报告人简介：Minyue Fu received the B.Sc. degree in electrical engineering from the University of Science and Technology of China, Hefei, China, in 1982, and the M.S. and Ph.D. degrees in electrical engineering from the University of Wisconsin-Madison, Madison, WI, USA. in 1983 and 1987, respectively. From 1987 to 1989, he was an Assistant Professor in the Department of Electrical and Computer Engineering, Wayne State University, USA. He joined the Department of Electrical and Computer Engineering at the University of Newcastle, Australia, in 1989, where he is a Chair Professor of Electrical Engineering. He has been Visiting Professors at the University of Iowa, USA, Nanyang Technological University, Singapore and Tokyo University, Tokyo, Japan. He has held ChangJiang Visiting Professorship at Shandong University, Jinan, China, and Distinguished Professorship at Zhejiang University and Guangdong University of Technology, China. He has been an Associate Editor for the IEEE Transactions on Automatic Control, Automatica, IEEE Transactions on Signal Processing, and the Journal of Optimization and Engineering. His main research interests include control systems, signal processing, and communications. His current research projects include networked control systems, distributed control, smart electricity networks, and super-precision positioning control systems.
Prof. Minyue Fu is a Fellow of IEEE, Fellow of Institute of Engineers Australia, and Fellow of Chinese Association of Automation.
In this talk, we present a new distributed algorithm for solving linear systems associated with a sparse graph under a generalized diagonal dominance assumption. The algorithm runs iteratively on each node of the graph, with low complexities on local information exchange between neighboring nodes, local computation and local storage. For an acyclic graph under the condition of diagonal dominance, the algorithm is shown to converge to the correct solution in a finite number of iterations, equaling the diameter of the graph. For a loopy graph, the algorithm is shown to converge to the correct solution asymptotically. Simulations verify that the proposed algorithm significantly outperforms the classical Jacobi method and a recent distributed linear system solver based on average consensus and orthogonal projection. If time permits, we will discuss variants of this algorithm for solving two related problems: average consensus and distributed least squares.