244 citations · 883 across the 23 of their papers we have counts for
6 papers · 1 filter
Variance-Reduced Decentralized Stochastic Optimization with Accelerated Convergence
Ran Xin, Usman A. Khan, Soummya Kar
This paper describes a novel algorithmic framework to minimize a finite-sum of functions available over a network of nodes. The proposed framework, that we call~\GTVR, is stochasti…
Variance-Reduced Decentralized Stochastic Optimization with Gradient Tracking -- Part II: GT-SVRG
Ran Xin, Usman A. Khan, Soummya Kar
Decentralized stochastic optimization has recently benefited from gradient tracking methods \cite{DSGT_Pu,DSGT_Xin} providing efficient solutions for large-scale empirical risk min…
Variance-Reduced Decentralized Stochastic Optimization with Gradient Tracking--Part I: GT-SAGA
Ran Xin, Usman A. Khan, Soummya Kar
In this paper, we study decentralized empirical risk minimization problems, where the goal is to minimize a finite-sum of smooth and strongly-convex functions available over a netw…
An introduction to decentralized stochastic optimization with gradient tracking
Ran Xin, Soummya Kar, Usman A. Khan
Decentralized solutions to finite-sum minimization are of significant importance in many signal processing, control, and machine learning applications. In such settings, the data i…
Distributed stochastic optimization with gradient tracking over strongly-connected networks
Ran Xin, Anit Kumar Sahu, Usman A. Khan +1
In this paper, we study distributed stochastic optimization to minimize a sum of smooth and strongly-convex local cost functions over a network of agents, communicating over a stro…
Distributed Nesterov gradient methods over arbitrary graphs
Ran Xin, Dusan Jakovetic, Usman A. Khan
In this letter, we introduce a distributed Nesterov method, termed as , that does not require doubly-stochastic weight matrices. Instead, the implementation is based…