Inexact Restoration method for nonlinear optimization without derivatives
- Autores
- Arouxét, María Belén; Echebest, Nélida Ester; Pilotta, Elvio Ángel
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- A derivative-free optimization method is proposed for solving a general nonlinear programming problem. It is assumed that the derivatives of the objective function and the constraints are not available. The new method is based on the Inexact Restoration scheme, where each iteration is decomposed in two phases. In the first one, the violation of the feasibility is reduced. In the second one, the objective function is minimized onto a linearization of the nonlinear constraints. At both phases, polynomial interpolation models are used in order to approximate the objective function and the constraints. At the first phase a derivative-free solver for box constrained optimization can be used. For the second phase, we propose a new method ad-hoc based on trust-region strategy that uses the projection of the simplex gradient on the tangent space. Under suitable assumptions, the algorithm is well defined and convergence results are proved. A numerical implementation is described and numerical experiments are presented to validate the theoretical results.
Facultad de Ciencias Exactas - Materia
-
Matemática
Inexact Restoration
Derivative-free optimization
Trust-region methods
Polynomial interpolation - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/94598
Ver los metadatos del registro completo
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Inexact Restoration method for nonlinear optimization without derivativesArouxét, María BelénEchebest, Nélida EsterPilotta, Elvio ÁngelMatemáticaInexact RestorationDerivative-free optimizationTrust-region methodsPolynomial interpolationA derivative-free optimization method is proposed for solving a general nonlinear programming problem. It is assumed that the derivatives of the objective function and the constraints are not available. The new method is based on the Inexact Restoration scheme, where each iteration is decomposed in two phases. In the first one, the violation of the feasibility is reduced. In the second one, the objective function is minimized onto a linearization of the nonlinear constraints. At both phases, polynomial interpolation models are used in order to approximate the objective function and the constraints. At the first phase a derivative-free solver for box constrained optimization can be used. For the second phase, we propose a new method ad-hoc based on trust-region strategy that uses the projection of the simplex gradient on the tangent space. Under suitable assumptions, the algorithm is well defined and convergence results are proved. A numerical implementation is described and numerical experiments are presented to validate the theoretical results.Facultad de Ciencias Exactas2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf26-43http://sedici.unlp.edu.ar/handle/10915/94598enginfo:eu-repo/semantics/altIdentifier/issn/0377-0427info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cam.2015.04.047info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:00:35Zoai:sedici.unlp.edu.ar:10915/94598Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:00:36.191SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Inexact Restoration method for nonlinear optimization without derivatives |
| title |
Inexact Restoration method for nonlinear optimization without derivatives |
| spellingShingle |
Inexact Restoration method for nonlinear optimization without derivatives Arouxét, María Belén Matemática Inexact Restoration Derivative-free optimization Trust-region methods Polynomial interpolation |
| title_short |
Inexact Restoration method for nonlinear optimization without derivatives |
| title_full |
Inexact Restoration method for nonlinear optimization without derivatives |
| title_fullStr |
Inexact Restoration method for nonlinear optimization without derivatives |
| title_full_unstemmed |
Inexact Restoration method for nonlinear optimization without derivatives |
| title_sort |
Inexact Restoration method for nonlinear optimization without derivatives |
| dc.creator.none.fl_str_mv |
Arouxét, María Belén Echebest, Nélida Ester Pilotta, Elvio Ángel |
| author |
Arouxét, María Belén |
| author_facet |
Arouxét, María Belén Echebest, Nélida Ester Pilotta, Elvio Ángel |
| author_role |
author |
| author2 |
Echebest, Nélida Ester Pilotta, Elvio Ángel |
| author2_role |
author author |
| dc.subject.none.fl_str_mv |
Matemática Inexact Restoration Derivative-free optimization Trust-region methods Polynomial interpolation |
| topic |
Matemática Inexact Restoration Derivative-free optimization Trust-region methods Polynomial interpolation |
| dc.description.none.fl_txt_mv |
A derivative-free optimization method is proposed for solving a general nonlinear programming problem. It is assumed that the derivatives of the objective function and the constraints are not available. The new method is based on the Inexact Restoration scheme, where each iteration is decomposed in two phases. In the first one, the violation of the feasibility is reduced. In the second one, the objective function is minimized onto a linearization of the nonlinear constraints. At both phases, polynomial interpolation models are used in order to approximate the objective function and the constraints. At the first phase a derivative-free solver for box constrained optimization can be used. For the second phase, we propose a new method ad-hoc based on trust-region strategy that uses the projection of the simplex gradient on the tangent space. Under suitable assumptions, the algorithm is well defined and convergence results are proved. A numerical implementation is described and numerical experiments are presented to validate the theoretical results. Facultad de Ciencias Exactas |
| description |
A derivative-free optimization method is proposed for solving a general nonlinear programming problem. It is assumed that the derivatives of the objective function and the constraints are not available. The new method is based on the Inexact Restoration scheme, where each iteration is decomposed in two phases. In the first one, the violation of the feasibility is reduced. In the second one, the objective function is minimized onto a linearization of the nonlinear constraints. At both phases, polynomial interpolation models are used in order to approximate the objective function and the constraints. At the first phase a derivative-free solver for box constrained optimization can be used. For the second phase, we propose a new method ad-hoc based on trust-region strategy that uses the projection of the simplex gradient on the tangent space. Under suitable assumptions, the algorithm is well defined and convergence results are proved. A numerical implementation is described and numerical experiments are presented to validate the theoretical results. |
| publishDate |
2015 |
| dc.date.none.fl_str_mv |
2015 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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http://sedici.unlp.edu.ar/handle/10915/94598 |
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eng |
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eng |
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info:eu-repo/semantics/altIdentifier/issn/0377-0427 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cam.2015.04.047 |
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