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发布时间:2020-11-12文章来源:华南理工大学数学学院浏览次数:687

报告题目:Orthogonal Tensor Decompositions

报  告  人:曾超  博士(香港大学数学科学学院)

报告时间:20201113日(星期五)下午15:00-16:00                

报告地点:腾讯会议,会议号: 751 219 864  

                 会议链接: https://meeting.tencent.com/s/MGTRM6foGkbV

邀  请  : 朱远鹏  博士

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数学学院

20201112

 

报告摘要:

The orthogonal decomposition factorizes a tensor into a sum of an orthogonal list of rank-one tensors. Unlike tensor rank, a subtensor may have a larger orthogonal tensor rank than the whole tensor, and orthogonal tensor rank is lower semicontinuous. The lower semicontinuity guarantees the existence of low orthogonal rank approximation. To fit the orthogonal decomposition, we propose an algorithm based on the augmented Lagrangian method and guarantee the orthogonality by a novel orthogonalization procedure. Numerical experiments show that the proposed method has great advantages over the existing methods for strongly orthogonal decompositions in terms of the approximation error.

 

报告人简介:

曾超,于2010年和2016年获中国科大学士和博士学位,之后在南开大学,香港浸会大学,香港大学从事博士后工作。研究方向包括数值代数,数值优化,多元样条,目前感兴趣的课题为张量计算,图像处理。已在SIAM J. Numer. Anal., SIAM J. Imaging Sci.等期刊发表论文十余篇。