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New hybrid conjugate gradient methods with the generalized Wolfe line search

The conjugate gradient method was an efficient technique for solving the unconstrained optimization problem. In this paper, we made a linear combination with parameters β(k) of the DY method and the HS method, and putted forward the hybrid method of DY and HS. We also proposed the hybrid of FR and P...

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Detalles Bibliográficos
Autores principales: Xu, Xiao, Kong, Fan-yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4920800/
https://www.ncbi.nlm.nih.gov/pubmed/27386329
http://dx.doi.org/10.1186/s40064-016-2522-9
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author Xu, Xiao
Kong, Fan-yu
author_facet Xu, Xiao
Kong, Fan-yu
author_sort Xu, Xiao
collection PubMed
description The conjugate gradient method was an efficient technique for solving the unconstrained optimization problem. In this paper, we made a linear combination with parameters β(k) of the DY method and the HS method, and putted forward the hybrid method of DY and HS. We also proposed the hybrid of FR and PRP by the same mean. Additionally, to present the two hybrid methods, we promoted the Wolfe line search respectively to compute the step size α(k) of the two hybrid methods. With the new Wolfe line search, the two hybrid methods had descent property and global convergence property of the two hybrid methods that can also be proved.
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spelling pubmed-49208002016-07-06 New hybrid conjugate gradient methods with the generalized Wolfe line search Xu, Xiao Kong, Fan-yu Springerplus Methodology The conjugate gradient method was an efficient technique for solving the unconstrained optimization problem. In this paper, we made a linear combination with parameters β(k) of the DY method and the HS method, and putted forward the hybrid method of DY and HS. We also proposed the hybrid of FR and PRP by the same mean. Additionally, to present the two hybrid methods, we promoted the Wolfe line search respectively to compute the step size α(k) of the two hybrid methods. With the new Wolfe line search, the two hybrid methods had descent property and global convergence property of the two hybrid methods that can also be proved. Springer International Publishing 2016-06-24 /pmc/articles/PMC4920800/ /pubmed/27386329 http://dx.doi.org/10.1186/s40064-016-2522-9 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Methodology
Xu, Xiao
Kong, Fan-yu
New hybrid conjugate gradient methods with the generalized Wolfe line search
title New hybrid conjugate gradient methods with the generalized Wolfe line search
title_full New hybrid conjugate gradient methods with the generalized Wolfe line search
title_fullStr New hybrid conjugate gradient methods with the generalized Wolfe line search
title_full_unstemmed New hybrid conjugate gradient methods with the generalized Wolfe line search
title_short New hybrid conjugate gradient methods with the generalized Wolfe line search
title_sort new hybrid conjugate gradient methods with the generalized wolfe line search
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4920800/
https://www.ncbi.nlm.nih.gov/pubmed/27386329
http://dx.doi.org/10.1186/s40064-016-2522-9
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