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03371nam a22005175i 4500 |
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978-3-319-65919-0 |
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DE-He213 |
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20171030081340.0 |
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cr nn 008mamaa |
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171030s2017 gw | s |||| 0|eng d |
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|a 9783319659190
|9 978-3-319-65919-0
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|a 10.1007/978-3-319-65919-0
|2 doi
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|d GrThAP
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|a QA402.5-402.6
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|a PBU
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|a MAT003000
|2 bisacsh
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|a 519.6
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|a Ploskas, Nikolaos.
|e author.
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|a Linear Programming Using MATLAB®
|h [electronic resource] /
|c by Nikolaos Ploskas, Nikolaos Samaras.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XVII, 637 p. 59 illus., 47 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Springer Optimization and Its Applications,
|x 1931-6828 ;
|v 127
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| 505 |
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|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.
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|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.
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| 650 |
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|a Mathematics.
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| 650 |
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|a Computer science
|x Mathematics.
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| 650 |
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|a Algorithms.
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| 650 |
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|a Computer software.
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| 650 |
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|a Mathematical optimization.
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| 650 |
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4 |
|a Mathematics.
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| 650 |
2 |
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|a Continuous Optimization.
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| 650 |
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4 |
|a Mathematical Software.
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| 650 |
2 |
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|a Math Applications in Computer Science.
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| 650 |
2 |
4 |
|a Algorithms.
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| 700 |
1 |
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|a Samaras, Nikolaos.
|e author.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9783319659176
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| 830 |
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|a Springer Optimization and Its Applications,
|x 1931-6828 ;
|v 127
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-3-319-65919-0
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-SMA
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| 950 |
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|a Mathematics and Statistics (Springer-11649)
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