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Randomized Algorithms: Approximation, Generation, and Counting
     

Randomized Algorithms: Approximation, Generation, and Counting

by Russ Bubley
 

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Randomized Algorithms discusses two problems of fine pedigree: counting and generation, both of which are of fundamental importance to discrete mathematics and probability. When asking questions like "How many are there?" and "What does it look like on average?" of families of combinatorial structures, answers are often difficult to find — we can be

Overview

Randomized Algorithms discusses two problems of fine pedigree: counting and generation, both of which are of fundamental importance to discrete mathematics and probability. When asking questions like "How many are there?" and "What does it look like on average?" of families of combinatorial structures, answers are often difficult to find — we can be blocked by seemingly intractable algorithms. Randomized Algorithms shows how to get around the problem of intractability with the Markov chain Monte Carlo method, as well as highlighting the method's natural limits. It uses the technique of coupling before introducing "path coupling" a new technique which radically simplifies and improves upon previous methods in the area.

Product Details

ISBN-13:
9781447111801
Publisher:
Springer London
Publication date:
07/31/2012
Series:
Distinguished Dissertations Series
Edition description:
Softcover reprint of the original 1st ed. 2001
Pages:
152
Product dimensions:
6.10(w) x 9.25(h) x 0.01(d)

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