Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms

7 March 2017

Papers citing "Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms"

5 / 5 papers shown

Title
Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approach Colin Dirren Mattia Bianchi Panagiotis D. Grontas John Lygeros Florian Dorfler 41 0 0 18 Oct 2024
Decentralized Stochastic Variance Reduced Extragradient Method Luo Luo Haishan Ye 29 7 0 01 Feb 2022
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning Luofeng Liao Zuyue Fu Zhuoran Yang Yixin Wang Mladen Kolar Zhaoran Wang OffRL 23 35 0 19 Feb 2021
Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity S. Du Wei Hu 68 120 0 05 Feb 2018
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014