Overview
- Methodically presents all components of the simplex-type methods?
- Enables readers to experiment with MATLAB® codes that are able to solve large-scale benchmark linear programs?
- Contains 11 presolve techniques, 11 scaling techniques, 6 pivoting rules, and 4 basis inverse and update methods
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Optimization and Its Applications (SOIA, volume 127)
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
- MATLAB linear programming
- linear programming algorithms
- parametric programming
- scaling techniques
- sensitivity analysis
- simplex algorithm
- Linear Programming Problem
- Convert MAT2MPS
- Geometry of Linear Programming Problems
- Convert MPS2MAT
- Presolve Methods
- Gauss-Jordan Elimination
- matlab Optimization toolbox
- Pivoting Rules
- matlab toolbox
- Revised Dual Simplex Algorithm
- Exterior Point Simplex Algorithm
- Revised Primal Simplex Algorithm
- Interior Point Methods
- Sensitivity Analysis
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Linear Programming Using MATLAB®
Authors: Nikolaos Ploskas, Nikolaos Samaras
Series Title: Springer Optimization and Its Applications
DOI: https://doi.org/10.1007/978-3-319-65919-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-65917-6Published: 10 November 2017
Softcover ISBN: 978-3-319-88131-7Published: 25 August 2018
eBook ISBN: 978-3-319-65919-0Published: 28 October 2017
Series ISSN: 1931-6828
Series E-ISSN: 1931-6836
Edition Number: 1
Number of Pages: XVII, 637
Number of Illustrations: 12 b/w illustrations, 47 illustrations in colour
Topics: Continuous Optimization, Mathematical Software, Math Applications in Computer Science, Algorithms