Linear Programming using MATLAB? (Springer Optimization and Its Applications) von Ploskas, Nikolaos; Samaras, Nikolaos: New (2017)

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pp. 620.

Bestandsnummer des Verk�ufers 26375440360

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Bibliografische Details

Titel: Linear Programming using MATLAB? (Springer Optimization and Its Applications)
Verlag: Springer
Ver�ffentlichungsdatum: 2017
Sprache: Englisch
ISBN-10: 3319659170
ISBN-13: 9783319659176
Bindung: Hardcover
Zustand: New

�ber diesen Titel

Inhaltsangabe

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.

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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. 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.

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