报告人：林宙辰
报告地点：数学与统计学院105室
报告时间：数学与统计学院105室
邀请人：
报告摘要：
Alternating direction method (ADM) is an intuitive yet powerful method for various convex optimization problems. However, the traditional ADM assumes that each subproblem is easy to solve and its convergence is proven only in the case of two blocks. Such limitations greatly prevent ADM from wider applications to more complex problems. So I generalize ADM in two aspects. First, I linearize the quadratic penalty term and update the penalty parameter adaptively, introducing linearized ADM (LADM) with adaptive penalty. Second, I modify LADM slightly to account for the multiple block case, introducing linearized ADM with parallel splitting and adaptive penalty. Deeper results are achieved in the scenario of machine learning and signal processing and the proposed algorithms fit for engineering use much better.
主讲人简介：
ZHOUCHEN LIN （林宙辰） received the Ph.D. degree in applied mathematics from Peking University in 2000. He is currently a Professor at Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Peking University. He is also a Chair Professor at Northeast Normal University and a guest professor at Beijing Jiaotong University. He was a guest professor at Shanghai Jiaotong University and Southeast University, and a guest researcher at Institute of Computing Technology, Chinese Academy of Sciences. Before joining Peking University, he was a lead researcher at Microsoft Research Asia. He is an associate editor of IEEE T. PAMI and IJCV, an area chair of CVPR 2014, and a Senior member of the IEEE. His webpage is: http://www.cis.pku.edu.cn/faculty/vision/zlin/zlin.htm
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