Linear and Nonlinear Optimization
(Sprache: Englisch)
Flexible graduate textbook that introduces the applications, theory, and algorithms of linear and nonlinear optimization.
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Flexible graduate textbook that introduces the applications, theory, and algorithms of linear and nonlinear optimization.
Klappentext zu „Linear and Nonlinear Optimization “
Flexible graduate textbook that introduces the applications, theory, and algorithms of linear and nonlinear optimization in a clear succinct style, supported by numerous examples and exercises. It introduces important realistic applications and explains how optimization can address them. 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.
Inhaltsverzeichnis zu „Linear and Nonlinear Optimization “
Preface; Part I. Basics: 1. Optimization models; 2. Fundamentals of optimization; 3. Representation of linear constraints; Part II. Linear Programming: 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; Part III. Unconstrained Optimization: 11. Basics of unconstrained optimization; 12. Methods for unconstrained optimization; 13. Low-storage methods for unconstrained problems; Part IV. Nonlinear Optimization: 14. Optimality conditions for constrained problems; 15. Feasible-point methods; 16. Penalty and barrier methods; Part V. Appendices: Appendix A. Topics from linear algebra; Appendix B. Other fundamentals; Appendix C. Software; Bibliography; Index.
Bibliographische Angaben
- Autoren: Igor Griva , Stephen G. Nash , Ariela Sofer
- 2009, 2. Aufl., 764 Seiten, Maße: 24,7 cm, Gebunden, Englisch
- Verlag: Cambridge University Press
- ISBN-10: 0898716616
- ISBN-13: 9780898716610
Sprache:
Englisch
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