A new method to compute second derivatives
- Autores
- Scolnik, Hugo Daniel; Gambini, María Juliana
- Año de publicación
- 2001
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- In this article we consider the problem of computing approximations to the second derivatives of functions of n variables using finite differences. We show how to derive different formulas and how to comput the errors of those approximations as functions of the increment h, both for first and second derivatives. Based upon those results we describe the methods of Gill and Murray and the one of gradient difference. On the other hand we introduce a new algorithm which use conjugate directions methods for minimizing functions without derivatives and the corresponding numerical comparisons with the other two methods. Finally, numerical experiences are given and the corresponding conclusions are discussed.
Facultad de Informática - Materia
-
Ciencias Informáticas
Algorithms
finite difference
numerical approximation - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc/3.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/9428
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A new method to compute second derivativesScolnik, Hugo DanielGambini, María JulianaCiencias InformáticasAlgorithmsfinite differencenumerical approximationIn this article we consider the problem of computing approximations to the second derivatives of functions of n variables using finite differences. We show how to derive different formulas and how to comput the errors of those approximations as functions of the increment h, both for first and second derivatives. Based upon those results we describe the methods of Gill and Murray and the one of gradient difference. On the other hand we introduce a new algorithm which use conjugate directions methods for minimizing functions without derivatives and the corresponding numerical comparisons with the other two methods. Finally, numerical experiences are given and the corresponding conclusions are discussed.Facultad de Informática2001info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/9428enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/ipaper3.pdfinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:23:30Zoai:sedici.unlp.edu.ar:10915/9428Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:23:30.78SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
A new method to compute second derivatives |
| title |
A new method to compute second derivatives |
| spellingShingle |
A new method to compute second derivatives Scolnik, Hugo Daniel Ciencias Informáticas Algorithms finite difference numerical approximation |
| title_short |
A new method to compute second derivatives |
| title_full |
A new method to compute second derivatives |
| title_fullStr |
A new method to compute second derivatives |
| title_full_unstemmed |
A new method to compute second derivatives |
| title_sort |
A new method to compute second derivatives |
| dc.creator.none.fl_str_mv |
Scolnik, Hugo Daniel Gambini, María Juliana |
| author |
Scolnik, Hugo Daniel |
| author_facet |
Scolnik, Hugo Daniel Gambini, María Juliana |
| author_role |
author |
| author2 |
Gambini, María Juliana |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas Algorithms finite difference numerical approximation |
| topic |
Ciencias Informáticas Algorithms finite difference numerical approximation |
| dc.description.none.fl_txt_mv |
In this article we consider the problem of computing approximations to the second derivatives of functions of n variables using finite differences. We show how to derive different formulas and how to comput the errors of those approximations as functions of the increment h, both for first and second derivatives. Based upon those results we describe the methods of Gill and Murray and the one of gradient difference. On the other hand we introduce a new algorithm which use conjugate directions methods for minimizing functions without derivatives and the corresponding numerical comparisons with the other two methods. Finally, numerical experiences are given and the corresponding conclusions are discussed. Facultad de Informática |
| description |
In this article we consider the problem of computing approximations to the second derivatives of functions of n variables using finite differences. We show how to derive different formulas and how to comput the errors of those approximations as functions of the increment h, both for first and second derivatives. Based upon those results we describe the methods of Gill and Murray and the one of gradient difference. On the other hand we introduce a new algorithm which use conjugate directions methods for minimizing functions without derivatives and the corresponding numerical comparisons with the other two methods. Finally, numerical experiences are given and the corresponding conclusions are discussed. |
| publishDate |
2001 |
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2001 |
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eng |
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eng |
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openAccess |
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