Randomized Algorithms

This book presents basic tools from probability theory used in algorithmic applications, with concrete examples.

Klappentext For many applications a randomized algorithm is either the simplest algorithm available! or the fastest! or both. This tutorial presents the basic concepts in the design and analysis of randomized algorithms. The first part of the book presents tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete setting. In the second part of the book! each of the seven chapters focuses on one important area of application of randomized algorithms: data structures; geometric algorithms; graph algorithms; number theory; enumeration; parallel algorithms; and on-line algorithms. A comprehensive and representative selection of the algorithms in these areas is also given. This first book on the subject should prove invaluable as a reference for researchers and professional programmers! as well as for students. Zusammenfassung Presents basic tools from probability theory used in algorithmic applications! with concrete examples. This book should prove invaluable as a reference for researchers and professional programmers! as well as for students. Inhaltsverzeichnis Part I. Tools and Techniques: 1. Introduction; 2. Game-theoretic techniques; 3. Moments and deviations; 4. Tail inequalities; 5. The probabilistic method; 6. Markov chains and random walks; 7. Algebraic techniques; Part II. Applications: 8. Data structures; 9. Geometric algorithms and linear programming; 10. Graph algorithms; 11. Approximate counting; 12. Parallel and distributed algorithms; 13. Online algorithms; 14. Number theory and algebra; Appendix A: notational index; Appendix B: mathematical background; Appendix C: basic probability theory.

Autorentext
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Inhalt
Part I. Tools and Techniques: 1. Introduction; 2. Game-theoretic techniques; 3. Moments and deviations; 4. Tail inequalities; 5. The probabilistic method; 6. Markov chains and random walks; 7. Algebraic techniques; Part II. Applications: 8. Data structures; 9. Geometric algorithms and linear programming; 10. Graph algorithms; 11. Approximate counting; 12. Parallel and distributed algorithms; 13. Online algorithms; 14. Number theory and algebra; Appendix A: notational index; Appendix B: mathematical background; Appendix C: basic probability theory.

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