Randomized AlgorithmsFor many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications. Algorithmic examples are also given to illustrate the use of each tool in a concrete setting. In the second part of the book, each chapter focuses on an important area to which randomized algorithms can be applied, providing a comprehensive and representative selection of the algorithms that might be used in each of these areas. Although written primarily as a text for advanced undergraduates and graduate students, this book should also prove invaluable as a reference for professionals and researchers. 
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Review: Randomized Algorithms
User Review  DJ  GoodreadsNice book on randomized algorithms recommended by Nielsen and chuang, but as Nick mentions, the maths required are still one grad analysis class beyond me Read full review
Review: Randomized Algorithms
User Review  dead letter office  Goodreadsi'll just say you should be suspicious of any textbook that misspells the author's name on the spine. Read full review
Contents
Introduction  3 
GameTheoretic Techniques  28 
Moments and Deviations  43 
Tail Inequalities  67 
The Probabilistic Method  101 
Markov Chains and Random Walks  127 
Algebraic Techniques  161 
Data Structures  197 
Approximate Counting  306 
Parallel and Distributed Algorithms  335 
Online Algorithms  368 
Number Theory and Algebra  392 
429  
Basic Probability Theory  438 
447  
467  
Common terms and phrases
adversary analysis apply assume binary bipartite graph Boolean cache Chapter Chernoff bound choice choose chosen clauses competitiveness coefficient compute Consider constraints contains corresponding cost define Definition deleted described deterministic algorithm distribution elements evaluation Exercise expanding graphs expected number expected running given graph G hash function independent inequality input integer intersection iteration Las Vegas algorithm least Lemma linear program lower bound Markov chain martingale mincut modulo node number of edges number of steps O(logn obtain offline online algorithm output pairwise independent partition path perfect matching permutation pointers polynomial prime processors proof prove quadratic residue random bits random variable random walk randomized algorithm recursive result RNC algorithm sampling satisfying Section segments Show skip list space square roots subtree subset Suppose technique Theorem treap truth assignment uniformly at random upper bound vector verify vertex weight
Popular passages
Page 463  JP Schmidt, A. Siegel, and A. Srinivasan. ChernoffHoeffding bounds for applications with limited independence. In Proceedings of the 4th Annual ACMSIAM Symposium on Discrete Algorithms, pages 331340, 1993.
Page 451  U. Manber and G. Myers. Suffix arrays: A new method for online string searches. In Proceedings of the 1st Annual ACMSIAM Symposium on Discrete Algorithms, pages 319327, 1990.
Page 452  KL Clarkson. A Las Vegas algorithm for linear programming when the dimension is small. In Proc. 29th Annu. IEEE Sympos. Found. Comput. Sei., pages 452456, 1988. [21] KL Clarkson and PW Shor. Applications of random sampling in computational geometry, II.