Linear Programming Using MATLAB®

Springer Optimization and Its Applications Band 127

Nikolaos Ploskas, Nikolaos Samaras

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Beschreibung


This book offers a theoretical and computational presentation of a variety of linear programming algorithms and methods with an emphasis on the revised simplex method and its components. A theoretical background and mathematical formulation is included for each algorithm as well as comprehensive numerical examples and corresponding MATLAB® code. The MATLAB® implementations presented in this book  are sophisticated and allow users to find solutions to large-scale benchmark linear programs. Each algorithm is followed by a computational study on benchmark problems that analyze the computational behavior of the presented algorithms.

As a solid companion to existing algorithmic-specific literature, this book will be useful to researchers, scientists, mathematical programmers, and students with a basic knowledge of linear algebra and calculus.  The clear presentation enables the reader to understand and utilize all components of simplex-type methods, such as presolve techniques, scaling techniques, pivoting rules, basis update methods, and sensitivity analysis.

Produktdetails

Einband Taschenbuch
Erscheinungsdatum 25.08.2018
Verlag Springer
Seitenzahl 637
Maße (L/B/H) 23,5/15,5/3,5 cm
Gewicht 997 g
Auflage Softcover reprint of the original 1st ed. 2017
Sprache Englisch
ISBN 978-3-319-88131-7

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  • Linear Programming Using MATLAB®
  • 1. Introduction.- 2. Linear Programming Algorithms.- 3. Linear Programming Benchmark and Random Problems.- 4. Presolve Methods.- 5. Scaling Techniques.- 6. Pivoting Rules.- 7. Basis Inverse and  Update Methods.- 8. Revised Primal Simplex Algorithm.- 9. Exterior Point Simplex Algorithms.- 10. Interior Point Method.- 11. Sensitivity Analysis.- Appendix: MATLAB's Optimization Toolbox Algorithms.-  Appendix: State-of-the-art Linear Programming Solvers;CLP and CPLEX.