|
|
|
|
| LEADER |
02769nmm a2200349 u 4500 |
| 001 |
EB001652275 |
| 003 |
EBX01000000000000000954950 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
171103 ||| eng |
| 020 |
|
|
|a 9783319659190
|
| 100 |
1 |
|
|a Ploskas, Nikolaos
|
| 245 |
0 |
0 |
|a Linear Programming Using MATLAB®
|h Elektronische Ressource
|c by Nikolaos Ploskas, Nikolaos Samaras
|
| 250 |
|
|
|a 1st ed. 2017
|
| 260 |
|
|
|a Cham
|b Springer International Publishing
|c 2017, 2017
|
| 300 |
|
|
|a XVII, 637 p. 59 illus., 47 illus. in color
|b online resource
|
| 505 |
0 |
|
|a 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.
|
| 653 |
|
|
|a Computer science / Mathematics
|
| 653 |
|
|
|a Continuous Optimization
|
| 653 |
|
|
|a Algorithms
|
| 653 |
|
|
|a Mathematical Applications in Computer Science
|
| 653 |
|
|
|a Computer software
|
| 653 |
|
|
|a Mathematical optimization
|
| 653 |
|
|
|a Mathematical Software
|
| 700 |
1 |
|
|a Samaras, Nikolaos
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a Springer Optimization and Its Applications
|
| 028 |
5 |
0 |
|a 10.1007/978-3-319-65919-0
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-65919-0?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 519.6
|
| 520 |
|
|
|a 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
|