Geodesic
A modification of Dai-Yuan's conjugate gradient algorithm for solving unconstrained optimization
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 15 (2022) no. 3, pp. 127-133 Cet article a éte moissonné depuis la source Math-Net.Ru

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The spectral conjugate gradient method is an essential generalization of the conjugate gradient method, and it is also one of the effective numerical methods to solve large scale unconstrained optimization problems. We propose a new spectral Dai–Yuan (SDY) conjugate gradient method to solve nonlinear unconstrained optimization problems. The proposed method's global convergence was achieved under appropriate conditions, performing numerical testing on 65 benchmark tests to determine the effectiveness of the proposed method in comparison to other methods like the AMDYN algorithm and some other existing ones like Dai-Yuan method.
Keywords: unconstrained optimization, conjugate gradient method
Mots-clés : spectral conjugate gradient, sufficient descent, global convergence. 
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     title = {A modification of {Dai-Yuan's} conjugate gradient algorithm for solving unconstrained optimization},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a, Matemati\v{c}eskoe modelirovanie i programmirovanie},
     pages = {127--133},
     year = {2022},
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     url = {http://geodesic.mathdoc.fr/item/VYURU_2022_15_3_a8/}
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Y. Najm Huda; I. Ahmed Huda. A modification of Dai-Yuan's conjugate gradient algorithm for solving unconstrained optimization. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 15 (2022) no. 3, pp. 127-133. http://geodesic.mathdoc.fr/item/VYURU_2022_15_3_a8/

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