On the Extension of Dai-Liao Conjugate Gradient Method for Vector Optimization

Qingjie Hu (), Ruyun Li (), Yanyan Zhang and Zhibin Zhu
Additional contact information
Qingjie Hu: Guilin University of Electronic Technology
Ruyun Li: Guilin University of Electronic Technology
Yanyan Zhang: Guilin University of Electronic Technology
Zhibin Zhu: Guilin University of Electronic Technology

Journal of Optimization Theory and Applications, 2024, vol. 203, issue 1, No 29, 810-843

Abstract: Abstract In this paper, we extend the Dai-Liao conjugate gradient method to vector optimization. Firstly, we analyze the global convergence of the direct extension version of the Dai-Liao conjugate gradient method for K-strongly convex vector functions. Secondly, we investigate the global convergence of the vector version of restricted non-negative Dai-Liao conjugate gradient method for general vector functions. Additionally, we discuss the global convergence of the vector version of modified Dai-Liao conjugate gradient method for general vector functions. Finally, numerical experiments demonstrate that the proposed conjugate gradient methods are effective for solving vector optimization problems. In particular, these methods can effectively generate the Pareto frontiers for the test problems.

Keywords: Vector optimization; Pareto optimality; Dai-Liao conjugate gradient method; Global convergence (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10957-024-02535-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:203:y:2024:i:1:d:10.1007_s10957-024-02535-x

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-024-02535-x

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().