7 citations · 17 across the 11 of their papers we have counts for
6 papers · 1 filter
A fast randomized incremental gradient method for decentralized non-convex optimization
Ran Xin, Usman A. Khan, Soummya Kar
We study decentralized non-convex finite-sum minimization problems described over a network of nodes, where each node possesses a local batch of data samples. In this context, we a…
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…
An improved convergence analysis for decentralized online stochastic non-convex optimization
Ran Xin, Usman A. Khan, Soummya Kar
In this paper, we study decentralized online stochastic non-convex optimization over a network of nodes. Integrating a technique called gradient tracking in decentralized stochasti…
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…