Distributed Optimization Using the Primal-Dual Method of Multipliers
Title: Distributed Optimization Using the Primal-Dual Method of Multipliers
时间地点：11月13日（周一）下午4:00-5:00；S309（思源楼）
Abstract:
Recently, distributed signal processing has drawn increasing attention due to the demand for big-data processing and popularity of various types of networks (e.g., wireless sensor networks, smart grid, decentralized active noise control). The basic idea is to have a number of computing units (e.g., a sensor or a mobile phone) collaborate with neighboring ones in a distributed way to complete a complex task. Different types of methods have been proposed in the literature to enable distributed signal processing, such as belief propagation and distributed convex/nonconvex optimization. In this talk we present two distributed optimization algorithms, namely the alternating direction method of multipliers (ADMM) and the primal-dual method of multipliers (PDMM). Relationship between PDMM and Kalman filter will be discussed.
Biography:
Dr. Guoqiang Zhang received the Bachelor degree from the University of Science and Technology of China (USTC) in 2003, a master of philosophy (M.Phil.) from the University of Hong Kong in 2006, and his PhD degree from the Royal Institute of Technology – KTH in 2010. He then worked as a post-doctoral researcher at Delft University of Technology full time until the end of 2014 and part time until the end of 2016. From 2015 to 2016, he worked as a senior researcher at Ercisson AB, Sweden. He is now working as a senior lecturer at the University of Technology Sydney. His current research interests include large scale distributed optimization, distributed control and signal processing in networks, and optimization in machine learning.
