244 citations · 883 across the 23 of their papers we have counts for
24 papers
Understanding the Influence of Digraphs on Decentralized Optimization: Effective Metrics, Lower Bound, and Optimal Algorithm
Liyuan Liang, Xinmeng Huang, Ran Xin +1
This paper investigates the influence of directed networks on decentralized stochastic non-convex optimization associated with column-stochastic mixing matrices. Surprisingly, we f…
Variance reduced stochastic optimization over directed graphs with row and column stochastic weights
Muhammad I. Qureshi, Ran Xin, Soummya Kar +1
This paper proposes AB-SAGA, a first-order distributed stochastic optimization method to minimize a finite-sum of smooth and strongly convex functions distributed over an arbitrary…
A Stochastic Proximal Gradient Framework for Decentralized Non-Convex Composite Optimization: Topology-Independent Sample Complexity and Communication Efficiency
Ran Xin, Subhro Das, Usman A. Khan +1
Decentralized optimization is a promising parallel computation paradigm for large-scale data analytics and machine learning problems defined over a network of nodes. This paper is…
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
Ran Xin, Usman A. Khan, Soummya Kar
This paper considers decentralized stochastic optimization over a network of nodes, where each node possesses a smooth non-convex local cost function and the goal of the networ…
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…