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- Produkterinnerung
- Produkterinnerung
Focuses on the interplay between algorithm design and the underlying computational models.
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Focuses on the interplay between algorithm design and the underlying computational models.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 394
- Erscheinungstermin: 28. Mai 2019
- Englisch
- Abmessung: 260mm x 208mm x 26mm
- Gewicht: 1067g
- ISBN-13: 9781108496827
- ISBN-10: 1108496822
- Artikelnr.: 53213452
- Verlag: Cambridge University Press
- Seitenzahl: 394
- Erscheinungstermin: 28. Mai 2019
- Englisch
- Abmessung: 260mm x 208mm x 26mm
- Gewicht: 1067g
- ISBN-13: 9781108496827
- ISBN-10: 1108496822
- Artikelnr.: 53213452
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.
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.
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.
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.
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.