Prefix and plain Kolmogorov complexity characterizations of 2-randomness: simple proofs

Miller (J Symb Log 69(3):907–913, 2004, http://projecteuclid.org/euclid.jsl/1096901774) and independently Nies et al. (J Symb Log 70(2):515–535, 2005) gave a complexity characterization of 2-random sequences in terms of plain Kolmogorov complexity $${C(\cdot)}$$C(·) : they are sequences that have infinitely many initial segments with O(1)-maximal plain complexity (among the strings of the same length). Later Miller (Notre Dame J Form Log 50(4):381–391, 2009) showed that prefix complexity $${K(\cdot)}$$K(·) can also be used in a similar way: a sequence is 2-random if and only if it has infinitely many initial segments with O(1)-maximal prefix complexity (which is n + K(n) for strings of length n). The known proofs of these results are quite involved; in this paper we provide elementary proofs. Miller (J Symb Log 69(3):907–913, 2004, http://projecteuclid.org/euclid.jsl/1096901774) also gave a quantitative version of the first result: the $${\mathbf{0}'}$$0′-randomness deficiency of a sequence $${\omega}$$ω equals lim inf$${_n [n - C(\omega_1\dots\omega_n)] + O(1)}$$n[n-C(ω1⋯ωn)]+O(1). Our simplified proof can also be used to prove this. We show (and this seems to be a new result) that a similar quantitative result is also true for prefix complexity: $${\mathbf{0}'}$$0′-randomness deficiency equals lim inf$${_n [n + KP(n) - K(\omega_1\dots\omega_n)]+ O(1)}$$n[n+KP(n)-K(ω1⋯ωn)]+O(1).

[1]  Péter Gács Exact Expressions for some Randomness Tests , 1979, Theoretical Computer Science.

[2]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 1997, Texts in Computer Science.

[3]  Bruno Bauwens,et al.  An additivity theorem for plain complexity , 2011, ArXiv.

[4]  Bruno Bauwens,et al.  An Additivity Theorem for Plain Kolmogorov Complexity , 2012, Theory of Computing Systems.

[5]  Joseph S. Miller,et al.  Every 2-random real is Kolmogorov random , 2004, Journal of Symbolic Logic.

[6]  Chris J. Conidis Effectively approximating measurable sets by open sets , 2012, Theor. Comput. Sci..

[7]  Claus-Peter Schnorr,et al.  Process complexity and effective random tests , 1973 .

[8]  Péter Gács,et al.  Algorithmic tests and randomness with respect to a class of measures , 2011, ArXiv.

[9]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[10]  Alexander Shen Algorithmic Information Theory and Kolmogorov Complexity , 2000 .

[11]  Nikolai K. Vereshchagin,et al.  Limit complexities revisited [once more] , 2012, ArXiv.

[12]  Rodney G. Downey,et al.  Algorithmic Randomness and Complexity , 2010, Theory and Applications of Computability.

[13]  A. Kolmogorov Three approaches to the quantitative definition of information , 1968 .

[14]  Per Martin-Löf,et al.  The Definition of Random Sequences , 1966, Inf. Control..

[15]  André Nies,et al.  Randomness, relativization and Turing degrees , 2005, J. Symb. Log..

[16]  Gregory J. Chaitin,et al.  A recent technical report , 1974, SIGA.

[17]  Joseph S. Miller,et al.  The K-Degrees, Low for K Degrees, and Weakly Low for K Sets , 2009, Notre Dame J. Formal Log..

[18]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[19]  Denis R. Hirschfeldt,et al.  Algorithmic randomness and complexity. Theory and Applications of Computability , 2012 .

[20]  Nikolai K. Vereshchagin,et al.  Limit Complexities Revisited , 2009, Theory of Computing Systems.