5 citations · 19 across the 7 of their papers we have counts for
6 papers · 1 filter
On the Performance of Gradient Tracking with Local Updates
Edward Duc Hien Nguyen, Sulaiman A. Alghunaim, Kun Yuan +1
We study the decentralized optimization problem where a network of agents seeks to minimize the average of a set of heterogeneous non-convex cost functions distributedly. State…
Can Primal Methods Outperform Primal-dual Methods in Decentralized Dynamic Optimization?
Kun Yuan, Wei Xu, Qing Ling
In this paper, we consider the decentralized dynamic optimization problem defined over a multi-agent network. Each agent possesses a time-varying local objective function, and all…
Decentralized Proximal Gradient Algorithms with Linear Convergence Rates
Sulaiman A. Alghunaim, Ernest K. Ryu, Kun Yuan +1
This work studies a class of non-smooth decentralized multi-agent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a c…
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
Sulaiman A. Alghunaim, Kun Yuan, Ali H. Sayed
Decentralized optimization is a powerful paradigm that finds applications in engineering and learning design. This work studies decentralized composite optimization problems with n…
A Proximal Diffusion Strategy for Multi-Agent Optimization with Sparse Affine Constraints
Sulaiman A. Alghunaim, Kun Yuan, Ali H. Sayed
This work develops a proximal primal-dual decentralized strategy for multi-agent optimization problems that involve multiple coupled affine constraints, where each constraint may i…
Online Dual Coordinate Ascent Learning
Bicheng Ying, Kun Yuan, Ali H. Sayed
The stochastic dual coordinate-ascent (S-DCA) technique is a useful alternative to the traditional stochastic gradient-descent algorithm for solving large-scale optimization proble…