Optimal algorithms for smooth and strongly convex distributed
optimization in networks

Optimal algorithms for smooth and strongly convex distributed optimization in networks

28 February 2017

Francis R. Bach

Sébastien Bubeck

Laurent Massoulié

Papers citing "Optimal algorithms for smooth and strongly convex distributed optimization in networks"

8 / 58 papers shown

Title
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives Hadrien Hendrikx Francis R. Bach Laurent Massoulié 18 42 0 05 Oct 2018
COLA: Decentralized Linear Learning Lie He An Bian Martin Jaggi 24 117 0 13 Aug 2018
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters Pavel Dvurechensky D. Dvinskikh Alexander Gasnikov César A. Uribe Angelia Nedić 20 104 0 11 Jun 2018
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication Zebang Shen Aryan Mokhtari Tengfei Zhou P. Zhao Hui Qian 25 56 0 25 May 2018
A Push-Pull Gradient Method for Distributed Optimization in Networks Shi Pu Wei Shi Jinming Xu A. Nedić 19 98 0 20 Mar 2018
Collaborative Deep Learning in Fixed Topology Networks Zhanhong Jiang Aditya Balu C. Hegde S. Sarkar FedML 29 179 0 23 Jun 2017
Improved Convergence Rates for Distributed Resource Allocation A. Nedić Alexander Olshevsky Wei Shi 19 75 0 16 Jun 2017
A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates Zhi Li W. Shi Ming Yan 12 223 0 25 Apr 2017