Sandeep Sen

Design and Analysis of Algorithms (eBook, PDF)

A Contemporary Perspective

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 23. Mai 2019
• Englisch
• ISBN-13: 9781108576895
• Artikelnr.: 66178099

Autorenporträt

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.

Inhaltsangabe

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.