activity
20182021
most citedAccelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks

10 citations · 14 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG20211 cited

Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation

Xuezhong Lin, Jingyu Pan, Jinming Xu +2

As technology scaling is approaching the physical limit, lithography hotspot detection has become an essential task in design for manufacturability. While the deployment of pattern…

eess.SY20211 cited

Decentralized Coordination Between Economic Dispatch and Demand Response in Multi-Energy Systems

Zishun Liu, Shanying Zhu, Jinming Xu +1

In this paper, we investigate the problem of coordination between economic dispatch (ED) and demand response (DR) in multi-energy systems (MESs), aiming to improve the economic uti…

math.OC20192 cited

A Unified Contraction Analysis of a Class of Distributed Algorithms for Composite Optimization

Jinming Xu, Ying Sun, Ye Tian +1

We study distributed composite optimization over networks: agents minimize the sum of a smooth (strongly) convex function, the agents' sum-utility, plus a non-smooth (extended-valu…

math.OC201910 cited

Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks

Jinming Xu, Ye Tian, Ying Sun +1

This paper proposes a novel family of primal-dual-based distributed algorithms for smooth, convex, multi-agent optimization over networks that uses only gradient information and go…

math.OC2018

Push-Pull Gradient Methods for Distributed Optimization in Networks

Shi Pu, Wei Shi, Jinming Xu +1

In this paper, we focus on solving a distributed convex optimization problem in a network, where each agent has its own convex cost function and the goal is to minimize the sum of…

math.OC2018

A Push-Pull Gradient Method for Distributed Optimization in Networks

Shi Pu, Wei Shi, Jinming Xu +1

In this paper, we focus on solving a distributed convex optimization problem in a network, where each agent has its own convex cost function and the goal is to minimize the sum of…