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1997 | Buch | 2. Auflage

An Introduction to Kolmogorov Complexity and Its Applications

verfasst von: Ming Li, Paul Vitányi

Verlag: Springer New York

Buchreihe : Texts in Computer Science

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Über dieses Buch

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).

Inhaltsverzeichnis

Frontmatter
1. Preliminaries
Abstract
Suppose we want to describe a given object by a finite binary string. We do not care whether the object has many descriptions; however, each description should describe but one object. Prom among all descriptions of an object we can take the length of the shortest description as a measure of the object’s complexity. It is natural to call an object “simple” if it has at least one short description, and to call it “complex” if all of its descriptions are long.
Ming Li, Paul Vitányi
2. Algorithmic Complexity
Abstract
The most natural approach to defining the quantity of information is clearly to define it in relation to the individual object (be it Homer’s Odyssey or a particular type of dodo) rather than in relation to a set of objects from which the individual object may be selected. To do so, one could define the quantity of information in an object in terms of the number of bits required to describe it. A description of an object is evidently only useful if we can reconstruct the object from this description.
Ming Li, Paul Vitányi
3. Algorithmic Prefix Complexity
Abstract
While the development of an algorithmic theory of complexity according to the original definitions (plain Kolmogorov complexity) in Chapter 2 was very fruitful, for certain goals the mathematical framework is not yet satisfactory. This has resulted in a plethora of proposals of modified measures to get rid of one or the other problem. Let us list a few conspicuous inconveniences.
Ming Li, Paul Vitányi
4. Algorithmic Probability
Abstract
P.S. Laplace (1749–1827) has pointed out the following reason why intuitively a regular outcome of a random event is unlikely:
“We arrange in our thought all possible events in various classes; and we regard as extraordinary those classes which include a very small number. In the game of heads and tails, if heads comes up a hundred times in a row then this appears to us extraordinary, because the almost infinite number of combinations that can arise in a hundred throws are divided in regular sequences, or those in which we observe a rule that is easy to grasp, and in irregular sequences, that are incomparably more numerous.”
Ming Li, Paul Vitányi
5. Inductive Reasoning
Abstract
The Oxford English Dictionary defines induction as “the process of inferring a general law or principle from the observations of particular instances.” This defines precisely what we would like to call inductive inference. On the other hand, we regard inductive reasoning as a more general concept than inductive inference, namely, as a process of reassigning a probability (or credibility) to a law or proposition from the observation of particular instances.
Ming Li, Paul Vitányi
6. The Incompressibility Method
Abstract
The incompressibility of random objects yields a simple but powerful proof technique. The incompressibility method is a general-purpose tool and should be compared with the pigeon-hole principle or the probabilistic method. Whereas the older methods generally show the existence of an object with the required properties, the incompressibility argument shows that almost all objects have the required property. This follows immediately from the fact that the argument is typically used on a Kolmogorov random object. Since such objects are effectively indistinguishable, the proof holds for all such objects. Each class of objects has an abundance of objects that are Kolmogorov random relative to the class.
Ming Li, Paul Vitányi
7. Resource-Bounded Complexity
Abstract
Recursion theory has a resource-bounded version in computational complexity theory. Similarly, Kolmogorov complexity has resource-bounded Kolmogorov complexity (also known as generalized Kolmogorov complexity). Several authors suggested early on the possibility of restricting the power of the device used to (de)compress strings. Says Kolmogorov:
“The concept discussed ...does not allow for the ‘difficulty’ of preparing a program p for passing from an object x to an object y. [... some] object permitting a very simple program, i.e., with very small complexity K(x) can be restored by short programs only as the result of computations of a thoroughly unreal nature. [... this concerns] the relationship between the necessary complexity of a program and its permissible difficulty t. The complexity K(x) that was obtained [before] is, in this case, the minimum of K t (x) on the removal of the constraints on t.”
Ming Li, Paul Vitányi
8. Physics, Information, and Computation
Abstract
Various issues in information theory and theoretical physics can be fruitfully analyzed by Kolmogorov complexity. This is the case for physical aspects of information processing and for application of complexity to physics issues. Physicists have used complexity arguments in a variety of settings like information distance, thermodynamics, chaos, biology, and philosophy. We touch briefly upon several themes, but focus on three main issues.
Ming Li, Paul Vitányi
Backmatter
Metadaten
Titel
An Introduction to Kolmogorov Complexity and Its Applications
verfasst von
Ming Li
Paul Vitányi
Copyright-Jahr
1997
Verlag
Springer New York
Electronic ISBN
978-1-4757-2606-0
Print ISBN
978-1-4757-2608-4
DOI
https://doi.org/10.1007/978-1-4757-2606-0