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Conjugate Gradient Algorithms in Nonconvex Optimization (Nonconvex Optimization and Its Applications, 89) - Softcover
Synopsis
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.
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From the Back Cover
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.
"About this title" may belong to another edition of this title.
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Pytlak:Conjugate Gradient Algorithms in (eng)
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Conjugate Gradient Algorithms in Nonconvex Optimization
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Conjugate Gradient Algorithms in Nonconvex Optimization
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ISBN 10: 3642099254
ISBN 13: 9783642099250
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Taschenbuch. Condition: 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. Seller Inventory # 9783642099250
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Conjugate Gradient Algorithms in Nonconvex Optimization
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Springer Berlin Heidelberg, 2010
ISBN 10: 3642099254
ISBN 13: 9783642099250
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Condition: 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. Seller Inventory # 5048913
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Conjugate Gradient Algorithms in Nonconvex Optimization
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Taschenbuch. Condition: Neu. Conjugate Gradient Algorithms in Nonconvex Optimization | Radoslaw Pytlak | Taschenbuch | Nonconvex Optimization and Its Applications | xxvi | Englisch | 2010 | Springer | EAN 9783642099250 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 107135824
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Conjugate Gradient Algorithms in Nonconvex Optimization
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Taschenbuch. Condition: 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. Seller Inventory # 9783642099250
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Taschenbuch. Condition: 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 Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg 504 pp. Englisch. Seller Inventory # 9783642099250
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Conjugate Gradient Algorithms in Nonconvex Optimization (Nonconvex Optimization and Its Applications, 89)
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