Cutting planes and a biased Newton direction for minimizing quasiconvex functions
- Autores
- Echebest, Nélida Ester; Guardarucci, María Teresa; Scolnik, Hugo Daniel; Vacchino, María Cristina
- Año de publicación
- 2000
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- A biased Newton direction is introduced for minimizing quasiconver functions with bounded level sets. It is a generalization of the usual Newton’s direction for strictly convex quadratic functions. This new direction can be derived from the intersection of approzimating hyperplanes to the epigraph at points on the boundary of the same level set. Based on that direction, an unconstrained minimization algorithm is presented. It is proved to have global and local-quadratic convergence under standard hypotheses. These theoretical results may lead to different methods based on computing search directions using only first order information at points on the level sets. Most of all if the computational cost can be reduced by relaxing some of the conditions according for instance to the results presented in the Appendix. Some tests are presented to show the qualitative behavior of the new direction and with the purpose to stimulate further research on these kind of algorithms.
Facultad de Ciencias Exactas - Materia
-
Matemática
Quasiconvex functions
Level sets
Discretization methods - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/150086
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Cutting planes and a biased Newton direction for minimizing quasiconvex functionsEchebest, Nélida EsterGuardarucci, María TeresaScolnik, Hugo DanielVacchino, María CristinaMatemáticaQuasiconvex functionsLevel setsDiscretization methodsA biased Newton direction is introduced for minimizing quasiconver functions with bounded level sets. It is a generalization of the usual Newton’s direction for strictly convex quadratic functions. This new direction can be derived from the intersection of approzimating hyperplanes to the epigraph at points on the boundary of the same level set. Based on that direction, an unconstrained minimization algorithm is presented. It is proved to have global and local-quadratic convergence under standard hypotheses. These theoretical results may lead to different methods based on computing search directions using only first order information at points on the level sets. Most of all if the computational cost can be reduced by relaxing some of the conditions according for instance to the results presented in the Appendix. Some tests are presented to show the qualitative behavior of the new direction and with the purpose to stimulate further research on these kind of algorithms.Facultad de Ciencias Exactas2000info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf135-166http://sedici.unlp.edu.ar/handle/10915/150086enginfo:eu-repo/semantics/altIdentifier/issn/1014-8264info: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:UNLP2025-09-03T11:10:35Zoai:sedici.unlp.edu.ar:10915/150086Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:10:35.365SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Cutting planes and a biased Newton direction for minimizing quasiconvex functions |
title |
Cutting planes and a biased Newton direction for minimizing quasiconvex functions |
spellingShingle |
Cutting planes and a biased Newton direction for minimizing quasiconvex functions Echebest, Nélida Ester Matemática Quasiconvex functions Level sets Discretization methods |
title_short |
Cutting planes and a biased Newton direction for minimizing quasiconvex functions |
title_full |
Cutting planes and a biased Newton direction for minimizing quasiconvex functions |
title_fullStr |
Cutting planes and a biased Newton direction for minimizing quasiconvex functions |
title_full_unstemmed |
Cutting planes and a biased Newton direction for minimizing quasiconvex functions |
title_sort |
Cutting planes and a biased Newton direction for minimizing quasiconvex functions |
dc.creator.none.fl_str_mv |
Echebest, Nélida Ester Guardarucci, María Teresa Scolnik, Hugo Daniel Vacchino, María Cristina |
author |
Echebest, Nélida Ester |
author_facet |
Echebest, Nélida Ester Guardarucci, María Teresa Scolnik, Hugo Daniel Vacchino, María Cristina |
author_role |
author |
author2 |
Guardarucci, María Teresa Scolnik, Hugo Daniel Vacchino, María Cristina |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Matemática Quasiconvex functions Level sets Discretization methods |
topic |
Matemática Quasiconvex functions Level sets Discretization methods |
dc.description.none.fl_txt_mv |
A biased Newton direction is introduced for minimizing quasiconver functions with bounded level sets. It is a generalization of the usual Newton’s direction for strictly convex quadratic functions. This new direction can be derived from the intersection of approzimating hyperplanes to the epigraph at points on the boundary of the same level set. Based on that direction, an unconstrained minimization algorithm is presented. It is proved to have global and local-quadratic convergence under standard hypotheses. These theoretical results may lead to different methods based on computing search directions using only first order information at points on the level sets. Most of all if the computational cost can be reduced by relaxing some of the conditions according for instance to the results presented in the Appendix. Some tests are presented to show the qualitative behavior of the new direction and with the purpose to stimulate further research on these kind of algorithms. Facultad de Ciencias Exactas |
description |
A biased Newton direction is introduced for minimizing quasiconver functions with bounded level sets. It is a generalization of the usual Newton’s direction for strictly convex quadratic functions. This new direction can be derived from the intersection of approzimating hyperplanes to the epigraph at points on the boundary of the same level set. Based on that direction, an unconstrained minimization algorithm is presented. It is proved to have global and local-quadratic convergence under standard hypotheses. These theoretical results may lead to different methods based on computing search directions using only first order information at points on the level sets. Most of all if the computational cost can be reduced by relaxing some of the conditions according for instance to the results presented in the Appendix. Some tests are presented to show the qualitative behavior of the new direction and with the purpose to stimulate further research on these kind of algorithms. |
publishDate |
2000 |
dc.date.none.fl_str_mv |
2000 |
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/10915/150086 |
url |
http://sedici.unlp.edu.ar/handle/10915/150086 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/issn/1014-8264 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf 135-166 |
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