Randomized Algorithms: Approximation, Generation, and Counting
by Russ Bubley
Book Details
Reading Info
About This Book
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 Mont
Our Review
This advanced computer science text tackles two fundamental problems in discrete mathematics: counting combinatorial structures and generating random samples from complex probability distributions. When traditional deterministic approaches become computationally intractable, this work demonstrates how randomized algorithms—particularly Markov chain Monte Carlo methods—provide powerful alternatives for answering questions about "how many" and "what does it look like" across various mathematical structures.
The book's strength lies in bridging theoretical computer science with practical applications, offering sophisticated techniques for approximating solutions to problems that resist exact computation. Readers with backgrounds in algorithms, probability theory, or computational mathematics will find rigorous treatment of generation and counting methods that have become essential tools in fields from statistical physics to machine learning. This represents essential reading for graduate students and researchers seeking to understand how randomization can overcome computational barriers in discrete mathematics.
Themes
Subjects
Looking for more books?
Visit our sister site BooksbyOrder.com