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Randomized Algorithms: Approximation, Generation and Counting

  • Russ Bubley

Part of the Distinguished Dissertations book series (DISTDISS)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Russ Bubley
    Pages 1-11
  3. Russ Bubley
    Pages 29-36
  4. Russ Bubley
    Pages 37-82
  5. Russ Bubley
    Pages 83-90
  6. Russ Bubley
    Pages 91-118
  7. Russ Bubley
    Pages 119-124
  8. Back Matter
    Pages 139-152

About this book

Introduction

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.

Keywords

Discrete Mathematics Erfüllbarkeitsproblem der Aussagenlogik Markov Chain Monte Carlo Method Path Coupling Probability Randomized Algorithms algorithm algorithms complexity

Authors and affiliations

  • Russ Bubley
    • 1
  1. 1.University of LeedsLeedsUK

Bibliographic information