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Linear Programming Using MATLAB® [electronic resource] / by Nikolaos Ploskas, Nikolaos Samaras.

By: Ploskas, Nikolaos [author.].
Contributor(s): Samaras, Nikolaos [author.] | SpringerLink (Online service).
Material type: TextTextSeries: Springer Optimization and Its Applications: 127Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Description: XVII, 637 p. 59 illus., 47 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319659190.Subject(s): Computer software | Computer science | Algorithms | Continuous Optimization | Mathematical Software | Math Applications in Computer Science | AlgorithmsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 519.6 Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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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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.

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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