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Xiuxian Li
Researcher at Tongji University
Publications - 84
Citations - 1127
Xiuxian Li is an academic researcher from Tongji University. The author has contributed to research in topics: Computer science & Convex function. The author has an hindex of 14, co-authored 64 publications receiving 553 citations. Previous affiliations of Xiuxian Li include Nanyang Technological University & City University of Hong Kong.
Papers
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Ultra-Wideband and Odometry-Based Cooperative Relative Localization With Application to Multi-UAV Formation Control
Kexin Guo,Xiuxian Li,Lihua Xie +2 more
TL;DR: Without any external infrastructures prepositioned, each agent cooperatively performs a consensus-based fusion, which fuses the obtained direct and indirect RL estimates, to generate the relative positions to its neighbors in real time despite the fact that some UAVs may not have direct range measurements to their neighbors.
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Distributed Online Convex Optimization With Time-Varying Coupled Inequality Constraints
TL;DR: This paper proves that the distributed online primal-dual dynamic mirror descent algorithm achieves sublinear dynamic regret and constraint violation if the accumulated dynamic variation of the optimal sequence also grows sublinearly, and achieves smaller bounds on the constraint violation.
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Distributed Online Convex Optimization with Time-Varying Coupled Inequality Constraints
TL;DR: In this paper, a distributed online primal-dual dynamic mirror descent algorithm is proposed to solve the problem, where the local cost, regularization, and constraint functions are held privately and revealed only after each time slot.
Journal ArticleDOI
Distributed Online Optimization for Multi-Agent Networks With Coupled Inequality Constraints
Xiuxian Li,Xinlei Yi,Lihua Xie +2 more
TL;DR: To address the distributed online optimization problem over a multi-agent network subject to local set constraints and coupled inequality constraints, a modified primal-dual algorithm is developed, which does not rest on any assumption on parameter boundedness and is applicable to unbalanced networks.
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l1-gain analysis and model reduction problem for Boolean control networks
TL;DR: The weighted l1-gain analysis and l1 model reduction problem for Boolean control networks are proposed and investigated via semi-tensor product method, and two examples, including the Boolean model for biochemical oscillators in the cell cycle, are displayed to show the feasibility of the theoretical results.