Open AccessProceedings Article
On process complexity
Adam R. Day
- 01 Jan 2009
- pp 31-36
6
TL;DR: In this article, the authors define a variant of process complexity based on Levin's definition of a process, called strict process complexity, and prove that it does not agree within an additive constant with Schnorr's original process complexity.
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Abstract: Process complexity is one of the basic variants of Kolmogorov complexity Unlike plain Kolmogorov complexity process complexity provides a simple characterization of randomness for real numbers in terms of initial segment complexity Process complexity was first developed in (Schnorr 1973) Schnorr's definition of a process, while simple, can be difficult to work with In many situations, a preferable definition of a process is that given by Levin in (Levin & Zvonkin 1970) In this paper we define a variant of process complexity based on Levin's definition of a process We call this variant strict process complexity Strict process complexity retains the main desirable properties of process complexity Particularly, it provides simple characterizations of Martin-Lof random real numbers, and of computable real numbers However, we will prove that strict process complexity does not agree within an additive constant with Schnorr's original process complexity
One of the basic properties of prefix-free complexity is that it is subadditive Subadditive means that there is some constant d such that for all strings σ, τ the complexity of στ (σ and τ concatenated) is less than or equal to the sum of the complexities of σ and τ plus d A fundamental question about any complexity measure is whether or not it is subadditive In this paper we resolve this question for process complexity by proving that neither of these process complexities is subadditive
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Ming Li,Paul M. B. Vitányi +1 more
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TL;DR: The Journal of Symbolic Logic as discussed by the authors presents a thorough treatment of the subject with a wide range of illustrative applications such as the randomness of finite objects or infinite sequences, Martin-Loef tests for randomness, information theory, computational learning theory, the complexity of algorithms, and the thermodynamics of computing.
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Three approaches to the quantitative definition of information
TL;DR: In this article, three approaches to the quantitative definition of information are presented: information-based, information-aware and information-neutral approaches to quantifying information in the context of information retrieval.
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Algorithmic Randomness and Complexity
Rodney G. Downey,Denis R. Hirschfeldt +1 more
- 29 Oct 2010
TL;DR: This chapter discusses Randomness-Theoretic Weakness, Omega as an Operator, Complexity of C.E. Sets, and other Notions of Effective Randomness.
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A Theory of Program Size Formally Identical to Information Theory
TL;DR: A new definition of program-size complexity is made, which has precisely the formal properties of the entropy concept of information theory.
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The complexity of finite objects and the development of the concepts of information and randomness by means of the theory of algorithms
A K Zvonkin,Leonid A. Levin +1 more
TL;DR: The present article is a survey of the fundamental results connected with the concept of complexity as the minimum number of binary signs containing all the information about a given object that are sufficient for its recovery (decoding).