An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem
- Autores
- Croceri, Graciela; Pizarro, Gonzalo; Sottosanto, Graciela
- Año de publicación
- 2017
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- In this work an iterative method to solve the nonlinear least squares problem is presented. The algorithm combines a secant method with a strategy of nonmonotone trust region. In order to dene the quadratic model, the Hessian matrix is chosen using a secant approach that takes advantage of the structure of the problem, and the radius of the trust region is updated following an adaptive technique. Moreover, convergence properties of this algorithm are proved. The numerical experimentation, in which several ways of choosing the Hessian matrix are compared, shows the effiency and robustness of the method.
Sociedad Argentina de Informática e Investigación Operativa - Materia
-
Ciencias Informáticas
Trust region
Least Squares Problem
Structured Secant Approximation - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/4.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/135159
Ver los metadatos del registro completo
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An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares ProblemCroceri, GracielaPizarro, GonzaloSottosanto, GracielaCiencias InformáticasTrust regionLeast Squares ProblemStructured Secant ApproximationIn this work an iterative method to solve the nonlinear least squares problem is presented. The algorithm combines a secant method with a strategy of nonmonotone trust region. In order to dene the quadratic model, the Hessian matrix is chosen using a secant approach that takes advantage of the structure of the problem, and the radius of the trust region is updated following an adaptive technique. Moreover, convergence properties of this algorithm are proved. The numerical experimentation, in which several ways of choosing the Hessian matrix are compared, shows the effiency and robustness of the method.Sociedad Argentina de Informática e Investigación Operativa2017-06-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf80-94http://sedici.unlp.edu.ar/handle/10915/135159enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/24info:eu-repo/semantics/altIdentifier/issn/1514-6774info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:34:01Zoai:sedici.unlp.edu.ar:10915/135159Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:34:01.58SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem |
| title |
An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem |
| spellingShingle |
An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem Croceri, Graciela Ciencias Informáticas Trust region Least Squares Problem Structured Secant Approximation |
| title_short |
An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem |
| title_full |
An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem |
| title_fullStr |
An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem |
| title_full_unstemmed |
An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem |
| title_sort |
An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem |
| dc.creator.none.fl_str_mv |
Croceri, Graciela Pizarro, Gonzalo Sottosanto, Graciela |
| author |
Croceri, Graciela |
| author_facet |
Croceri, Graciela Pizarro, Gonzalo Sottosanto, Graciela |
| author_role |
author |
| author2 |
Pizarro, Gonzalo Sottosanto, Graciela |
| author2_role |
author author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas Trust region Least Squares Problem Structured Secant Approximation |
| topic |
Ciencias Informáticas Trust region Least Squares Problem Structured Secant Approximation |
| dc.description.none.fl_txt_mv |
In this work an iterative method to solve the nonlinear least squares problem is presented. The algorithm combines a secant method with a strategy of nonmonotone trust region. In order to dene the quadratic model, the Hessian matrix is chosen using a secant approach that takes advantage of the structure of the problem, and the radius of the trust region is updated following an adaptive technique. Moreover, convergence properties of this algorithm are proved. The numerical experimentation, in which several ways of choosing the Hessian matrix are compared, shows the effiency and robustness of the method. Sociedad Argentina de Informática e Investigación Operativa |
| description |
In this work an iterative method to solve the nonlinear least squares problem is presented. The algorithm combines a secant method with a strategy of nonmonotone trust region. In order to dene the quadratic model, the Hessian matrix is chosen using a secant approach that takes advantage of the structure of the problem, and the radius of the trust region is updated following an adaptive technique. Moreover, convergence properties of this algorithm are proved. The numerical experimentation, in which several ways of choosing the Hessian matrix are compared, shows the effiency and robustness of the method. |
| publishDate |
2017 |
| dc.date.none.fl_str_mv |
2017-06-04 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/135159 |
| url |
http://sedici.unlp.edu.ar/handle/10915/135159 |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/24 info:eu-repo/semantics/altIdentifier/issn/1514-6774 |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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application/pdf 80-94 |
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