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Informationen zum Autor Sandeep Sen is Professor in the department of Computer Science and Engineering, Indian Institute of Technology (IIT), Delhi. He received his Ph.D. from Duke University, North Carolina, and M.S. from University of California, Santa Barabara. Prior to joining IIT Delhi, he served as a post-doctoral researcher at Bell Laboratories, Murray Hill, New Jersey and at Duke University, North Carolina. He served as visiting researcher at many reputed institutes including Max-Planck-Institut für Informatik, Germany, IBM Research Lab, Microsoft Research Lab, University of Newcastle, Australia, University of North Carolina, Chapel Hill, University of Connecticut and Simon Fraser University, Vancouver. With more than twenty-five years of teaching experience, his areas of interest include randomized algorithms, computational geometry and graph algorithms. Amit Kumar is Professor in the department of Computer Science and Engineering, Indian Institute of Technology (IIT) Delhi. He received his Ph.D. from Cornell University, New York. Prior to joining IIT Delhi, he worked as a member of technical staff at Bell Laboratories, New Jersey and has held several visiting professor positions at Microsoft Research India, IBM Research India and Max-Planck-Institut für Informatik, Germany. He has published over eighty research articles and holds five patents. His areas of interest include design of algorithms, approximation algorithms, computer networks and network management and routing. Klappentext Focuses on the interplay between algorithm design and the underlying computational models. Zusammenfassung A valuable text in the field of computer science and engineering, covering fundamental concepts and recent advancements. To help the reader to design/redesign algorithms for their requirements rather than be overawed by the challenges of a new framework. Inhaltsverzeichnis Preface; Acknowledgement; 1. Model and analysis; 2. Basics of probability and tail inequalities; 3. Warm up problems; 4. Optimization I: brute force and greedy strategy; 5. Optimization II: dynamic programming; 6. Searching; 7. Multidimensional searching and geometric algorithms; 8. String matching and finger printing; 9. Fast Fourier transform and applications; 10. Graph algorithms; 11. NP completeness and approximation algorithms; 12. Dimensionality reduction; 13. Parallel algorithms; 14. Memory hierarchy and caching; 15. Streaming data model; Appendix A. Recurrences and generating functions; Index....
