Conjugate gradient algorithms in nonconvex optimization
This up-to-date book is on algorithms for large-scale unconstrained and bound constrained optimization. Optimization techniques are shown from a conjugate gradient algorithm perspective. Large part of the book is devoted to preconditioned conjugate gradient algorithms. In particular memoryless and l...
Shranjeno v:
| Glavni avtor: | |
|---|---|
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Izdaja: | 1st ed. 2009. |
| Serija: | Nonconvex Optimization and Its Applications
89 |
| Online dostop: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Sporočilo: |
TEXTE IMPRIME POUR L'EXEMPLAIRE (518.1 PYT c à la bibliothèque de l'ENSMP, Fontainebleau ParisTech) Description d'après consultation du 30 mars 2012 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Conjugate Gradient Algorithms in Nonconvex Optimization, Texte imprimé, 9783540856337 • Conjugate Gradient Algorithms in Nonconvex Optimization, Texte imprimé, 9783540929970 • Conjugate Gradient Algorithms in Nonconvex Optimization, Texte imprimé, 9783642099250 • Conjugate Gradient Algorithms in Nonconvex Optimization, Texte imprimé, 9783540856337 |
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| 009 | PPN131119389 | ||
| 020 | |a 9783540856344 | ||
| 041 | 0 | |a eng | |
| 082 | |a 515.64 | ||
| 084 | |a 90-02. 2010 | ||
| 084 | |a 65F10. 2010 | ||
| 084 | |a 65K10. 2010 | ||
| 084 | |a 90C30. 2010 | ||
| 084 | |a 90C52. 2010 | ||
| 084 | |a 90C53. 2010 | ||
| 100 | 1 | |a Pytlak, Radosław, |d 1956- | |
| 245 | 1 | 0 | |a Conjugate gradient algorithms in nonconvex optimization |c by Radosław Pytlak. |
| 250 | |a 1st ed. 2009. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 300 | |a 1 vol. (XXVI-477 p.) : |b Ill. ; |c 24 cm. | ||
| 490 | 0 | |a Nonconvex Optimization and Its Applications |v 89 | |
| 500 | |a TEXTE IMPRIME POUR L'EXEMPLAIRE (518.1 PYT c à la bibliothèque de l'ENSMP, Fontainebleau ParisTech) | ||
| 500 | |a Description d'après consultation du 30 mars 2012 | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 504 | |a Bibliogr. (p. 463-471) ; Index (p. 473-477) | ||
| 505 | 1 | |a Conjugate Direction Methods for Quadratic Problems Conjugate Gradient Methods for Nonconvex Problems Memoryless Quasi-Newton Methods Preconditioned Conjugate Gradient Algorithms Limited Memory Quasi-Newton Algorithms The Method of Shortest Residuals and Nondifferentiable Optimization The Method of Shortest Residuals for Differentiable Problems The Preconditioned Shortest Residuals Algorithm Optimization on a Polyhedron Conjugate Gradient Algorithms for Problems with Box Constraints Preconditioned Conjugate Gradient Algorithms for Problems with Box Constraints Preconditioned Conjugate Gradient Based Reduced-Hessian Methods | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a This up-to-date book is on algorithms for large-scale unconstrained and bound constrained optimization. Optimization techniques are shown from a conjugate gradient algorithm perspective. Large part of the book is devoted to preconditioned conjugate gradient algorithms. In particular memoryless and limited memory quasi-Newton algorithms are presented and numerically compared to standard conjugate gradient algorithms. The special attention is paid to the methods of shortest residuals developed by the author. Several effective optimization techniques based on these methods are presented. Because of the emphasis on practical methods, as well as rigorous mathematical treatment of their convergence analysis, the book is aimed at a wide audience. It can be used by researches in optimization, graduate students in operations research, engineering, mathematics and computer science. Practitioners can benefit from numerous numerical comparisons of professional optimization codes discussed in the book | ||
| 776 | 0 | |t Conjugate Gradient Algorithms in Nonconvex Optimization |b Texte imprimé |z 9783540856337 | |
| 776 | 0 | |t Conjugate Gradient Algorithms in Nonconvex Optimization |b Texte imprimé |z 9783540929970 | |
| 776 | 0 | |t Conjugate Gradient Algorithms in Nonconvex Optimization |b Texte imprimé |z 9783642099250 | |
| 776 | 0 | |t Conjugate Gradient Algorithms in Nonconvex Optimization |b Texte imprimé |z 9783540856337 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-3-540-85634-4 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-68P7DQL6-C |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:747851514 |u https://ezproxy.univ-orleans.fr/login?url=https://dx.doi.org/10.1007/978-3-540-85634-4 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:750869151 |u https://ezproxy.insa-cvl.fr/login?qurl=https://dx.doi.org/10.1007/978-3-540-85634-4 |z Accès INSA CVL | |
| 997 | |0 941002 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/Autre ressource numérique/ | ||