There are many ways to measure the complexity of a given object, but there are two measures of particular importance in the theory of computing: One is Kolmogorov complexity, which measures the amount of information necessary to describe an object. Another is computational complexity, which measures
Kolmogorov Complexity and Computational Complexity
β Scribed by Osamu Watanabe
- Publisher
- Springer
- Year
- 1992
- Tongue
- English
- Leaves
- 110
- Series
- EATCS Monographs on Theoretical Computer Science
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
The mathematical theory of computation has given rise to two important apΒ proaches to the informal notion of "complexity": Kolmogorov complexity, usuΒ ally a complexity measure for a single object such as a string, a sequence etc., measures the amount of information necessary to describe the object. CompuΒ tational complexity, usually a complexity measure for a set of objects, measures the compuational resources necessary to recognize or produce elements of the set. The relation between these two complexity measures has been considered for more than two decades, and may interesting and deep observations have been obtained. In March 1990, the Symposium on Theory and Application of MinimalΒ Length Encoding was held at Stanford University as a part of the AAAI 1990 Spring Symposium Series. Some sessions of the symposium were dedicated to Kolmogorov complexity and its relations to the computational complexity theΒ ory, and excellent expository talks were given there. Feeling that, due to the importance of the material, some way should be found to share these talks with researchers in the computer science community, I asked the speakers of those sessions to write survey papers based on their talks in the symposium. In response, five speakers from the sessions contributed the papers which appear in this book.
β¦ Table of Contents
Front Matter....Pages i-vii
Introduction....Pages 1-3
Applications of Time-Bounded Kolmogorov Complexity in Complexity Theory....Pages 4-22
On Sets with Small Information Content....Pages 23-42
Kolmogorov Complexity, Complexity Cores, and the Distribution of Hardness....Pages 43-65
Resource Bounded Kolmogorov Complexity and Statistical Tests....Pages 66-84
Complexity and Entropy: An Introduction to the Theory of Kolmogorov Complexity....Pages 85-102
Back Matter....Pages 103-106
β¦ Subjects
Computation by Abstract Devices; Algorithm Analysis and Problem Complexity; Combinatorics
π SIMILAR VOLUMES
<p>This book contains a revised version of the dissertation the author wrote at the Department of Computer Science of the University of Chicago. The thesis was submitted to the Faculty of Physical Sciences in conformity with the requirements for the PhD degree in June 1999. It was honored with the 1
<p>This book contains a revised version of the dissertation the author wrote at the Department of Computer Science of the University of Chicago. The thesis was submitted to the Faculty of Physical Sciences in conformity with the requirements for the PhD degree in June 1999. It was honored with the 1
Looking at a sequence of zeros and ones, we often feel that it is not random, that is, it is not plausible as an outcome of fair coin tossing. Why? The answer is provided by algorithmic information theory: because the sequence is compressible, that is, it has small complexity or, equivalently, can b
Offers a comprehensive and accessible treatment of the theory of algorithms and complexity. Develops all the necessary mathematical prerequisites from such diverse fields as computability, logic, number theory, combinatorics, and probability. DLC: Computational complexity.
Offers a comprehensive and accessible treatment of the theory of algorithms and complexity. Develops all the necessary mathematical prerequisites from such diverse fields as computability, logic, number theory, combinatorics, and probability. DLC: Computational complexity.