报告人简介：

张扬，现为加拿大曼尼托巴大学数学系教授、加拿大国家自然科学和工程基金委计算机学部会评专家，曾担任Springer旗下Journal of System Science and Complexity期刊编委。其主要研究领域为矩阵代数、计算机代数、符号计算及其应用，在国际期刊《Automatica》等刊物上发表SCI论文60多篇，在Springer出版社出版专著1部，研究工作得到加拿大国家自然科学和工程基金委员会的连续资助。

报告摘要：

In this talk, we first introduce the rank problem of quaternion tensors. Although it is NP-hard, we obtain the upper bounds for certain lower order tensors.
The tensor completion has been widely used in the fields of computer vision and image processing. In the second part of this talk, a new completion method for the quaternion tensor is explored via the QR decomposition and the definition of novel quaternion tensor norm, which can well balance the model generalization ability and efficiency, and the performance of the completion method has been substantially improved.