7 citations · 9 across the 3 of their papers we have counts for
7 papers
A general framework for decentralized optimization with first-order methods
Ran Xin, Shi Pu, Angelia Nedić +1
Decentralized optimization to minimize a finite sum of functions over a network of nodes has been a significant focus within control and signal processing research due to its natur…
S-ADDOPT: Decentralized stochastic first-order optimization over directed graphs
Muhammad I. Qureshi, Ran Xin, Soummya Kar +1
In this report, we study decentralized stochastic optimization to minimize a sum of smooth and strongly convex cost functions when the functions are distributed over a directed net…
Gradient tracking and variance reduction for decentralized optimization and machine learning
Ran Xin, Soummya Kar, Usman A. Khan
Decentralized methods to solve finite-sum minimization problems are important in many signal processing and machine learning tasks where the data is distributed over a network of n…
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…