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Random Noise Increases Kolmogorov Complexity and Hausdorff Dimension

Authors Gleb Posobin , Alexander Shen

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Author Details

Gleb Posobin
  • Computer Science department, Columbia University, New York, USA
Alexander Shen
  • LIRMM CNRS & University of Montpellier, 161 rue Ada, 34095, Montpellier, France,
  • On leave from IITP RAS, Moscow


Authors are grateful to the participants and organizers of the Heidelberg Kolmogorov complexity program where the question of the complexity increase was raised, and to all colleagues (from the ESCAPE team, LIRMM, Montpellier, Kolmogorov seminar and HSE Theoretical Computer Science Group, and other places) who participated in the discussions, in particular to B. Bauwens, N. Greenberg, K. Makarychev, Yu. Makarychev, J. Miller, A. Milovanov, F. Nazarov, I. Razenshteyn, A. Romashchenko, N. Vereshchagin, L. B. Westrick, and, last but not least, to Peter Gács who explained us how the tools from [Ahlswede et al., 1976] can be used to provide the desired result about Kolmogorov complexity.

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Gleb Posobin and Alexander Shen. Random Noise Increases Kolmogorov Complexity and Hausdorff Dimension. In 36th International Symposium on Theoretical Aspects of Computer Science (STACS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 126, pp. 57:1-57:14, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)


Consider a bit string x of length n and Kolmogorov complexity alpha n (for some alpha<1). It is always possible to increase the complexity of x by changing a small fraction of bits in x [Harry Buhrman et al., 2005]. What happens with the complexity of x when we randomly change each bit independently with some probability tau? We prove that a linear increase in complexity happens with high probability, but this increase is smaller than in the case of arbitrary change considered in [Harry Buhrman et al., 2005]. The amount of the increase depends on x (strings of the same complexity could behave differently). We give exact lower and upper bounds for this increase (with o(n) precision). The same technique is used to prove the results about the (effective Hausdorff) dimension of infinite sequences. We show that random change increases the dimension with probability 1, and provide an optimal lower bound for the dimension of the changed sequence. We also improve a result from [Noam Greenberg et al., 2018] and show that for every sequence omega of dimension alpha there exists a strongly alpha-random sequence omega' such that the Besicovitch distance between omega and omega' is 0. The proofs use the combinatorial and probabilistic reformulations of complexity statements and the technique that goes back to Ahlswede, Gács and Körner [Ahlswede et al., 1976].

Subject Classification

ACM Subject Classification
  • Theory of computation → Randomness, geometry and discrete structures
  • Kolmogorov complexity
  • effective Hausdorff dimension
  • random noise


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