主 讲 人:宋恩彬
讲座时间:2021年12月3日14:30-15:30
讲座地点:腾讯会议(会议号: 945 760 641; 密码: 1203)
讲座题目:Convergent Algorithms for Joint Dimension Assignment and Compression of Estimation in Distributed Multisensor Networks and Robust Hypothesis Testings
讲座内容:This talk mainly includes two parts. In the first part, we consider linear distributed estimation of an unknown random parameter distributed estimation of an unknown random parameter vector in a bandwidth-constrained multisensor network. Then we jointly determine the compression dimension of each sensor (referred to as dimension assignment) and design the corresponding compression matrix when the total compression dimensions is limited. Successive quadratic upper-bound minimization (SQUM), SQUM block coordinate descent (SQUM-BCD) and nuclear norm regularization (NNR) methods are developed to solve it approximately. Furthermore, we show that any accumulation point of the sequence generated by the SQUM method satisfies the Karush-Kuhn-Tucker conditions of the rank-constrained optimization problem. In the second part, we consider the popular minimax robust hypothesis testing problem. Then, we propose the gradient projection algorithm (GPA) and the hybrid gradient projection algorithm (HGPA) to solve the transformed problem. When the distance is chosen to be the Kullback-Leibler (KL) or α-divergence, the decision rule sequences generated by the GPA and the HGPA are respectively proved to converge weakly and strongly to the global minimizer under some mild conditions. To the best of our knowledge, these decision rules are the first to be guaranteed to globally converge towards the optimal solution.
主讲人简介:宋恩彬,四川大学教授,博士生导师,2007年于四川大学获得理学博士学位。 2007年7月~2009年11月在四川大学计算机学院从事博士后研究;2009年12月至2014年6月在四川大学数学学院任副教授。2010年4月~2011年5月,在美国明尼苏达大学电子与计算机工程学院从事博士后研究;2014年1月~2014年2月,在香港中文大学访问。2014年7月至今在四川大学数学学院任教授。曾获得2009年全国百篇优秀博士论文提名奖和2010年四川省科学技术进步奖一等奖。近几年,主要从事信息融合,传感器网络,信号处理,数学及优化理论在信息处理中的应用等方面的基础研究。近年来主持和参与多项包括国家自然科学基金和科技部重大专项等纵向和横向课题,发表所研究领域论文60余篇。
主办:西南交通大学数学学院信息与计算科学系