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springer-math/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 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. · GitHub

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This repository accompanies Linear Programming Using MATLAB® by Nikolaos Ploskas and Nikolaos Samaras (Springer, 2018).

Download the files as a zip using the green button, or clone the repository to your machine using Git.

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Release v1.0 corresponds to the code in the published book, without corrections or updates.

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For corrections to the content in the published book, see the file errata.md.

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See the file Contributing.md for more information on how you can contribute to this repository.

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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. T…

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