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 book should prove invaluable as a reference for researchers and professional programmers, as well as for students.
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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 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 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 Pr[X 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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