num_pages: 342
description: The book gives an accessible account of modern probabilistic methods for analyzing combinatorial structures and algorithms. It will be an useful guide for graduate students and researchers. Special features included: a simple treatment of Talagrand's 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 exploit the structure of the underlying graph; a succinct treatment of randomized algorithms and derandomization techniques.
language: English
title: Probabilistic Methods for Algorithmic Discrete Mathematics
titlecomplete: Probabilistic Methods for Algorithmic Discrete Mathematics (Algorithms and Combinatorics, 16)
genres: Mathematics
ratings_count: 2
publisher: Springer