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学术报告:Low-Rank Matrix Optimization: Theory and Algorithms

  报告时间

  8月20日:9:00—12:00 ,14:00—16:00

  8月22日:9:00—12:00 ,14:00—16:00

  8月23日:9:00—12:00 ,14:00—16:00

  报告地点:北辰校区理学院西教五416

  报告题目Low-Rank Matrix Optimization: Theory and Algorithms

  报告嘉宾 Qi Houduo教授

 

  报告简介

  One of the purposes in machine learning is to reveal and explore the structure among data. When data is put in arrays, they tend to be of low rank. Therefore, low-rank matrix optimization has become increasingly important in machine learning algorithms. This series of talks aims to provide a selective overview of the topic. We pay particular attention to efficient algorithms and try to elaborate on important optimization techniques that are uniquely related to low-rank optimization. The principle in guiding our selection of the material is the speed and solution quality of the resulting algorithms. After this course, one is expected to appreciate the difficulty of the problem, what works and what do not, and more importantly we are left for further exploration.

  We will focus on the following (selective) aspects of the topic:

  Low-rank matrix optimization: motivating examples (from Principle33 Component Analysis to Low-rank and sparse optimization, Low-rank Hankel matrix optimization for spectrally sparse optimization)

  Penalty methods (the need for regularization, proximal operators, DC penalties)

  Methods of Alternating Projections (global and linear convergence, and its penalized version)

  A show-case example: outlier detection via low-rank EDM (Euclidean Distance Matrix) optimization.

  嘉宾简介

  Qi Houduo教授(主页:http://www.personal.soton.ac.uk/hdqi)为英国南安普敦大学教授,博士生导师。1990年毕业于北京大学统计学专业,1993年获曲阜师范大学硕士学位, 1996年中国科学研究院数学与系统科学研究院应用数学研究所博士毕业。曾在香港理工大学、新南威尔士大学等做博士后研究,获澳大利亚研究委员会(ARC)资助,以及ARC和享有全球盛誉的Queen Elizabeth II Fellowship奖励。研究方向有:约束优化、矩阵优化、变分不等式、数值分析等。在国际顶级期刊SIAM on Optimization, Mathematical Programming 等杂志发表高水平研究论文十余篇。

 

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