Analysis of Algorithms: Computational Methods & Mathematical Tools presents the methods and tools needed to determine the effectiveness of algorithms. It begins with basic computational tools such as generating functions, combinatorial calculus, and asymptomatic methods, and continues through applications such as searching and sorting, communications protocols, and bin packing heuristics. The techniques needed for an effective use of each concept are shown in examples, then in exercises for which detailed solutions are provided. Proofs are given to illustrate the focal topic of the chapter. While the book can be used as a reference tool for algorithm designers and scientists specializing in their analyses, the exercises also make this a useful guide for graduate courses and seminars. Much of the material is culled from recent journal articles, and is presented here for the first time in book form.
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Micha Hofri, Computer Science Department, University of Houston.
"The probabilistic analysis of algorithms, as a discipline, draws on a number of branches of mathematics: combinatorics, probability theory, graph theory, real and complex analysis, and occasionally algebra, number theory, computation theory, operational calculus, and more. It is unreasonable to expect the students to have more than a cursory knowledge of the techniques we use, so much of the text introduces and explores these methods. . .The emphasis throughout is on the analytic and probabilistic aspects, rather than the algorithmic ones." --from the Preface
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