报告人：刘春光（暨南大学）

时  间：10月30日上午10:00

地  点：腾讯会议ID：305 588 520（无密码）

内容摘要:

In this talk, we review some recent progress on sparse time-frequency representation for studying trend and instantaneous frequency of nonlinear and non-stationary data. This method is inspired by the Empirical Mode Decomposition method (EMD) and the recently developed compressed (compressive) sensing theory. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions. This problem can be formulated as a nonlinear optimization problem. In order to solve this optimization problem, a nonlinear matching pursuit method is developed by generalizing the classical matching pursuit. One important advantage of this nonlinear matching pursuit method is it can be implemented very efficiently and is very stable to noise. Further, convergence analysis of this nonlinear matching pursuit method is carried out and the uniqueness of the decomposition is analyzed under certain scale separation assumptions. Finally, we present a two-level method which further improves this nonlinear matching pursuit method and makes it less sensitive to initial guess.

个人简介：

刘春光，暨南大学数学系副教授；1998年本科毕业于南开大学数学科学学院，2006年博士毕业于香港中文大学，师从吴恭孚教授。刘春光副教授主要从事最优化理论、多尺度数据分析的研究，取得了重要成果，在包括SIAM JOURNAL ON OPTIMIZATION, MATHEMATICAL PROGRAMMING,OPTIMIZATION等在内的重要国际刊物发表论文多篇。

联系人：陈东阳