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Journal of Applied Mathematics and Computing

Erschienen in:

28.03.2022 | Original Research

Two modified conjugate gradient methods for unconstrained optimization with applications in image restoration problems

verfasst von: Guodong Ma, Hui Lin, Wenhui Jin, Daolan Han

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 6/2022

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Abstract

The conjugate gradient methods (CGMs) are very effective iterative methods for solving unconstrained optimization problems. In this paper, the second inequality of the strong Wolfe line search is used to modify the conjugate parameters of the PRP and HS methods, and thereby two efficient conjugate parameters are presented. Under basic assumptions, we prove that the two modified CGMs satisfy sufficient descent condition and converge globally for unconstrained optimization problems. Finally, to verify the effectiveness of our presented methods, we perform medium-large-scale numerical experiments for the normal unconstrained optimization and image restoration problems. The numerical results show the encouraging efficiency and applicability of the proposed methods.

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Titel: Two modified conjugate gradient methods for unconstrained optimization with applications in image restoration problems
verfasst von: Guodong Ma
Hui Lin
Wenhui Jin
Daolan Han
Publikationsdatum: 28.03.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Applied Mathematics and Computing / Ausgabe 6/2022
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI: https://doi.org/10.1007/s12190-022-01725-y

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