activity
20202022
most citedComposition and Application of Current Advanced Driving Assistance System: A Review

5 citations · 11 across the 5 of their papers we have counts for

collaborators

6 papers

math.OC2022

An Optimal Distributed Algorithm with Operator Extrapolation for Stochastic Aggregative Games

Tongyu Wang, Peng Yi, Jie Chen

This work studies Nash equilibrium seeking for a class of stochastic aggregative games, where each player has an expectation-valued objective function depending on its local strate…

math.OC2022

Distributed coordination for seeking the optimal Nash equilibrium of aggregative games

Xiaoyu Ma, Jinlong Lei, Peng Yi +1

This paper aims to design a distributed coordination algorithm for solving a multi-agent decision problem with a hierarchical structure. The primary goal is to search the Nash equi…

math.OC20213 cited

No-regret distributed learning in subnetwork zero-sum games

Shijie Huang, Jinlong Lei, Yiguang Hong +2

In this paper, we consider a distributed learning problem in a subnetwork zero-sum game, where agents are competing in different subnetworks. These agents are connected through tim…

cs.AI20215 cited

Composition and Application of Current Advanced Driving Assistance System: A Review

Xinran Li, Kuo-Yi Lin, Min Meng +4

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehic…

math.OC2021

Decentralized Online Learning for Noncooperative Games in Dynamic Environments

Min Meng, Xiuxian Li, Yiguang Hong +2

Decentralized online learning for seeking generalized Nash equilibrium (GNE) of noncooperative games in dynamic environments is studied in this paper. Each player aims at selfishly…

math.OC20203 cited

Linearly Convergent Algorithm with Variance Reduction for Distributed Stochastic Optimization

Jinlong Lei, Peng Yi, Jie Chen +1

This paper considers a distributed stochastic strongly convex optimization, where agents connected over a network aim to cooperatively minimize the average of all agents' local cos…