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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2022

13-08-2021 | Original Article

A conjugate gradient algorithm based on double parameter scaled Broyden–Fletcher–Goldfarb–Shanno update for optimization problems and image restoration

Authors: Dan Luo, Yong Li, Junyu Lu, Gonglin Yuan

Published in: Neural Computing and Applications | Issue 1/2022

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Abstract

In this paper, we present a three-term conjugate gradient algorithm and three approaches are used in the designed algorithm: (i) A modified weak Wolfe-Powell line search technique is introduced to obtain \(\alpha _k\). (ii) The search direction \(d_k\) is given by a symmetrical Perry matrix which contains two positive parameters, and the sufficient descent property of the generated directions holds independent of the MWWP line search technique. (iii) A parabolic will be proposed and regarded as the projection surface, the next point \(x_{k+1}\) is generated by a new projection technique. The global convergence of the new algorithm under a MWWP line search is obtained for general functions. Numerical experiments show that the given algorithm is promising.
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Metadata
Title
A conjugate gradient algorithm based on double parameter scaled Broyden–Fletcher–Goldfarb–Shanno update for optimization problems and image restoration
Authors
Dan Luo
Yong Li
Junyu Lu
Gonglin Yuan
Publication date
13-08-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 1/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-06383-y

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