7 citations · 17 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
Push-SAGA: A decentralized stochastic algorithm with variance reduction over directed graphs
Muhammad I. Qureshi, Ran Xin, Soummya Kar +1
In this paper, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes. Push-SAGA combines node-level varia…
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…
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 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…