Japan Journal of Industrial and Applied Mathematics

, Volume 35, Issue 3, pp 1163–1172 | Cite as

Majorization bounds for SVD

  • Zhongming TengEmail author
  • Xuansheng Wang
Original Paper Area 2


Given an approximating singular subspace of a matrix, in this paper, two kind of majorization type bounds on the singular value errors by the canonical angles between the singular subspaces and its approximations are obtained. From these results, based on the information about approximation accuracies of a pair of approximate singular subspaces, several bounds can be directly obtained to estimate how accurate the approximate singular values are. These results are helpful to understand how approximate singular values converge to the corresponding exact singular values in the projection subspace type algorithms.


Singular value Singular vector Majorization 

Mathematics Subject Classification

65F15 15A18 



The authors are grateful to the anonymous referees for their careful reading, useful comments, and suggestions for improving the presentation of this paper. The work of the first author is supported in part by National Natural Science Foundation of China NSFC-11601081 and the research fund for distinguished young scholars of Fujian Agriculture and Forestry University No. xjq201727. The work of the second author is supported in part by National Natural Science Foundation of China Grant NSFC-11601347, and the Shenzhen Infrastructure Project No. JCYJ20170306095959113.


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Copyright information

© The JJIAM Publishing Committee and Springer Japan KK, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer and Information ScienceFujian Agriculture and Forestry UniversityFuzhouPeople’s Republic of China
  2. 2.School of Software EngineeringShenzhen Institute of Information TechnologyShenzhenPeople’s Republic of China

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