报告人：凌晨（杭州电子科技大学）

时间：2022年06月28日16:00-

地址：理科楼LA106

摘要：This paper proposes two tensor product based approaches to tensor completion, which recovers missing entries of data represented by tensors. The proposed approaches are based on the tensor singular-value decomposition and the related tensor tubal rank, which are able to capture hidden information from tensors thanks to the balanced consideration of multi-dimensional low-rank features. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solutions. Some computational results for color images, multi-spectral images, videos, and magnetic resonance imaging data recovery show that our approaches perform better than some existing state-of-the-art tensor-based completion methods.

简介：杭州电子科技大学理学院教授，博士生导师。现任中国运筹学会数学规划分会副理事长、中国经济数学与管理数学研究会副理事长，曾任中国运筹学会理事、中国系统工程学会理事、浙江省数学会常务理事。近十年来，主持国家自科基金和浙江省自科基金各4项、其中省基金重点项目1项。在国内外重要刊物发表论文80余篇，多篇发表在Math. Program.、SIAM J. on Optim.和 SIAM J.on Matrix Anal.and Appl. 、COAP、JOTA、JOGO等。

邀请人：李声杰

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