16 citations · 28 across the 13 of their papers we have counts for
13 papers
Event-triggered Dual Gradient Tracking for Distributed Resource Allocation
Xiayan Xu, Xiaomeng Chen, Dawei Shi +1
High communication costs create a major bottleneck for distributed resource allocation over unbalanced directed networks. Conventional dual gradient tracking methods, while effecti…
Communication-efficient and Differentially-private Distributed Nash Equilibrium Seeking with Linear Convergence
Xiaomeng Chen, Wei Huo, Kemi Ding +2
The distributed computation of a Nash equilibrium (NE) for non-cooperative games is gaining increased attention recently. Due to the nature of distributed systems, privacy and comm…
Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games
Wei Huo, Xiaomeng Chen, Kemi Ding +2
This paper explores distributed aggregative games in multi-agent systems. Current methods for finding distributed Nash equilibrium require players to send original messages to thei…
Distributed Average Consensus via Noisy and Non-Coherent Over-the-Air Aggregation
Huiwen Yang, Xiaomeng Chen, Lingying Huang +2
Over-the-air aggregation has attracted widespread attention for its potential advantages in task-oriented applications, such as distributed sensing, learning, and consensus. In thi…
Differentially Private Dual Gradient Tracking for Distributed Resource Allocation
Wei Huo, Xiaomeng Chen, Lingying Huang +2
This paper investigates privacy issues in distributed resource allocation over directed networks, where each agent holds a private cost function and optimizes its decision subject…
An Efficient Distributed Nash Equilibrium Seeking with Compressed and Event-triggered Communication
Xiaomeng Chen, Wei Huo, Yuchi Wu +2
Distributed Nash equilibrium (NE) seeking problems for networked games have been widely investigated in recent years. Despite the increasing attention, communication expenditure is…
A Privacy-Preserving Finite-Time Push-Sum based Gradient Method for Distributed Optimization over Digraphs
Xiaomeng Chen, Wei Jiang, Themistoklis Charalambous +1
This paper addresses the problem of distributed optimization, where a network of agents represented as a directed graph (digraph) aims to collaboratively minimize the sum of their…
Tight Upper Bounds on the Error Probability of Spinal Codes over Fading Channels
Xiaomeng Chen, Aimin Li, Shaohua Wu
Spinal codes, a family of rateless codes introduced in 2011, have been proved to achieve Shannon capacity over both the additive white Gaussian noise (AWGN) channel and the binary…
Linear Convergent Distributed Nash Equilibrium Seeking with Compression
Xiaomeng Chen, Yuchi Wu, Xinlei Yi +2
Information compression techniques are majorly employed to address the concern of reducing communication cost over peer-to-peer links. In this paper, we investigate distributed Nas…
Improving Primal Heuristics for Mixed Integer Programming Problems based on Problem Reduction: A Learning-based Approach
Lingying Huang, Xiaomeng Chen, Wei Huo +4
In this paper, we propose a Bi-layer Predictionbased Reduction Branch (BP-RB) framework to speed up the process of finding a high-quality feasible solution for Mixed Integer Progra…
Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends
Lingying Huang, Xiaomeng Chen, Wei Huo +4
In this paper, we surveyed the existing literature studying different approaches and algorithms for the four critical components in the general branch and bound (B&B) algorithm, na…
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition
Xiaomeng Chen, Lingying Huang, Lidong He +2
In this paper, we study the problem of consensus-based distributed optimization where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents' c…
Privacy-Preserving Push-sum Average Consensus via State Decomposition
Xiaomeng Chen, Lingying Huang, Kemi Ding +2
Average consensus is extensively used in distributed networks for computation and control, where all the agents constantly communicate with each other and update their states in or…