Concentration Of Measure For The Analysis Of Randomized Algorithms

Concentration of Measure for the Analysis of Randomized Algorithms

This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms.

Probabilistic Methods for Algorithmic Discrete Mathematics

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

Handbook of randomized computing. 1