Linear Programming Using MATLAB®

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 num...

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Main Authors: Ploskas, Nikolaos, Samaras, Nikolaos (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2017, 2017
Edition:1st ed. 2017
Series:Springer Optimization and Its Applications
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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100 1 |a Ploskas, Nikolaos 
245 0 0 |a Linear Programming Using MATLAB®  |h Elektronische Ressource  |c by Nikolaos Ploskas, Nikolaos Samaras 
250 |a 1st ed. 2017 
260 |a Cham  |b Springer International Publishing  |c 2017, 2017 
300 |a XVII, 637 p. 59 illus., 47 illus. in color  |b online resource 
505 0 |a 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. 
653 |a Computer science—Mathematics 
653 |a Continuous Optimization 
653 |a Math Applications in Computer Science 
653 |a Algorithms 
653 |a Algorithms 
653 |a Computer software 
653 |a Mathematical optimization 
653 |a Mathematical Software 
700 1 |a Samaras, Nikolaos  |e [author] 
710 2 |a SpringerLink (Online service) 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
490 0 |a Springer Optimization and Its Applications 
856 |u https://doi.org/10.1007/978-3-319-65919-0?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 519.6 
520 |a 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