86 citations · 126 across the 36 of their papers we have counts for
18 papers · 1 filter
Optimal Design of Distributed Inexact Gradient Tracking: Exact Worst-case Convergence Rate and Explicit Parameters
Qiuchen Tian, Li Chai, Jinming Xu
This paper addresses the problem of designing distributed optimization algorithms. Different to existing methods that usually give sufficient conditions and conservative convergenc…
Optimal Parameter Design for DIGing on Minimizing Unweighted Sum of Squares
Qiuchen Tian, Li Chai, Jinming Xu
There is no general method for designing proper parameters to achieve faster convergence in distributed optimization algorithms. In this paper, we consider the distributed inexact…
Exact Worst-case Convergence Rates of Distributed Gradient Tracking Methods
Qiuchen Tian, Li Chai, Jinming Xu
Different from most existing literature in the analysis of distributed optimization algorithms that reports sufficient convergence conditions leading to a conservative convergence…
Adaptive Polyak Stepsize with Level-value Adjustment for Distributed Optimization
Chen Ouyang, Yongyang Xiong, Jinming Xu +2
Stepsize selection remains a critical challenge in the practical implementation of distributed optimization. Existing distributed algorithms often rely on restrictive prior knowled…
Heterogeneous Stochastic Momentum ADMM for Distributed Nonconvex Composite Optimization
Yangming Zhang, Yongyang Xiong, Jinming Xu +2
This paper investigates the distributed stochastic nonconvex and nonsmooth composite optimization problem. Existing stochastic typically rely on uniform step size strictly bounded…
Pareto-optimal Trade-offs Between Communication and Computation with Flexible Gradient Tracking
Yan Huang, Jinming Xu, Li Chai +2
This paper addresses distributed stochastic optimization problems under non-i.i.d. data, focusing on the inherent trade-offs between communication and computational efficiency. To…
An Optimistic Gradient Tracking Method for Distributed Minimax Optimization
Yan Huang, Jinming Xu, Jiming Chen +1
This paper studies the distributed minimax optimization problem over networks. To enhance convergence performance, we propose a distributed optimistic gradient tracking method, ter…
Hessian-Free Distributed Bilevel Optimization via Penalization with Time-Scale Separation
Youcheng Niu, Jinming Xu, Ying Sun +2
This paper considers a class of distributed bilevel optimization (DBO) problems with a coupled inner-level subproblem. Existing approaches typically rely on hypergradient estimatio…
Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
Yan Huang, Xiang Li, Yipeng Shen +2
In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive steps…
Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis
Youcheng Niu, Jinming Xu, Ying Sun +2
This paper consider solving a class of nonconvex-strongly-convex distributed stochastic bilevel optimization (DSBO) problems with personalized inner-level objectives. Most existing…
On the Computation-Communication Trade-Off with A Flexible Gradient Tracking Approach
Yan Huang, Jinming Xu
We propose a flexible gradient tracking approach with adjustable computation and communication steps for solving distributed stochastic optimization problem over networks. The prop…
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
Yan Huang, Ying Sun, Zehan Zhu +2
We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenario…
Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis
Jinming Xu, Ye Tian, Ying Sun +1
We study distributed composite optimization over networks: agents minimize a sum of smooth (strongly) convex functions, the agents' sum-utility, plus a nonsmooth (extended-valued)…
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…
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…
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…
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…
A Bregman Splitting Algorithm for Distributed Optimization over Networks
Jinming Xu, Shanying Zhu, Yeng Chai Soh +1
We consider distributed optimization problems in which a group of agents are to collaboratively seek the global optimum through peer-to-peer communication networks. The problem ari…