报告人：曾铁勇 教授

时间：2021年5月7日（星期五）下午3:00-4:00

地点: T2-202 

报告摘要：

Phase retrieval is an important problem with significant physical and industrial applications. In this talk, we consider the case where the magnitude of the measurement of an underlying signal is corrupted by Gaussian noise. We introduce a convex augmentation approach for phase retrieval based on total variation regularization. In contrast to popular convex relaxation models like PhaseLift, our model can be efficiently solved by a modified semi-proximal alternating direction method of multipliers (sPADMM). The modified sPADMM is more general and flexible than the standard one, and its convergence is also established in this paper. Extensive numerical experiments are conducted to showcase the effectiveness of the proposed method. 

讲者简介： 

曾铁勇，博士，香港中文大学数学人工智能中心主任，于2000年本科毕业于北京大学，2007年巴黎第十三大学获得博士学位。主要研究领域包括数据科学，优化理论，图像处理，反问题等。在优化、图像处理、反问题的国际一流杂志SIAM Journal on Imaging Sciences，SIAM Journal on Scientific Computing，International Journal of Computer Vision，Journal of Scientific Computing，IEEE PAMI, IEEE TNNLS，IEEE Transactions on Image Processing，Pattern Recognition，Journal of Mathematical Imaging and Vision等发表过近百篇SCI论文。