Linear programming using MATLAB® /

Linear programming using MATLAB® /

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Bibliographic Details
Author / Creator:	Ploskas, Nikolaos, author.
Imprint:	Cham : Springer, 2017.
Description:	1 online resource (xvii, 637 pages) : illustrations (some color)
Language:	English
Series:	Springer Optimization and Its Applications, 1931-6828 ; v. 127 Springer optimization and its applications ; v. 127.
Subject:	Linear programming. Mathematics. Computer science -- Mathematics. Algorithms. Computer software. Mathematical optimization. Algorithms. Computer science -- Mathematics. Computer software. Linear programming. Mathematical optimization. Mathematics.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11389026

Hidden Bibliographic Details
Other authors / contributors:	Samaras, Nikolaos, author.
ISBN:	9783319659190 3319659197 9783319659176 3319659170
Digital file characteristics:	text file PDF
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.--
Other form:	Printed edition: 9783319659176
Standard no.:	10.1007/978-3-319-65919-0

Table of 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.