5 papers · 1 filter
Distributed Stochastic Momentum Tracking with Local Updates: Achieving Optimal Communication and Iteration Complexities
Kun Huang, Shi Pu
We propose Local Momentum Tracking (LMT), a novel distributed stochastic gradient method for solving distributed optimization problems over networks. To reduce communication overhe…
Decentralized Min-Max Optimization with Gradient Tracking
Runze You, Kun Huang, Shi Pu
This paper presents a novel distributed formulation of the min-max optimization problem. Such a formulation enables enhanced flexibility among agents when optimizing their maximiza…
Distributed Normal Map-based Stochastic Proximal Gradient Methods over Networks
Kun Huang, Shi Pu, Angelia Nedić
Consider agents connected over a network collaborating to minimize the average of their local cost functions combined with a common nonsmooth function. This paper introduces a…
A Compressed Gradient Tracking Method for Decentralized Optimization with Linear Convergence
Yiwei Liao, Zhuorui Li, Kun Huang +1
Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minim…
Improving the Transient Times for Distributed Stochastic Gradient Methods
Kun Huang, Shi Pu
We consider the distributed optimization problem where agents each possessing a local cost function, collaboratively minimize the average of the cost functions over a conne…