AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home AIMS Mathematics Article
PDF (249.1 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

A tighter M-eigenvalue localization set for partially symmetric tensors and its an application

College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, Guizhou, 550025, China
Show Author Information

Abstract

In this paper, a new M-eigenvalue inclusion set for a partially symmetric tensor is provided. It is proved that the new set is tighter than some existing M-eigenvalue inclusion sets. Based on the obtained results, an upper bound of the largest M-eigenvalue is given and a modified WQZ-algorithm is established which guarantees the generated converges to the largest M-eigenvalue of the tensor faster.

Keywords

partially symmetric tensorsM-eigenvalueslocalization setsM-spectral radius
CLC number: 15A18, 15A42, 15A69

References

【1】
【1】
 
 
Crossref Google Scholar
AIMS Mathematics
Volume 7 Issue 4,
April 2022
Pages 6084-6098
DOI: 10.3934/math.2022339

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Cite this Report
Close
Close
Cite this article:
Bai S. A tighter M-eigenvalue localization set for partially symmetric tensors and its an application. AIMS Mathematics, 2022, 7(4): 6084-6098. https://doi.org/10.3934/math.2022339

1

Views

0

Downloads

0

Crossref

0

Web of Science

0

Scopus
Received: 12 November 2021
Revised: 28 December 2021
Accepted: 06 January 2022
Published: 15 April 2022
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)