Introduction to the Analysis of Algorithms

ISBN-10: 032190575X
ISBN-13: 9780321905758
Edition: 2nd 2013
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Description: Despite growing interest in the mathematical analysis of algorithms, basic information on methods and models has rarely been directly accessible to practitioners, researchers, or students. This book organizes and presents that knowledge, fully  More...

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Book details

List price: $79.99
Edition: 2nd
Copyright year: 2013
Publisher: Addison Wesley Professional
Publication date: 1/18/2013
Binding: Hardcover
Pages: 604
Size: 7.75" wide x 9.25" long x 1.50" tall
Weight: 2.530
Language: English

Despite growing interest in the mathematical analysis of algorithms, basic information on methods and models has rarely been directly accessible to practitioners, researchers, or students. This book organizes and presents that knowledge, fully introducing today's primary techniques for mathematically analyzing algorithms. Robert Sedgewick and the late Philippe Flajolet have drawn from both classical mathematical and computer science material, integrating discrete mathematics, elementary real analysis, combinatorics, algorithms, and data structures. They focus on "average-case" or "probabilistic" analysis, while also covering tools for "worst case" or "complexity" analysis. Improvements in this edition include: * Upgraded figures and code* Newer style for presenting much of the text's math* An all-new chapter on trees This book's thorough, self-contained coverage will help readers appreciate the field's challenges, prepare them for advanced results covered in Donald Knuth's books, and provide the background they need to keep abreast of new research. Coverage includes: recurrences, generating functions, asymptotics, trees, strings, maps, sorting, tree search, string search, and hashing algorithms. Ideal for junior- or senior-level courses on mathematical analysis of algorithms, this book will also be useful in courses on discrete mathematics for computer scientists, and in introducing mathematics students to computer science principles related to algorithms and data structures. 

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