An Introduction to Kolmogorov Complexity and Its Applications

  • Ming Li
  • Paul Vitányi

Part of the Graduate Texts in Computer Science book series (TCS)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Ming Li, Paul Vitányi
    Pages 1-92
  3. Ming Li, Paul Vitányi
    Pages 93-188
  4. Ming Li, Paul Vitányi
    Pages 189-238
  5. Ming Li, Paul Vitányi
    Pages 239-314
  6. Ming Li, Paul Vitányi
    Pages 315-377
  7. Ming Li, Paul Vitányi
    Pages 379-457
  8. Ming Li, Paul Vitányi
    Pages 459-520
  9. Ming Li, Paul Vitányi
    Pages 521-589
  10. Back Matter
    Pages 591-637

About this book

Introduction

Briefly, we review the basic elements of computability theory and prob­ ability theory that are required. Finally, in order to place the subject in the appropriate historical and conceptual context we trace the main roots of Kolmogorov complexity. This way the stage is set for Chapters 2 and 3, where we introduce the notion of optimal effective descriptions of objects. The length of such a description (or the number of bits of information in it) is its Kolmogorov complexity. We treat all aspects of the elementary mathematical theory of Kolmogorov complexity. This body of knowledge may be called algo­ rithmic complexity theory. The theory of Martin-Lof tests for random­ ness of finite objects and infinite sequences is inextricably intertwined with the theory of Kolmogorov complexity and is completely treated. We also investigate the statistical properties of finite strings with high Kolmogorov complexity. Both of these topics are eminently useful in the applications part of the book. We also investigate the recursion­ theoretic properties of Kolmogorov complexity (relations with Godel's incompleteness result), and the Kolmogorov complexity version of infor­ mation theory, which we may call "algorithmic information theory" or "absolute information theory. " The treatment of algorithmic probability theory in Chapter 4 presup­ poses Sections 1. 6, 1. 11. 2, and Chapter 3 (at least Sections 3. 1 through 3. 4).

Keywords

algorithms complexity statistics

Authors and affiliations

  • Ming Li
    • 1
  • Paul Vitányi
    • 2
  1. 1.Department of Computer ScienceUniversity of WaterlooWaterlooCanada
  2. 2.Centrum voor Wiskunde en InformaticaSJ AmsterdamThe Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-2606-0
  • Copyright Information Springer-Verlag New York 1997
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4757-2608-4
  • Online ISBN 978-1-4757-2606-0
  • Series Print ISSN 1868-0941
  • Series Online ISSN 1868-095X
  • About this book