Design and Analysis of Algorithms
A Contemporary Perspective
Will be published approx. on 31. July 2019
Book
Paperback/Softback
350 pages
978-1-108-72199-8 (ISBN)
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Description
The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.
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05/2019
Cambridge University Press
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Persons
Author
Indian Institute of Technology, Delhi
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.
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.
Indian Institute of Technology, Delhi
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.
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.
Content
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.