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Linear Programming Using MATLAB® / by Nikolaos Ploskas, Nikolaos Samaras.
Springer Nature - Springer Mathematics and Statistics eBooks 2017 English International Available online
View online- Format:
- Book
- Author/Creator:
- Ploskas, Nikolaos, Author.
- Samaras, Nikolaos, Author.
- Series:
- Springer Optimization and Its Applications, 1931-6836 ; 127
- Language:
- English
- Subjects (All):
- Mathematical optimization.
- Computer software.
- Computer science—Mathematics.
- Algorithms.
- Continuous Optimization.
- Mathematical Software.
- Mathematical Applications in Computer Science.
- Local Subjects:
- Continuous Optimization.
- Mathematical Software.
- Mathematical Applications in Computer Science.
- Algorithms.
- Physical Description:
- 1 online resource (XVII, 637 p. 59 illus., 47 illus. in color.)
- Edition:
- 1st ed. 2017.
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2017.
- Summary:
- 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.
- Contents:
- 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.
- Notes:
- Includes bibliographical references at the end of each chapters and index.
- ISBN:
- 3-319-65919-7
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