Linear Programming

Foundations and Extensions

Häftad, Engelska, 2021

Av Robert J. Vanderbei

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With its focus on solving practical problems, the book features free C programs to implement the major algorithms covered, including the two-phase simplex method, the primal-dual simplex method, the path-following interior-point method, and and the homogeneous self-dual method.

Produktinformation

Utgivningsdatum2021-04-26
Mått155 x 235 x 36 mm
Vikt760 g
FormatHäftad
SpråkEngelska
SerieInternational Series in Operations Research & Management Science
Antal sidor471
Upplaga5
FörlagSpringer Nature Switzerland AG
ISBN9783030394172

Tillhör följande kategorier

Företagsekonomi inom Ekonomi och ledarskap
Programvaruutveckling inom Data och it
Ledarskap och motivation inom Ekonomi och ledarskap
Optimering inom Naturvetenskap och teknik

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.

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.