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Proximal Method and Nesterov’s Acceleration Method for Composite Optimization
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报告题目:Proximal Method and Nesterov’s Acceleration Method for Composite Optimization
报告人:徐洪坤 教授  杭州电子科技大学

邀请人:刘三阳 教授
报告时间:2018年1月17日(周三)下午4:00
报告地点:信远楼II206数统院报告厅
报告人简介:徐洪坤于1988年获西安交通大学博士学位,目前为杭州电子科技大学特聘教授。曾任华东理工大学讲师、副教授;西班牙塞维利亚大学访问教授;加拿大达尔豪斯大学博士后研究员;南非夸祖鲁纳塔尔大学副教授、教授、资深教授;台湾中山大学西湾讲席教授;天津市特聘讲座教授等。2004年荣获南非数学学会杰出研究奖和教育部自然科学二等奖(与徐宗本、蒋耀林共同获得)。曾任台湾中山大学应用数学系主任和理学院院长。2005年当选南非科学院院士,2012年当选发展中国家科学院(现更名为世界科学院)院士,2014年入选浙江省“千人计划”,2014年起入选科睿唯安(原汤森路透)全球高倍引学者。已发表论文200余篇。现(曾)担任近10种SCI数学杂志编委。50余次国际学术会议邀请报告。主要研究兴趣包括:非线性泛函分析、最优化理论和算法、巴拿赫空间几何理论,非线性映像迭代方法,反问题及其正则化方法,金融数学等。
报告摘要:In many applied areas such as compressed sensing and machine learning, it is commonly needed to solve a composite optimization problem where the objective function is a sum of two (or more) component functions, one of which may have a simple structure and plays the role of regularization. To solve such a composite optimization problem, the proximal algorithm is prevailingly applied. This algorithm has however a slow sublinear rate of convergence. Yu. Nesterov (1983) initiated an acceleration method which can speed up the convergence rate of the
gradient-projection algorithm from O(1/k) to O(1/k2). This is extended to the case of composite optimization by Beck and Teboulle in 2009. Since then Nesterov’s acceleration has been paid a lot of attention by researchers from various areas including optimization, engineering, computer science, statistics, and so on.In this talk, we will briefly introduce the results on the study of Nesterov’s acceleration technique and its application in big data problems and connection with the asymptotics of certain dynamic systems.

 

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