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Journal of Applied Mathematics and Computing

02.04.2024 | Original Research

A conjugate gradient projection method with restart procedure for solving constraint equations and image restorations

verfasst von: Xianzhen Jiang, Zefeng Huang, Huihui Yang

Erschienen in: Journal of Applied Mathematics and Computing

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Abstract

The conjugate gradient projection method is one of the most effective methods for solving large-scale nonlinear monotone convex constrained equations. In this paper, a new search direction with restart procedure is proposed, and a self-adjusting line search criterion is improved, then a three-term conjugate gradient projection method is designed to solve the large-scale nonlinear monotone convex constrained equations and image restorations. Without using the Lipschitz continuity of these equations, the presented method is proved to be globally convergent. Moreover, its R-linear convergence rate is attained under Lipschitz continuity and the usual assumptions. Finally, large-scale numerical experiments for the convex constraint equations and image restorations have been performed, which show that the new method is effective.

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Metadaten
Titel
A conjugate gradient projection method with restart procedure for solving constraint equations and image restorations
verfasst von
Xianzhen Jiang
Zefeng Huang
Huihui Yang
Publikationsdatum
02.04.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Applied Mathematics and Computing
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI
https://doi.org/10.1007/s12190-024-02044-0

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