Linear Programming Using MATLAB®. 1st ed. 2017

種類:

電子ブック

責任表示:

by Nikolaos Ploskas, Nikolaos Samaras

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2017

著者名:

• Ploskas, Nikolaos.
• Samaras, Nikolaos.
• SpringerLink (Online service)

シリーズ名:

Springer Optimization and Its Applications ; 127

ISBN:

9783319659190 [3319659197]    

注記:

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.
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,

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