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Numerical Optimization (Springer Series in Operations Research and Financial Engineering) [Englisch] [Gebundene Ausgabe]

Jorge Nocedal , Stephen Wright
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27. Juli 2006 0387303030 978-0387303031 2nd ed. 2006
Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.

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  • Gebundene Ausgabe: 664 Seiten
  • Verlag: Springer; Auflage: 2nd ed. 2006 (27. Juli 2006)
  • Sprache: Englisch
  • ISBN-10: 0387303030
  • ISBN-13: 978-0387303031
  • Größe und/oder Gewicht: 25,8 x 18,5 x 4,4 cm
  • Durchschnittliche Kundenbewertung: 3.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 74.476 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
  • Komplettes Inhaltsverzeichnis ansehen



MMOR Mathematical Methods of Operations Research, 2001: "The books looks very suitable to be used in an graduate-level course in optimization for students in mathematics, operations research, engineering, and others. Moreover, it seems to be very helpful to do some self-studies in optimization, to complete own knowledge and can be a source of new ideas.... I recommend this excellent book to everyone who is interested in optimization problems."


"Numerical Optimization" presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition, the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.

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0 von 10 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen gut 9. Juni 2009
Format:Gebundene Ausgabe|Verifizierter Kauf
Es hat ca. ein Monat gedauert bis die Lieferung ankam, war aber auch aus der USA. Zustand aber einwandfrei
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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)
Amazon.com: 4.4 von 5 Sternen  14 Rezensionen
6 von 6 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen outstanding 15. Mai 2007
Von kelly londry - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe|Verifizierter Kauf
This book is a well-written, outstanding reference for anyone interested in understanding, using, and/or implementing state-of-the-art techniques in nonlinear optimization. Ample attention is paid to both constrained and unconstrained problem types, with a healthy and refreshing emphasis on trust-region strategies, and modern SQP and Interior-Point algorithms. Sufficient detail is paid to most topics while overall perspectives are well-maintained. This book is the very best of its kind for its intended audience. I strongly recommend it.
22 von 28 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Too much explanation, relative to the required background; some omissions in motivation 23. September 2006
Von Alexander C. Zorach - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
While I acknowledge the many good points that the other reviewers pointed out, I found this book less than "optimal" in a number of respects.

The text is very wordy and yet still sometimes lacks critical explanations. In particular, I found that the motivation for the ideas in earlier chapters is insufficient for the skeptical and questioning reader--one needs to put more trust in the author than I was comfortable with. The lines of reasoning used to motivate the methods are vague: Nocedal spends too much time talking about optimization from a distance. I would have appreciated a book that was more concise and that had more airtight reasoning, exploring questions more thoroughly.

I also feel that this book is impoverished with respect to algorithms. One does not encounter enough algorithms early on, and the book does not encourage enough experimentation. It also suffers from the very common "sin" among Numerical mathematics texts--it talks extensively about the convergence of algorithms before cultivating a deep understanding of those algorithms. The effect is that the reader gets bogged down with technical details. While the motivated reader can go off on her own and experiment to fill in these gaps and piece together the puzzle, I think most people who have this level of initiative and intellectual curiosity would be better served by a book that is more concise.

Following on this same theme, the level of explanation is not consistent with the level of background required to read the book. Some things are explained in a level of detail appropriate to an introductory undergraduate text, but the book requires substantial background in multivariable calculus and linear algebra. Someone without prior background in numerical linear algebra will probably find the notation in the book unintuitive and cumbersome; the appendices are of little help. But anyone with sufficient background to fully understand the material in this book will probably find it has too much explanation and moves too slowly.

I haven't found a better book on the topic yet; solving such an optimization problem seems to beyond the scope of the algorithms covered in this text. But I do feel confident that this book is not the best, due to the flaws I've mentioned above!
4 von 5 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen The best book for engineers that want to implement too 3. Oktober 2008
Von Nikolaos Vasiloglou - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe|Verifizierter Kauf
The book is quite complete and goes directly to the point. if you ever need optimization in your design you will find it here. Simple and well presented. It has enough details about algorithmic performance and description that should be enough to implement. It is a book that you will never regret having it in your library. If you want something more theoretical use Nonlinear Programming by Bertsekas. If you want to use optimization in your programs use this.
2 von 3 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Optimal textbook 3. Juni 2009
Von Z. Rafii - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe|Verifizierter Kauf
This textbook is kind of expensive (like many textbooks) but it is worthy. Everything about optimization is inside, well written and in details. And since everything is optimization, it can be really useful for all areas. I have just taken my final today in optimization with Nocedal as the instructor. He is as clear as his book, maybe more funny!
5.0 von 5 Sternen a classic 17. Juni 2014
Von Manchor Ko - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe|Verifizierter Kauf
The best text book on the various issues around steepest descent, conjugate gradient, Newtonian methods etc. Clearly show you why you still need to care about steepest-descent even though we were taught it is much slower than Newton or CG. Those that are practical oriented might have ignored the key role SD play in many methods to guarantee convergence (or progress).

Very good write up on the Wolfe condition, Cauchy point, and trust region.
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