7 citations · 10 across the 6 of their papers we have counts for
11 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…
Minimal Sufficient Conditions for Structural Observability/Controllability of Composite Networks via Kronecker Product
Mohammadreza Doostmohammadian, Usman A. Khan
In this paper, we consider composite networks formed from the Kronecker product of smaller networks. We find the observability and controllability properties of the product network…
On the Complexity of Minimum-Cost Networked Estimation of Self-Damped Dynamical Systems
Mohammadreza Doostmohammadian, Usman Khan
In this paper, we consider the optimal design of networked estimators to minimize the communication/measurement cost under the networked observability constraint. This problem is k…
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…