Linear Programming Computation (PDF)
(Sprache: Englisch)
With emphasis on computation, this book is a real breakthrough in the field of LP. In addition to conventional topics, such as the simplex method, duality, and interior-point methods, all deduced in a fresh and clear manner, it introduces the state of the...
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Produktinformationen zu „Linear Programming Computation (PDF)“
With emphasis on computation, this book is a real breakthrough in the field of LP. In addition to conventional topics, such as the simplex method, duality, and interior-point methods, all deduced in a fresh and clear manner, it introduces the state of the art by highlighting brand-new and advanced results, including efficient pivot rules, Phase-I approaches, reduced simplex methods, deficient-basis methods, face methods, and pivotal interior-point methods. In particular, it covers the determination of the optimal solution set, feasible-point simplex method, decomposition principle for solving large-scale problems, controlled-branch method based on generalized reduced simplex framework for solving integer LP problems.
Bibliographische Angaben
- Autor: Ping-Qi PAN
- 2014, 2014, 747 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3642407544
- ISBN-13: 9783642407543
- Erscheinungsdatum: 27.03.2014
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Dateiformat: PDF
- Größe: 6.07 MB
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Englisch
Pressezitat
From the book reviews:“The book seems to be mainly addressed to scientists who already possess some expertise in LP. The kind of presentation, however, also allows using parts of it as a basis for a course on the topic. In fact, a special feature of the book is that an algorithm typically is accompanied by some example for which the results of all computational steps needed to find a solution are written down.” (Rembert Reemtsen, zbMATH, Vol. 1302, 2015)“This book is a research monograph focusing on computational techniques in the simplex method for linear programming. … It may be of interest to researchers and developers of simplex method codes for linear programming.” (B. Borchers, Choice, Vol. 52 (3), November, 2014)
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