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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-8211273 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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