Linear Programming Using MATLAB®

  • Nikolaos Ploskas
  • Nikolaos Samaras

Part of the Springer Optimization and Its Applications book series (SOIA, volume 127)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Nikolaos Ploskas, Nikolaos Samaras
    Pages 1-11
  3. Nikolaos Ploskas, Nikolaos Samaras
    Pages 13-71
  4. Nikolaos Ploskas, Nikolaos Samaras
    Pages 73-134
  5. Nikolaos Ploskas, Nikolaos Samaras
    Pages 135-217
  6. Nikolaos Ploskas, Nikolaos Samaras
    Pages 219-275
  7. Nikolaos Ploskas, Nikolaos Samaras
    Pages 277-302
  8. Nikolaos Ploskas, Nikolaos Samaras
    Pages 303-328
  9. Nikolaos Ploskas, Nikolaos Samaras
    Pages 329-381
  10. Nikolaos Ploskas, Nikolaos Samaras
    Pages 383-435
  11. Nikolaos Ploskas, Nikolaos Samaras
    Pages 437-490
  12. Nikolaos Ploskas, Nikolaos Samaras
    Pages 491-540
  13. Nikolaos Ploskas, Nikolaos Samaras
    Pages 541-563
  14. Nikolaos Ploskas, Nikolaos Samaras
    Pages E1-E3
  15. Back Matter
    Pages 565-637

About this book

Introduction

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.

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

Authors and affiliations

  • Nikolaos Ploskas
    • 1
  • Nikolaos Samaras
    • 2
  1. 1.Department of Applied InformaticsUniversity of MacedoniaThessalonikiGreece
  2. 2.Department of Applied InformaticsUniversity of MacedoniaThessalonikiGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-65919-0
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-65917-6
  • Online ISBN 978-3-319-65919-0
  • Series Print ISSN 1931-6828
  • Series Online ISSN 1931-6836
  • About this book
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