| Details | | Publication Date: | 2001-04-16 | | Series: | Wiley-Interscience Series in Discrete Mathematics and Optimization | | Edition Description: | Illustrated |
| Size | | Length: | 551 pages | | Height: | 9.3 in | | Width: | 6.5 in | | Thickness: | 1.2 in | | Weight: | 32.8 oz |
Publisher's Note
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.
While most algorithm designs are finalized toward worst case scenarios where they have to cope efficiently with unrealistic inputs, the average case solution is a probabilistic approach that allows for the possibility that a simple algorithm would suffice. This book provides a unique overview of the tools and techniques used in average case analysis of algorithms.
Industry Reviews
"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." (<I>SciTech Book News</I>, Vol. 25, No. 3, September 2001)
<P>"...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)
<P>"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)
<P>"...a textbook intended for graduate students...as well as a reference for researchers." (Quarterly of Applied Mathematics, Vol. LX, No. 2, June 2002)
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