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Title: New hybrid conjugate gradient method for nonlinear optimization with application to image restoration problems (English)
Author: Hemici, Youcef Elhamam
Author: Khelladi, Samia
Author: Benterki, Djamel
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 60
Issue: 4
Year: 2024
Pages: 535-552
Summary lang: English
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Category: math
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Summary: The conjugate gradient method is one of the most effective algorithm for unconstrained nonlinear optimization problems. This is due to the fact that it does not need a lot of storage memory and its simple structure properties, which motivate us to propose a new hybrid conjugate gradient method through a convex combination of $\beta _{k}^{RMIL}$ and $\beta _{k}^{HS}$. We compute the convex parameter $\theta _{k}$ using the Newton direction. Global convergence is established through the strong Wolfe conditions. Numerical experiments show the superior efficiency of our algorithm to solve unconstrained optimization problem compared to other considered methods. Applied to image restoration problem, our algorithm is competitive with existing algorithms and performs even better when the level of noise in the image is significant. (English)
Keyword: unconstrained optimization
Keyword: conjugate gradient method
Keyword: descent direction
Keyword: line search
Keyword: image restoration
MSC: 65K05
MSC: 90C26
MSC: 90C30
DOI: 10.14736/kyb-2024-4-0535
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Date available: 2024-10-17T08:49:48Z
Last updated: 2024-10-17
Stable URL: http://hdl.handle.net/10338.dmlcz/152618
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