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Algeria; Oman Guelma; Muscat
Year
2023
Volume
33
Issue
2
Pages
348-364
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Section Mathematics
Title A new hybrid conjugate gradient algorithm for unconstrained optimization
Author(-s) Hafaidia I.a, Guebbai H.a, Al-Baali M.b, Ghiat M.a
Affiliations University 8 Mai 1945a, Sultan Qaboos Universityb
Abstract It is well known that conjugate gradient methods are useful for solving large-scale unconstrained nonlinear optimization problems. In this paper, we consider combining the best features of two conjugate gradient methods. In particular, we give a new conjugate gradient method, based on the hybridization of the useful DY (Dai-Yuan), and HZ (Hager-Zhang) methods. The hybrid parameters are chosen such that the proposed method satisfies the conjugacy and sufficient descent conditions. It is shown that the new method maintains the global convergence property of the above two methods. The numerical results are described for a set of standard test problems. It is shown that the performance of the proposed method is better than that of the DY and HZ methods in most cases.
Keywords unconstrained optimization, conjugate gradient methods, conjugacy conditions and sufficient descent conditions
UDC 519.6
MSC 90C30, 90C53
DOI 10.35634/vm230211
Received 2 July 2022
Language English
Citation Hafaidia I., Guebbai H., Al-Baali M., Ghiat M. A new hybrid conjugate gradient algorithm for unconstrained optimization, Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki, 2023, vol. 33, issue 2, pp. 348-364.
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