Randomized Algorithms: Approximation, Generation and Counting [electronic resource] /

Randomized Algorithms discusses two problems of fine pedigree: counting and generation, both of which are of fundamental importance to discrete mathematics and probability. When asking questions like "How many are there?" and "What does it look like on average?" of families of combinatorial structures, answers are often difficult to find -- we can be blocked by seemingly intractable algorithms. Randomized Algorithms shows how to get around the problem of intractability with the Markov chain Monte Carlo method, as well as highlighting the method's natural limits. It uses the technique of coupling before introducing "path coupling" a new technique which radically simplifies and improves upon previous methods in the area.

Bibliographic Details

Main Authors:	Bubley, Russ. author., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	London : Springer London, 2001
Subjects:	Computer science., Computer programming., Computers., Algorithms., Computer science, Probabilities., Computer Science., Algorithm Analysis and Problem Complexity., Programming Techniques., Theory of Computation., Math Applications in Computer Science., Symbolic and Algebraic Manipulation., Probability Theory and Stochastic Processes.,
Online Access:	http://dx.doi.org/10.1007/978-1-4471-0695-1
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