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    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
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Algorithms for Random Generation and Counting: A Markov Chain Approach

Algorithms for Random Generation and Counting: A Markov Chain Approach

Sinclair, A.

Hardcover

Series: Progress in Theoretical Computer Science

General MathematicsProbability & Statistics

ISBN10: 0817636587
ISBN13: 9780817636586
Publisher: Springer Nature
Published: Feb 1 1993
Pages: 147
Weight: 0.89
Height: 0.44 Width: 6.14 Depth: 9.21
Language: English
This monograph is a slightly revised version of my PhD thesis [86], com- pleted in the Department of Computer Science at the University of Edin- burgh in June 1988, with an additional chapter summarising more recent developments. Some of the material has appeared in the form of papers [50,88]. The underlying theme of the monograph is the study of two classical problems: counting the elements of a finite set of combinatorial structures, and generating them uniformly at random. In their exact form, these prob- lems appear to be intractable for many important structures, so interest has focused on finding efficient randomised algorithms that solve them ap- proxim ly, with a small probability of error. For most natural structures the two problems are intimately connected at this level of approximation, so it is natural to study them together. At the heart of the monograph is a single algorithmic paradigm: sim- ulate a Markov chain whose states are combinatorial structures and which converges to a known probability distribution over them. This technique has applications not only in combinatorial counting and generation, but also in several other areas such as statistical physics and combinatorial optimi- sation. The efficiency of the technique in any application depends crucially on the rate of convergence of the Markov chain.

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