报告人:李庆娜 北京理工大学副教授

报告时间:11月23日14:30

报告地点:腾 讯 ID：648 168 751  密 码：无

主办单位:数学与统计学院

欢迎光临!

  报告摘要:Nonmetric multidimensional scaling(NMDS) is an important tool in data science to deal with dissimilarity data. In this talk, we will discuss the feasibility, numerical algorithms and the applications of NMDS, mainly based on the rank constraint Euclidean distance matrix model for NMDS. Despite the long history of NMSD, the feasibility issue of NMDS has been rarely discussed, which motivates us to take a systematical investigation of it. The challenges of designing efficient numerical algorithms for NMDS are the nonconvex constraint as well as the huge number of ordinal constraints. We will also discuss several numerical algorithms for NMDS, trying to tackling the two challenges in different ways. For applications, besides the traditional application such as sensor network localization, protein molecular conformation, we will also apply NMDS model to image ranking and posture sensing.

  李庆娜，湖南大学博士，中科院数学与系统科学研究院博士后,现任北京理工大学数学与统计学院副教授、博导。主要研究最优化理论与算法及应用,主持国家自然科学基金青年、面上项目等。中国运筹学会数学优化分会青年理事，北京运筹学会理事。