Average Case Analysis of Algorithms on Sequences: 50
Author(s): Wojciech Szpankowski (Author)
Publisher: Wiley-Interscience
Publication Date: 24 April 2001
Edition: 1st
Language: English
Print length: 576 pages
ISBN-10: 9780471240631
ISBN-13: 9780471240631
Book Description
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. * 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.
Editorial Reviews
Review
“Surveying the major techniques of average case analysis, this graduate textbook presents both analytical methods used for well-structured algorithms and probabilistic methods used for more structurally complex algorithms.” (SciTech Book News, Vol. 25, No. 3, September 2001) “…contains a comprehensive treatment on probabilistic, combinatorial, and analytical techniques and methods…treatment is clear, rigorous, self-contained, with many examples and exercises.” (Zentralblatt MATH Vol. 968, 2001/18) “This well-organized book…is certainly useful…It is a valuable source for a deeper and more precise understanding of the behaviors of algorithms on sequences.” (Mathematical Reviews, 2002f)
From the Inside Flap
Comprehensive presentation of both analytic and probabilistic techniques
As a comprehensive survey of the major techniques of average case analysis, this work presents, in detail, both analytic methods used for well-structured algorithms and probabilistic methods used for more structurally complex algorithms. In particular, the applications in the book use algorithms that focus on data structures on sequences, also called strings, which are widely used in computer science, computational biology, and information theory. Specific techniques covered include the inclusion-exclusion principle, the first and second moment methods, the random coding technique, the subadditive ergodic theorem, large deviations, generating functions, complex asymptotic methods, the Mellin transform, and analytic poissonization and depoissonization. Each method is clearly explained and accompanied by related applications and problems involving algorithms on sequences.
Important features of the book include: * A foreword by well-known expert Dr. Philippe Flajolet, INRIA, France * Presentation of complex analysis used to solve discrete and probabilistic problems on sequences * Discussions of Lempel-Ziv data compression-schemes, the string edit problem, pattern matching algorithms, many variations of digital trees, the leader election algorithm, and more * A chapter devoted to tools used in information theory, particularly the random coding technique and pattern matching approach to data compression * Application sections in each chapter that illustrate the methods covered * An extensive bibliography
From the Back Cover
Comprehensive presentation of both analytic and probabilistic techniques
As a comprehensive survey of the major techniques of average case analysis, this work presents, in detail, both analytic methods used for well-structured algorithms and probabilistic methods used for more structurally complex algorithms. In particular, the applications in the book use algorithms that focus on data structures on sequences, also called strings, which are widely used in computer science, computational biology, and information theory. Specific techniques covered include the inclusion-exclusion principle, the first and second moment methods, the random coding technique, the subadditive ergodic theorem, large deviations, generating functions, complex asymptotic methods, the Mellin transform, and analytic poissonization and depoissonization. Each method is clearly explained and accompanied by related applications and problems involving algorithms on sequences.
Important features of the book include: * A foreword by well-known expert Dr. Philippe Flajolet, INRIA, France * Presentation of complex analysis used to solve discrete and probabilistic problems on sequences * Discussions of Lempel-Ziv data compression-schemes, the string edit problem, pattern matching algorithms, many variations of digital trees, the leader election algorithm, and more * A chapter devoted to tools used in information theory, particularly the random coding technique and pattern matching approach to data compression * Application sections in each chapter that illustrate the methods covered * An extensive bibliography
About the Author
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.
Wow! eBook
