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Linear and nonlinear optimization

Author: Igor Griva; Stephen Nash; Ariela Sofer; Society for Industrial and Applied Mathematics.
Publisher: Philadelphia, Pa. : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), 2009.
Edition/Format:   eBook : Document : English : 2nd edView all editions and formats
Summary:
Provides an introduction to the applications, theory, and algorithms of linear and nonlinear optimization. The emphasis is on practical aspects - discussing modern algorithms, as well as the influence of theory on the interpretation of solutions or on the design of software. The book includes several examples of realistic optimization models that address important applications. The succinct style of this second  Read more...
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Details

Additional Physical Format: Print version:
(DLC) 2008032477
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Igor Griva; Stephen Nash; Ariela Sofer; Society for Industrial and Applied Mathematics.
ISBN: 9780898717730 0898717736
OCLC Number: 693957867
Description: 1 online resource (xxii, 742 pages : illustrations)
Contents: 1. Optimization models --
2. Fundamentals of optimization --
3. Representation of linear constraints --
4. Geometry of linear programming --
5. The simplex method --
6. Duality and sensitivity --
7. Enhancements of the simplex method --
8. Network problems --
9. Computational complexity of linear programming --
10. Interior-point methods of linear programming --
11. Basics of unconstrained optimization --
12. Methods for unconstrained optimization --
13. Low-storage methods for unconstrained problems --
14. Optimality conditions for constrained problems --
15. Feasible-point methods --
16. Penalty and barrier methods.
Responsibility: Igor Griva, Stephen G. Nash, Ariela Sofer.

Abstract:

Provides an introduction to the applications, theory, and algorithms of linear and nonlinear optimization. The emphasis is on practical aspects - discussing modern algorithms, as well as the influence of theory on the interpretation of solutions or on the design of software. The book includes several examples of realistic optimization models that address important applications. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support-vector machines. The book is designed to be flexible. It has a modular structure, and uses consistent notation and terminology throughout. It can be used in many different ways, in many different courses, and at many different levels of sophistication.

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