Linear programming using MATLAB® / Nikolaos Ploskas, Nikolaos Samaras.
Ploskas, Nikolaos, author.; Samaras, Nikolaos, author.
2017
T57.74
Available Online

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Title
Linear programming using MATLAB® / Nikolaos Ploskas, Nikolaos Samaras.
Author
Ploskas, Nikolaos, author.
ISBN
9783319659190 (electronic book)
3319659197 (electronic book)
9783319659176
3319659170
DOI
https://doi.org/10.1007/978-3-319-65919-0
Published
Cham : Springer, 2017.
Language
English
Description
1 online resource (xvii, 637 pages) : illustrations.
Item Number
10.1007/978-3-319-65919-0 doi
Call Number
T57.74
Dewey Decimal Classification
519.7/2
519.6
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.-- Provided by publisher.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Added Author
Samaras, Nikolaos, author.
Series
Springer optimization and its applications ; v. 127.
Available in Other Form
Print version: 9783319659176
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Online Access
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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.

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