Limit complexities revisited [once more]

The main goal of this article is to put some known results in a common perspective and to simplify their proofs. We start with a simple proof of a result of Vereshchagin saying that $\limsup_n C(x|n)$ equals $C^{0'}(x)$. Then we use the same argument to prove similar results for prefix complexity, a priori probability on binary tree, to prove Conidis' theorem about limits of effectively open sets, and also to improve the results of Muchnik about limit frequencies. As a by-product, we get a criterion of 2-randomness proved by Miller: a sequence $X$ is 2-random if and only if there exists $c$ such that any prefix $x$ of $X$ is a prefix of some string $y$ such that $C(y)\ge |y|-c$. (In the 1960ies this property was suggested in Kolmogorov as one of possible randomness definitions.) We also get another 2-randomness criterion by Miller and Nies: $X$ is 2-random if and only if $C(x)\ge |x|-c$ for some $c$ and infinitely many prefixes $x$ of $X$. This is a modified version of our old paper that contained a weaker (and cumbersome) version of Conidis' result, and the proof used low basis theorem (in quite a strange way). The full version was formulated there as a conjecture. This conjecture was later proved by Conidis. Bruno Bauwens (personal communication) noted that the proof can be obtained also by a simple modification of our original argument, and we reproduce Bauwens' argument with his permission.Comment: See http://arxiv.org/abs/0802.2833 for the old pape

Effective bounds for convergence, descriptive complexity, and natural examples of simple and hypersimple sets

AbstractLet μ be a universal lower enumerable semi-measure (defined by L. Levin). Any computable upper bound for μ can be effectively separated from zero with a constant (this is similar to a theorem of G. Marandzhyan).Computable positive lower bounds for μ can be nontrivial and allow one to construct natural examples of hypersimple sets (introduced by E. Post)

Limit complexities revisited

The main goal of this paper is to put some known results in a common perspective and to simplify their proofs. We start with a simple proof of a result from (Vereshchagin, 2002) saying that \limsup_n\KS(x|n) (here \KS(x|n) is conditional (plain) Kolmogorov complexity of $x$ when $n$ is known) equals \KS^{\mathbf{0'}(x), the plain Kolmogorov complexity with \mathbf{0'-oracle. Then we use the same argument to prove similar results for prefix complexity (and also improve results of (Muchnik, 1987) about limit frequencies), a priori probability on binary tree and measure of effectively open sets. As a by-product, we get a criterion of $\mathbf{0'}$ Martin-L\"of randomness (called also 2-randomness) proved in (Miller, 2004): a sequence $\omega$ is 2-random if and only if there exists $c$ such that any prefix $x$ of $\omega$ is a prefix of some string $y$ such that \KS(y)\ge |y|-c. (In the 1960ies this property was suggested in (Kolmogorov, 1968) as one of possible randomness definitions; its equivalence to 2-randomness was shown in (Miller, 2004) while proving another 2-randomness criterion (see also (Nies et al. 2005)): $\omega$ is 2-random if and only if \KS(x)\ge |x|-c for some $c$ and infinitely many prefixes $x$ of $\omega$. Finally, we show that the low-basis theorem can be used to get alternative proofs for these results and to improve the result about effectively open sets; this stronger version implies the 2-randomness criterion mentioned in the previous sentence

Game interpretation of Kolmogorov complexity

The Kolmogorov complexity function K can be relativized using any oracle A, and most properties of K remain true for relativized versions. In section 1 we provide an explanation for this observation by giving a game-theoretic interpretation and showing that all "natural" properties are either true for all sufficiently powerful oracles or false for all sufficiently powerful oracles. This result is a simple consequence of Martin's determinacy theorem, but its proof is instructive: it shows how one can prove statements about Kolmogorov complexity by constructing a special game and a winning strategy in this game. This technique is illustrated by several examples (total conditional complexity, bijection complexity, randomness extraction, contrasting plain and prefix complexities).Comment: 11 pages. Presented in 2009 at the conference on randomness in Madison

Universal convergence of semimeasures on individual random sequences, in

Solomonoff’s central result on induction is that the posterior of a universal semimeasure M converges rapidly and with probability 1 to the true sequence generating posterior µ, if the latter is computable. Hence, M is eligible as a universal sequence predictor in case of unknown µ. Despite some nearby results and proofs in the literature, the stronger result of convergence for all (Martin-Löf) random sequences remained open. Such a convergence result would be particularly interesting and natural, since randomness can be defined in terms of M itself. We show that there are universal semimeasures M which do not converge for all random sequences, i.e. we give a partial negative answer to the open problem. We also provide a positive answer for some non-universal semimeasures. We define the incomputable measure D as a mixture over all computable measures and the enumerable semimeasure W as a mixture over all enumerable nearly-measures. We show that W converges to D and D to µ on all random sequences. The Hellinger distance measuring closeness of two distributions plays a central role

Conditional complexity and codes

AbstractLet x and y be binary strings. We prove that there exists a program p of size about K(x|y) that maps y to x and has small complexity when x is known (K(p|x)≈0). Having in mind the parallelism between Shannon information theory and algorithmic information theory, one can say that this result is parallel to Wolf–Slepian and Körner–Csiszar–Marton theorems, see (I. Csiszar and J. Körner, Information theory, Coding Theorems for Discrete Memoryless Systems, Akadémiai Kiadó, Budapest, 1981).We show also that for any three strings x,y,z of length at most n the length of the shortest program p that maps both y and z to x (i.e., p(y)=p(z)=x) equals max(K(x|y),K(x|z)+O(logn)