Nikolaos Ploskas, Nikolaos Samaras

Linear Programming Using MATLAB®

• Broschiertes Buch

Produktbeschreibung

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

• Verlag: Springer / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-88131-7
• Softcover reprint of the original 1st ed. 2017
• Seitenzahl: 660
• Erscheinungstermin: 25. August 2018
• Englisch
• Abmessung: 235mm x 155mm x 36mm
• Gewicht: 997g
• ISBN-13: 9783319881317
• ISBN-10: 3319881310
• Artikelnr.: 55723879

Inhaltsangabe

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.

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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.