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

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

Zusammenfassung
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

Inhalt

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.