报告人：衣鹏（同济大学电子与信息工程学院）

报告时间：2020年6月11日15:00-16:00

报告地点：南楼208教室

腾讯会议：516 948 450

摘要：We study network linear equations subject to digital communications with a finite data rate, where each node is associated with one equation from a system of linear equations. Each node holds a dynamic state and interacts with its neighbors through an undirected connected graph, where along each link the pair of nodes share information. Due to the data rate constraint, each node builds an encoder/decoder pair, with which it produces transmitted messages with a zooming-in finite-level uniform quantizer and also generates estimates of its neighbors' states from the received signals. We then propose a distributed quantized algorithm and show that when the network linear equations admit a unique solution, each node's state is driven to that solution exponentially fast. We further analyze the asymptotic rate of convergence and show that a larger number of quantization levels leads to a faster convergence rate although the rate is still fundamentally bounded by the inherent network structure and the linear equations. In addition, we establish a bound on the total number of communication bits required to obtain a solution with a prescribed accuracy. When a unique least-squares solution exists, we show that the algorithm can compute such a solution with a suitably selected time-varying step-size inherited from the encoder and zooming-in quantizer dynamics. In both cases, a minimal data rate is shown to be enough for guaranteeing the desired convergence when the algorithm parameters are properly chosen. These results ensure the applicability of various network linear equation solvers when peer-to-peer communication is digital.

个人简介：衣鹏，同济大学特聘研究院，博士生导师。分别于2011年和2016年从中国科学技术大学及中国科学院系统科学研究所获得本科和博士学位，2016年至2019年于多伦多大学和美国圣路易斯华盛顿大学从事博士后研究，入选中国科协“青年人才托举工程”及上海市青年科技英才“扬帆计划”，主要研究方向为多智能体系统的分布式优化与博弈。