@Article{NMTMA-14-2,
author = {P., Kaelo and Sindhu, Narayanan and P., Kaelo},
title = {A Linear Hybridization of Dai-Yuan and Hestenes-Stiefel Conjugate Gradient Method for Unconstrained Optimization},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2021},
volume = {14},
number = {2},
pages = {527--539},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.OA-2020-0056},
url = {https://global-sci.com/article/90309/a-linear-hybridization-of-dai-yuan-and-hestenes-stiefel-conjugate-gradient-method-for-unconstrained-optimization}
}