A Globally Convergent Hybrid FR-PRP Conjugate Gradient Method for Unconstrained Optimization Problems

Autor:	Olawale J. Adeleke, Idowu A. Osinuga, Raufu A. Raji
Rok vydání:	2022
Předmět:	General Mathematics
Zdroj:	WSEAS TRANSACTIONS ON MATHEMATICS. 20:736-744
ISSN:	2224-2880 1109-2769
Popis:	In this paper, a new conjugate gradient (CG) parameter is proposed through the convex combination of the Fletcher-Reeves (FR) and Polak-Ribiére-Polyak (PRP) CG update parameters such that the conjugacy condition of Dai-Liao is satisfied. The computational efficiency of the PRP method and the convergence profile of the FR method motivated the choice of these two CG methods. The corresponding CG algorithm satisfies the sufficient descent property and was shown to be globally convergent under the strong Wolfe line search procedure. Numerical tests on selected benchmark test functions show that the algorithm is efficient and very competitive in comparison with some existing classical methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::34ca7c8230789df4d9251d7e728e95a1 https://doi.org/10.37394/23206.2021.20.78 Zobrazit plný text záznamu