Geodesic
A new hybrid conjugate gradient algorithm for unconstrained optimization
Vestnik Udmurtskogo universiteta. Matematika, mehanika, kompʹûternye nauki, Tome 33 (2023) no. 2, pp. 348-364

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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
Mots-clés : conjugacy conditions and sufficient descent conditions. 
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     author = {Hafaidia Imane and H. Guebbai and Al-Baali Mehiddin and M. Ghiat},
     title = {A new hybrid conjugate gradient algorithm for unconstrained optimization},
     journal = {Vestnik Udmurtskogo universiteta. Matematika, mehanika, kompʹ\^uternye nauki},
     pages = {348--364},
     publisher = {mathdoc},
     volume = {33},
     number = {2},
     year = {2023},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/VUU_2023_33_2_a10/}
}
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Hafaidia Imane; H. Guebbai; Al-Baali Mehiddin; M. Ghiat. A new hybrid conjugate gradient algorithm for unconstrained optimization. Vestnik Udmurtskogo universiteta. Matematika, mehanika, kompʹûternye nauki, Tome 33 (2023) no. 2, pp. 348-364. http://geodesic.mathdoc.fr/item/VUU_2023_33_2_a10/