Numer. Math. Theor. Meth. Appl., 14 (2021), pp. 527-539.
Published online: 2021-01
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Conjugate gradient methods are interesting iterative methods that solve large scale unconstrained optimization problems. A lot of recent research has thus focussed on developing a number of conjugate gradient methods that are more effective. In this paper, we propose another hybrid conjugate gradient method as a linear combination of Dai-Yuan (DY) method and the Hestenes-Stiefel (HS) method. The sufficient descent condition and the global convergence of this method are established using the generalized Wolfe line search conditions. Compared to the other conjugate gradient methods, the proposed method gives good numerical results and is effective.
}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2020-0056}, url = {http://global-sci.org/intro/article_detail/nmtma/18610.html} }Conjugate gradient methods are interesting iterative methods that solve large scale unconstrained optimization problems. A lot of recent research has thus focussed on developing a number of conjugate gradient methods that are more effective. In this paper, we propose another hybrid conjugate gradient method as a linear combination of Dai-Yuan (DY) method and the Hestenes-Stiefel (HS) method. The sufficient descent condition and the global convergence of this method are established using the generalized Wolfe line search conditions. Compared to the other conjugate gradient methods, the proposed method gives good numerical results and is effective.