Linear Programming Using MATLAB®: Springer Optimization and Its Applications, cartea 127
Autor Nikolaos Ploskas, Nikolaos Samarasen Limba Engleză Hardback – 10 noi 2017
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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Springer International Publishing – 10 noi 2017 | 824.65 lei 38-44 zile |
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Specificații
ISBN-13: 9783319659176
ISBN-10: 3319659170
Pagini: 637
Ilustrații: XVII, 637 p. 59 illus., 47 illus. in color. With online files/update.
Dimensiuni: 155 x 235 x 41 mm
Greutate: 1.1 kg
Ediția:2017
Editura: Springer International Publishing
Colecția Springer
Seria Springer Optimization and Its Applications
Locul publicării:Cham, Switzerland
ISBN-10: 3319659170
Pagini: 637
Ilustrații: XVII, 637 p. 59 illus., 47 illus. in color. With online files/update.
Dimensiuni: 155 x 235 x 41 mm
Greutate: 1.1 kg
Ediția:2017
Editura: Springer International Publishing
Colecția Springer
Seria Springer Optimization and Its Applications
Locul publicării:Cham, Switzerland
Cuprins
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.
Textul de pe ultima copertă
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.
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.
Caracteristici
Methodically presents all components of the simplex-type methods? Enables readers to experiment with MATLAB® codes that are able to solve large-scale benchmark linear programs? Contains 11 presolve techniques, 11 scaling techniques, 6 pivoting rules, and 4 basis inverse and update methods Includes supplementary material: sn.pub/extras