Linear Programming: Foundations and Extensions

Linear Programming: Foundations and Extensions

by Robert J. Vanderbei
Linear Programming: Foundations and Extensions

Linear Programming: Foundations and Extensions

by Robert J. Vanderbei

Paperback(5th ed. 2020)

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Overview

Linear Programming: Foundations and Extensions, Third Edition is an introduction to the field of optimization. The book emphasizes constrained optimization, beginning with a substantial treatment of linear programming, and proceeds to cover convex analysis, network flows, integer programming, quadratic programming, and convex optimization. The book is carefully written with specific examples and concrete algorithms preceding more abstract topics. Topics are clearly developed with a large number of numerical examples worked out in detail.

Moreover, Linear Programming: Foundations and Extensions, Third Edition underscores the purpose of optimization-to solve practical problems on a computer. Accordingly, the book is coordinated with free efficient C programs that implement the major algorithms studied: The two-phase simplex method, The primal-dual simplex method, The path-following interior-point method, The homogeneous self-dual methods. In addition, there are online JAVA applets which illustrate various pivot rules and variants of the simplex method, both for linear programming and for network flows. These C programs and JAVA tools can be found on the books webpage: http://www.princeton.edu/-rvdb/LPbook/. Also check the book's webpage for new online instructional tools and exercises that have been added in the new edition.


Product Details

ISBN-13: 9783030394172
Publisher: Springer International Publishing
Publication date: 04/26/2020
Series: International Series in Operations Research & Management Science , #285
Edition description: 5th ed. 2020
Pages: 471
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Robert J. Vanderbei is Professor of Operations Research and Financial Engineering, and former Department Chair, OR and Financial Engineering at Princeton University. His research interests are in algorithms for nonlinear optimization and their application to problems arising in engineering and science. Application areas of interest focus mainly on inverse Fourier transform optimization problems and action minimization problems with a special interest in applying these techniques to the design of NASA’s terrestrial planet finder space telescope.

Table of Contents

Chapter 1. Introduction.- Chapter 2. The Simplex Method.- Chapter 3. Degeneracy.- Chapter 4. Efficiency of the Simplex Method.- Chapter 5. Duality Theory.- Chapter 6. The Simplex Method in Matrix Notational.- Chapter 7. Sensitivity and Parametric Analyses.- Chapter 8. Implementation Issues.- Chapter 9. Problems in General Form.- Chapter 10. Convex Analysis.- Chapter 11. Game Theory.- Chapter 12. Data Science Applications.- Chapter 13. Financial Applications.- Chapter 14. Network Flow Problems.- Chapter 15. Applications.- Chapter 16. Structural Optimization.- Chapter 17. The Central Path.- Chapter 18. A Path-Following Method.- Chapter 19. The KKT System.- Chapter 20. Implementation Issues.- Chapter 21. The Affine-Scaling Method.- Chapter 22. The Homogeneous Self-Dual Method.- Chapter 23. Integer Programming.- Chapter 24. Quadratic Programming.- Chapter 25. Convex Programming.
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