New Hybrid Conjugate Gradient Method as a Convex Combination of PRP and RMIL+ Methods
DOI:
https://doi.org/10.24193/subbmath.2024.2.14Keywords:
Unconstrained optimization, hybrid conjugate gradient method, line search, descent property, global convergenceAbstract
The Conjugate Gradient (CG) method is a powerful iterative approach for solving large-scale minimization problems, characterized by its simplicity, low computation cost and good convergence. In this paper, a new hybrid conjugate gradient HLB method (HLB: Hadji-Laskri-Bechouat) is proposed and analysed for unconstrained optimization. By comparing numerically CGHLB with PRP and RMIL+ and by using the Dolan and More CPU performance, we deduce that CGHLB is more efficient.
Mathematics Subject Classification (2010): 90C26, 65H10, 65K05, 90C26, 90C06.
Received 08 October 2021; Accepted 12 September 2022
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