A modified PRP-type conjugate gradient algorithm with complexity analysis and its application to image restoration problems

In this paper, a modified conjugate gradient method is proposed for nonconvex optimization. This method possesses the sufficient descent property independent of any line search. The global convergence property of the algorithm is established under the Wolfe line search strategy or the Armijo line se...

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Podrobná bibliografie
Vydáno v:	Journal of computational and applied mathematics Ročník 427; s. 115105
Hlavní autoři:	Chen, Yu, Kuang, Kai, Yan, Xueling
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.08.2023
Témata:	Complexity analysis Global convergence Nonconvex unconstrained optimization Sufficient descent condition Sufficient descent condition Global convergence Complexity analysis 49J52 90C33 Nonconvex unconstrained optimization
ISSN:	0377-0427
On-line přístup:	Získat plný text
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Popis
Shrnutí:	In this paper, a modified conjugate gradient method is proposed for nonconvex optimization. This method possesses the sufficient descent property independent of any line search. The global convergence property of the algorithm is established under the Wolfe line search strategy or the Armijo line search condition, respectively. Additionally, the complexity analysis of the proposed algorithm is investigated. To reach the point with the norm of the gradient below ɛ, the worst-case complexity bound matches that of the gradient method. Moreover, the obtained numerical results demonstrate that the modified method is effective for large-scale optimization problems and image restoration problems.
ISSN:	0377-0427
DOI:	10.1016/j.cam.2023.115105