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 (265.1 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

Two improved extrapolated gradient algorithms for pseudo-monotone variational inequalities

Haoran Tang, Weiqiang Gong( )
School of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing 210023, China
Show Author Information

Abstract

In this paper, the approximation problem for pseudo-monotonic variational inequalities is studied. With the help of the reciprocal form of parameters and the golden section ratio, two kinds of algorithms are introduced, and their strong convergence is proved under certain appropriate conditions.

Keywords

pseudo-monotonic variational inequalitysubgradient projectiongolden section ratiostrong convergence
CLC number: 47J20

References

【1】
【1】
 
 
Crossref Google Scholar
AIMS Mathematics
Volume 10 Issue 2,
February 2025
Pages 2064-2082
DOI: 10.3934/math.2025097

{{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:
Tang H, Gong W. Two improved extrapolated gradient algorithms for pseudo-monotone variational inequalities. AIMS Mathematics, 2025, 10(2): 2064-2082. https://doi.org/10.3934/math.2025097

115

Views

1

Downloads

1

Crossref

1

Web of Science

0

Scopus
Received: 13 November 2024
Revised: 14 January 2025
Accepted: 24 January 2025
Published: 15 February 2025
©2025 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)