Articles liés à Conjugate Gradient Algorithms in Nonconvex Optimization
Synopsis
Conjugate direction methods were proposed in the early 1950s. When high speed digital computing machines were developed, attempts were made to lay the fo- dations for the mathematical aspects of computations which could take advantage of the ef?ciency of digital computers. The National Bureau of Standards sponsored the Institute for Numerical Analysis, which was established at the University of California in Los Angeles. A seminar held there on numerical methods for linear equationswasattendedbyMagnusHestenes, EduardStiefel andCorneliusLanczos. This led to the ?rst communication between Lanczos and Hestenes (researchers of the NBS) and Stiefel (of the ETH in Zurich) on the conjugate direction algorithm. The method is attributed to Hestenes and Stiefel who published their joint paper in 1952 [101] in which they presented both the method of conjugate gradient and the conjugate direction methods including conjugate Gram-Schmidt processes. A closelyrelatedalgorithmwasproposedbyLanczos[114]whoworkedonalgorithms for determiningeigenvalues of a matrix. His iterative algorithm yields the similarity transformation of a matrix into the tridiagonal form from which eigenvalues can be well approximated.Thethree-termrecurrencerelationofthe Lanczosprocedurecan be obtained by eliminating a vector from the conjugate direction algorithm scheme. Initially the conjugate gradient algorithm was called the Hestenes-Stiefel-Lanczos method [86].
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Présentation de l'éditeur
This book details algorithms for large-scale unconstrained and bound constrained optimization. It shows optimization techniques from a conjugate gradient algorithm perspective as well as methods of shortest residuals, which have been developed by the author.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Conjugate Gradient Algorithms in Nonconvex Optimization (Nonconvex Optimization and Its Applications, 89) [Hardcover] Pytlak, Radoslaw (eng)
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Conjugate Gradient Algorithms in Nonconvex Optimization
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Springer Berlin Heidelberg Dez 2008, 2008
ISBN 10 : 3540856331
ISBN 13 : 9783540856337
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 504 pp. Englisch. N° de réf. du vendeur 9783540856337
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Conjugate Gradient Algorithms in Nonconvex Optimization
Edité par
Springer Berlin Heidelberg, 2008
ISBN 10 : 3540856331
ISBN 13 : 9783540856337
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 Residua. N° de réf. du vendeur 4901546
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Conjugate Gradient Algorithms in Nonconvex Optimization
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783540856337
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Conjugate Gradient Algorithms in Nonconvex Optimization
Edité par
Springer, Springer Spektrum Dez 2008, 2008
ISBN 10 : 3540856331
ISBN 13 : 9783540856337
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 504 pp. Englisch. N° de réf. du vendeur 9783540856337
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Conjugate Gradient Algorithms in Nonconvex Optimization
Edité par
Springer, 2008
ISBN 10 : 3540856331
ISBN 13 : 9783540856337
Ancien ou d'occasion
Couverture rigide
Vendeur : Buchpark, Trebbin, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : Sehr gut. Zustand: Sehr gut | Seiten: 504 | Sprache: Englisch | Produktart: Bücher | Conjugate direction methods were proposed in the early 1950s. When high speed digital computing machines were developed, attempts were made to lay the fo- dations for the mathematical aspects of computations which could take advantage of the ef?ciency of digital computers. The National Bureau of Standards sponsored the Institute for Numerical Analysis, which was established at the University of California in Los Angeles. A seminar held there on numerical methods for linear equationswasattendedbyMagnusHestenes, EduardStiefel andCorneliusLanczos. This led to the ?rst communication between Lanczos and Hestenes (researchers of the NBS) and Stiefel (of the ETH in Zurich) on the conjugate direction algorithm. The method is attributed to Hestenes and Stiefel who published their joint paper in 1952 [101] in which they presented both the method of conjugate gradient and the conjugate direction methods including conjugate Gram¿Schmidt processes. A closelyrelatedalgorithmwasproposedbyLanczos[114]whoworkedonalgorithms for determiningeigenvalues of a matrix. His iterative algorithm yields the similarity transformation of a matrix into the tridiagonal form from which eigenvalues can be well approximated.Thethree-termrecurrencerelationofthe Lanczosprocedurecan be obtained by eliminating a vector from the conjugate direction algorithm scheme. Initially the conjugate gradient algorithm was called the Hestenes¿Stiefel¿Lanczos method [86]. N° de réf. du vendeur 4681506/12
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Conjugate Gradient Algorithms in Nonconvex Optimization
Edité par
Springer, 2008
ISBN 10 : 3540856331
ISBN 13 : 9783540856337
Ancien ou d'occasion
Couverture rigide
Vendeur : Buchpark, Trebbin, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : Hervorragend. Zustand: Hervorragend | Seiten: 504 | Sprache: Englisch | Produktart: Bücher | Conjugate direction methods were proposed in the early 1950s. When high speed digital computing machines were developed, attempts were made to lay the fo- dations for the mathematical aspects of computations which could take advantage of the ef?ciency of digital computers. The National Bureau of Standards sponsored the Institute for Numerical Analysis, which was established at the University of California in Los Angeles. A seminar held there on numerical methods for linear equationswasattendedbyMagnusHestenes, EduardStiefel andCorneliusLanczos. This led to the ?rst communication between Lanczos and Hestenes (researchers of the NBS) and Stiefel (of the ETH in Zurich) on the conjugate direction algorithm. The method is attributed to Hestenes and Stiefel who published their joint paper in 1952 [101] in which they presented both the method of conjugate gradient and the conjugate direction methods including conjugate Gram¿Schmidt processes. A closelyrelatedalgorithmwasproposedbyLanczos[114]whoworkedonalgorithms for determiningeigenvalues of a matrix. His iterative algorithm yields the similarity transformation of a matrix into the tridiagonal form from which eigenvalues can be well approximated.Thethree-termrecurrencerelationofthe Lanczosprocedurecan be obtained by eliminating a vector from the conjugate direction algorithm scheme. Initially the conjugate gradient algorithm was called the Hestenes¿Stiefel¿Lanczos method [86]. N° de réf. du vendeur 4681506/1
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Conjugate Gradient Algorithms in Nonconvex Optimization
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Conjugate Gradient Algorithms in Nonconvex Optimization (Nonconvex Optimization and Its Applications)
Edité par
Springer, 2008
ISBN 10 : 3540856331
ISBN 13 : 9783540856337
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Conjugate Gradient Algorithms in Nonconvex Optimization
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Etat : New. PRINT ON DEMAND pp. 504. N° de réf. du vendeur 18440755
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