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A comprehensive introduction to network flows that brings together the classic and the contemporary aspects of the field, and provides an integrative view of theory, algorithms, and applications.
- emphasizes powerful algorithmic strategies and analysis tools such as data scaling, geometric improvement arguments, and potential function arguments.
- provides an easy-to-understand descriptions of several important data structures, including d-heaps, Fibonacci heaps, and dynamic trees.
- devotes a special chapter to conducting empirical testing of algorithms.
- features over 150 applications of network flows to a variety of engineering, management, and scientific domains.
- contains extensive reference notes and illustrations.
Table of contents
2. Paths, Trees and Cycles.
3. Algorithm Design and Analysis.
4. Shortest Paths: Label Setting Algorithms.
5. Shortest Paths: Label Correcting Algorithms.
6. Maximum Flows: Basic Ideas.
7. Maximum Flows: Polynomial Algorithms.
8. Maximum Flows: Additional Topics.
9. Minimum Cost Flows: Basic Algorithms.
10. Minimum Cost Flows: Polynomial Algorithms.
11. Minimum Cost Flows: Network Simplex Algorithms.
12. Assignments and Matchings.
13. Minimum Spanning Trees.
14. Convex Cost Flows.
15. Generalized Flows.
16. Lagrangian Relaxation and Network Optimization.
17. Multicommodity Flows.
18. Computational Testing of Algorithms.
19. Additional Applications.
Appendix A: Data Structures.
Appendix B: NP-Completeness.
Appendix C: Linear Programming.
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