Free Computer, Mathematics, Technical Books and Lecture Notes, etc.
- Title Average Case Analysis of Algorithms on Sequences
- Author(s) Wojciech Szpankowski
- Publisher: Wiley-Interscience; 1 edition (April 16, 2001)
- Hardcover 576 pages
- eBook PostScript files
- Language: English
- ISBN-10: 047124063X
- ISBN-13: 978-0471240631
- Share This:
A timely book on a topic that has witnessed a surge of interest over the last decade, owing in part to several novel applications, most notably in data compression and computational molecular biology. It describes methods employed in average case analysis of algorithms, combining both analytical and probabilistic tools in a single volume. This book is a comprehensive presentation of both analytic and probabilistic techniques
- Tools are illustrated through problems on words with applications to molecular biology, data compression, security, and pattern matching.
- Includes chapters on algorithms and data structures on words, probabilistic and analytical models, inclusion-exclusion principles, first and second moment methods, subadditive ergodic theorem and large deviations, elements of information theory, generating functions, complex asymptotic methods, Mellin transform and its applications, and analytic poissonization and depoissonization.
- Written by an established researcher with a strong international reputation in the field
- WOJCIECH SZPANKOWSKI, PhD, is Professor of Computer Science at Purdue University and has held visiting research positions at the Technical University of Gdansk, McGill University, INRIA, the Technical University of Vienna, University of Witwatersrand, Hewlett-Packard Laboratories, and Stanford University. He is the author of over 100 scientific publications in the areas of analysis of algorithms, information theory, performance evaluation of computer networks, stability of distributed systems, and queueing theory.
Reviews and Rating:
Related Book Categories:
- Data Structures and Algorithms
- Computational Complexity
- Operations Research (OR), Linear Programming, Optimization, and Approximation