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The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models

The hybrid conjugate gradient (CG) algorithms are among the efficient modifications of the conjugate gradient methods. Some interesting features of the hybrid modifications include inherenting the nice convergence properties and efficient numerical performance of the existing CG methods. In this p...

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Main Authors: Sulaiman, I.M, Mamat, M., Waziri, M.Y., Yakubu, U.A., Malik, M.
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
QA Mathematics
QA75 Electronic computers. Computer science
Online Access:http://eprints.unisza.edu.my/4741/1/FH03-FIK-21-51447.pdf
http://eprints.unisza.edu.my/4741/
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spelling my-unisza-ir.47412022-01-17T06:42:09Z http://eprints.unisza.edu.my/4741/ The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models Sulaiman, I.M Mamat, M. Waziri, M.Y. Yakubu, U.A. Malik, M. QA Mathematics QA75 Electronic computers. Computer science The hybrid conjugate gradient (CG) algorithms are among the efficient modifications of the conjugate gradient methods. Some interesting features of the hybrid modifications include inherenting the nice convergence properties and efficient numerical performance of the existing CG methods. In this paper, we proposed a new hybrid CG algorithm that inherits the features of the Rivaie et al. (RMIL∗) and Dai (RMIL+) conjugate gradient methods. The proposed algorithm generates a descent direction under the strong Wolfe line search conditions. Preliminary results on some benchmark problems reveal that the proposed method efficient and promising. 2021 Conference or Workshop Item PeerReviewed text en http://eprints.unisza.edu.my/4741/1/FH03-FIK-21-51447.pdf Sulaiman, I.M and Mamat, M. and Waziri, M.Y. and Yakubu, U.A. and Malik, M. (2021) The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models. In: 1st International Conference on Recent Trends in Applied Research, 14-15 Aug 2020, Nigeria, Virtual.
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
topic QA Mathematics
QA75 Electronic computers. Computer science
spellingShingle QA Mathematics
QA75 Electronic computers. Computer science
Sulaiman, I.M
Mamat, M.
Waziri, M.Y.
Yakubu, U.A.
Malik, M.
The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
description The hybrid conjugate gradient (CG) algorithms are among the efficient modifications of the conjugate gradient methods. Some interesting features of the hybrid modifications include inherenting the nice convergence properties and efficient numerical performance of the existing CG methods. In this paper, we proposed a new hybrid CG algorithm that inherits the features of the Rivaie et al. (RMIL∗) and Dai (RMIL+) conjugate gradient methods. The proposed algorithm generates a descent direction under the strong Wolfe line search conditions. Preliminary results on some benchmark problems reveal that the proposed method efficient and promising.
format Conference or Workshop Item
author Sulaiman, I.M
Mamat, M.
Waziri, M.Y.
Yakubu, U.A.
Malik, M.
author_facet Sulaiman, I.M
Mamat, M.
Waziri, M.Y.
Yakubu, U.A.
Malik, M.
author_sort Sulaiman, I.M
title The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
title_short The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
title_full The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
title_fullStr The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
title_full_unstemmed The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
title_sort convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
publishDate 2021
url http://eprints.unisza.edu.my/4741/1/FH03-FIK-21-51447.pdf
http://eprints.unisza.edu.my/4741/
_version_ 1724079378478923776
score 13.211869

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