Algorithms for Random Generation and Counting: A Markov Chain Approach

Name: Algorithms for Random Generation and Counting: A Markov Chain Approach
Brand: Springer, Berlin
SKU: 9781461267072
Price: 105.02 EUR
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Автор: A. Sinclair

Език:

Корици: С меки корици

Издател: Springer, Berlin

Наличност: Външен склад

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This monograph is a slightly revised version of my PhD thesis [86], com pleted in the Department of...

Информация за книгата

Автор

A. Sinclair

Език

Английски език

Корици

Книга - С меки корици

Издадена

2013

страници

147

EAN

9781461267072

ISBN

1461267072

Enbook ID

06794879

Издател

Springer, Berlin

Теглоt

254

Размери

155 x 235 x 8

Пълно описание

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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Algorithms for Random Generation and Counting: A Markov Chain Approach

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