The global convergence of spectral RMIL conjugate gradient method for unconstrained optimization with applications to robotic model and image recovery

Journal article

Authors/Editors

POOM KUMAM

Strategic Research Themes

Other (Strategic Research Themes)

Publication Details

Author list: Nasiru Salihu; Poom Kumam; Aliyu Muhammed Awwal; Ibrahim Mohammed Sulaiman; Thidaporn Seangwattana;

Publisher: Naramgari Sandeep, Central University of Karnataka, INDIA

Publication year: 2023

Volume number: 18

Issue number: 3

ISSN: 19326203

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150280453&doi=10.1371%2fjournal.pone.0281250&partnerID=40&md5=6efebfa70d5f1ab9a5f500c4e0500030

Languages: English-Great Britain (EN-GB)

View in Web of Science | View on publisher site | View citing articles in Web of Science

Abstract

In 2012, Rivaie et al. introduced RMIL conjugate gradient (CG) method which is globally convergent under the exact line search. Later, Dai (2016) pointed out abnormality in the convergence result and thus, imposed certain restricted RMIL CG parameter as a remedy. In this paper, we suggest an efficient RMIL spectral CG method. The remarkable feature of this method is that, the convergence result is free from additional condition usually imposed on RMIL. Subsequently, the search direction is sufficiently descent independent of any line search technique. Thus, numerical experiments on some set of benchmark problems indicate that the method is promising and efficient. Furthermore, the efficiency of the proposed method is demonstrated on applications arising from arm robotic model and image restoration problems. Copyright: © 2023 Salihu et al.

Keywords

No matching items found.