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A modified filter nonmonotone adaptive retrospective trust region method

In this paper, aiming at the unconstrained optimization problem, a new nonmonotone adaptive retrospective trust region line search method is presented, which takes advantages of multidimensional filter technique to increase the acceptance probability of the trial step. The new nonmonotone trust regi...

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Detalles Bibliográficos
Autores principales: Ding, Xianfeng, Qu, Quan, Wang, Xinyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211273/
https://www.ncbi.nlm.nih.gov/pubmed/34138939
http://dx.doi.org/10.1371/journal.pone.0253016
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author Ding, Xianfeng
Qu, Quan
Wang, Xinyi
author_facet Ding, Xianfeng
Qu, Quan
Wang, Xinyi
author_sort Ding, Xianfeng
collection PubMed
description In this paper, aiming at the unconstrained optimization problem, a new nonmonotone adaptive retrospective trust region line search method is presented, which takes advantages of multidimensional filter technique to increase the acceptance probability of the trial step. The new nonmonotone trust region ratio is presented, which based on the convex combination of nonmonotone trust region ratio and retrospective ratio. The global convergence and the superlinear convergence of the algorithm are shown in the right circumstances. Comparative numerical experiments show the better effective and robustness.
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spelling pubmed-82112732021-06-29 A modified filter nonmonotone adaptive retrospective trust region method Ding, Xianfeng Qu, Quan Wang, Xinyi PLoS One Research Article In this paper, aiming at the unconstrained optimization problem, a new nonmonotone adaptive retrospective trust region line search method is presented, which takes advantages of multidimensional filter technique to increase the acceptance probability of the trial step. The new nonmonotone trust region ratio is presented, which based on the convex combination of nonmonotone trust region ratio and retrospective ratio. The global convergence and the superlinear convergence of the algorithm are shown in the right circumstances. Comparative numerical experiments show the better effective and robustness. Public Library of Science 2021-06-17 /pmc/articles/PMC8211273/ /pubmed/34138939 http://dx.doi.org/10.1371/journal.pone.0253016 Text en © 2021 Ding et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ding, Xianfeng
Qu, Quan
Wang, Xinyi
A modified filter nonmonotone adaptive retrospective trust region method
title A modified filter nonmonotone adaptive retrospective trust region method
title_full A modified filter nonmonotone adaptive retrospective trust region method
title_fullStr A modified filter nonmonotone adaptive retrospective trust region method
title_full_unstemmed A modified filter nonmonotone adaptive retrospective trust region method
title_short A modified filter nonmonotone adaptive retrospective trust region method
title_sort modified filter nonmonotone adaptive retrospective trust region method
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211273/
https://www.ncbi.nlm.nih.gov/pubmed/34138939
http://dx.doi.org/10.1371/journal.pone.0253016
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