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Local adapt-then-combine algorithms for distributed nonsmooth optimization: Achieving provable communication acceleration
Luyao Guo, Xinli Shi, Wenying Xu +1
This paper is concerned with the distributed composite optimization problem over networks, where agents aim to minimize a sum of local smooth components and a common nonsmooth term…
Perturbed Proximal Gradient ADMM for Nonconvex Composite Optimization
Yuan Zhou, Xinli Shi, Luyao Guo +2
This paper proposes a Perturbed Proximal Gradient ADMM (PPG-ADMM) framework for solving general nonconvex composite optimization problems, where the objective function consists of…
Distributed Online Randomized Gradient-Free Optimization with Compressed Communication
Longkang Zhu, Xinli Shi, Xiangping Xu +2
This paper addresses two fundamental challenges in distributed online convex optimization: communication efficiency and optimization under limited feedback. We propose a unified fr…
A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization
Luyao Guo, Luqing Wang, Xinli Shi +1
Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform sever…
Distributed Online Randomized Gradient-Free optimization with Compressed Communication
Longkang Zhu, Xinli Shi, Xiangping Xu +1
This paper addresses two fundamental challenges in distributed online convex optimization: communication efficiency and optimization under limited feedback. We propose Online Compr…
Differentially Private Decentralized Optimization with Relay Communication
Luqing Wang, Luyao Guo, Shaofu Yang +1
Security concerns in large-scale networked environments are becoming increasingly critical. To further improve the algorithm security from the design perspective of decentralized o…