In this paper we propose and analyze another hybrid conjugate gradient algorithm [...]. The algorithm uses the standard Wolfe line search conditions. Numerical comparisons with conjugate gradient algorithms using a set of 750 unconstrained optimization problems, some of them from the CUTE library, show that this hybrid computational scheme outperforms the Hestenes-Stiefel and the Dai-Yuan conjugate gradient algorithms as well as some other known hybrid conjugate gradient algorithms. Comparisons with CG_DESCENT by Hager and Zhang [17] and LBFGS by Liu and Nocedal [22] show that CG_DESCENT is more robust then our algorithm, and LBFGS is top performer among these algorithms.
MSC: 49M07, 49M10, 90C06, 65K
Unconstrained optimization, hybrid conjugate gradient method, Newton direction, numerical comparisons
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