UID:
almahu_9947363276602882
Format:
XVII, 325 p.
,
online resource.
ISBN:
9783662127889
Series Statement:
Algorithms and Combinatorics, 16
Content:
The book gives an accessible account of modern pro- babilistic methods for analyzing combinatorial structures and algorithms. Each topic is approached in a didactic manner but the most recent developments are linked to the basic ma- terial. Extensive lists of references and a detailed index will make this a useful guide for graduate students and researchers. Special features included: - a simple treatment of Talagrand inequalities and their applications - an overview and many carefully worked out examples of the probabilistic analysis of combinatorial algorithms - a discussion of the "exact simulation" algorithm (in the context of Markov Chain Monte Carlo Methods) - a general method for finding asymptotically optimal or near optimal graph colouring, showing how the probabilistic method may be fine-tuned to explit the structure of the underlying graph - a succinct treatment of randomized algorithms and derandomization techniques.
Note:
The Probabilistic Method -- Probabilistic Analysis of Algorithms -- An Overview of Randomized Algorithms -- Mathematical Foundations of the Markov Chain Monte Carlo Method -- Percolation and the Random Cluster Model: Combinatorial and Algorithmic Problems -- Concentration -- Branching Processes and Their Applications in the Analysis of Tree Structures and Tree Algorithms -- Author Index.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9783642084263
Language:
English
DOI:
10.1007/978-3-662-12788-9
URL:
http://dx.doi.org/10.1007/978-3-662-12788-9
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