For 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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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 determine deterministic algorithm distribution elements evaluation Exercise expanding graphs expected number expected running given graph G hash function independent inequality input integer intersection iteration least Lemma linear program lower bound Markov chain martingale matrix multiplication min-cut 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 sub-tree subset Suppose technique Theorem treap truth assignment uniformly at random upper bound vector verify vertex weight
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