学术报告第201820期

报告题目： Distributed Finite-Time Optimization

报 告 人： Tao Yang

报告时间：2018年5月23日 上午9:30—10:30

报告地点：南一楼中311

报告摘要：

In this presentation, we consider the distributed optimization problem of multi-agent systems. The objective is to minimize the global objective function, which is the sum of local objective functions, by using local communication and local computation. We develop a distributed proportional-integral (PI) algorithm, based on the information received from its neighboring agents through the communication network and the gradient of its own objective function. For the case of quadratic objective functions, we establish sufficient conditions on the gain parameters under which the algorithm exponentially converges to the unique global minimizer. Moreover, we equip the proposed algorithm with a decentralized algorithm, which enables an arbitrarily chosen agent to compute the exact global minimizer within a finite number of time steps, using its own states observed over a successive time steps. Finally, the theoretical results are illustrated by numerical simulations.

报告人简介：

Tao Yang received the B.S. degree in Computer Science from Harbin University of Science and Technology in 2003, the M.S. degree with distinction in control engineering from City University, London in 2004, and the Ph.D. degree in electrical engineering from Washington State University in 2012. Between August 2012 and August 2014, he was an ACCESS Post-Doctoral Researcher with the ACCESS Linnaeus Centre, Royal Institute of Technology, Sweden. He is currently an Assistant Professor at the Department of Electrical Engineering, University of North Texas (UNT). Prior to joining the UNT, he was a Scientist/Engineer II with Energy & Environmental Directorate, Pacific Northwest National Laboratory. His research interests include distributed control and optimization with applications to power systems and transportation systems, Cyber Physical Systems, machine learning, networked control systems, and multi-agent systems.