacoste-Julien Mark W. Schmidt Francis R. Bach 124 259 0 10 Dec 2012