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
PDF (700 KB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

The general tensor regular splitting iterative method for multilinear PageRank problem

Shuting TangXiuqin DengRui Zhan( )
School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, 510006, China
Show Author Information

Abstract

The paper presents an iterative scheme called the general tensor regular splitting iterative (GTRS) method for solving the multilinear PageRank problem, which is based on a (weak) regular splitting technique and further accelerates the iterative process by introducing a parameter. The method yields familiar iterative schemes through the use of specific splitting strategies, including fixed-point, inner-outer, Jacobi, Gauss-Seidel and successive overrelaxation methods. The paper analyzes the convergence of these solvers in detail. Numerical results are provided to demonstrate the effectiveness of the proposed method in solving the multilinear PageRank problem.

CLC number: 65F10, 65H10

References

【1】
【1】
 
 
AIMS Mathematics
Pages 1443-1471

{{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

Close
Close
Cite this article:
Tang S, Deng X, Zhan R. The general tensor regular splitting iterative method for multilinear PageRank problem. AIMS Mathematics, 2024, 9(1): 1443-1471. https://doi.org/10.3934/math.2024071

7

Views

0

Downloads

0

Crossref

0

Web of Science

0

Scopus

Received: 08 October 2023
Revised: 17 November 2023
Accepted: 30 November 2023
Published: 15 January 2024
©2024 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)