activity
20172022
most citedGradient tracking and variance reduction for decentralized optimization and machine learning

7 citations · 17 across the 11 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

eess.SY2019

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…

math.OC2019

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…

eess.SY2019

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…

math.OC2019

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…

eess.SP2019

Digital synthesis of multistage etalons for enhancing the FSR

Faiza Iftikhar, Usman Khan, M. Imran Cheema

Fabry-Perot fiber etalons (FPE) built from three or more reflectors are attractive for a variety of applications including communications and sensing. For accelerating a research a…

math.OC2019

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…