Lowness for effective Hausdorff dimension

We examine the sequences A that are low for dimension, i.e. those for which the effective (Hausdorff) dimension relative to A is the same as the unrelativized effective dimension. Lowness for dimension is a weakening of lowness for randomness, a central notion in effective randomness. By considering analogues of characterizations of lowness for randomness, we show that lowness for dimension can be characterized in several ways. It is equivalent to lowishness for randomness, namely, that every Martin-Lof random sequence has effective dimension 1 relative to A, and lowishness for K, namely, that the limit of KA(n)/K(n) is 1. We show that there is a perfect -class of low for dimension sequences. Since there are only countably many low for random sequences, many more sequences are low for dimension. Finally, we prove that every low for dimension is jump-traceable in order ne, for any e > 0.

[1]  Jack H. Lutz,et al.  Effective Strong Dimension, Algorithmic Information, and Computational Complexity , 2002, ArXiv.

[2]  Gregory J. Chaitin Information-Theoretic Characterizations of Recursive Infinite Strings , 1976, Theor. Comput. Sci..

[3]  André Nies,et al.  Trivial Reals , 2002, CCA.

[4]  Gregory J. Chaitin,et al.  Algorithmic Information Theory , 1987, IBM J. Res. Dev..

[5]  A. Nies Computability and randomness , 2009 .

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

[7]  Bjorn Kjos-Hanssen,et al.  Low for random reals and positive-measure domination , 2007, 1408.2171.

[8]  Stephen G. Simpson,et al.  Almost everywhere domination and superhighness , 2007, Math. Log. Q..

[9]  Péter Gács,et al.  Every Sequence Is Reducible to a Random One , 1986, Inf. Control..

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

[11]  H. Geiringer On the Foundations of Probability Theory , 1967 .

[12]  Bjørn Kjos-Hanssen,et al.  Lowness notions, measure and domination , 2012, J. Lond. Math. Soc..

[13]  A. Nies Lowness properties and randomness , 2005 .

[14]  Rebecca Weber,et al.  Finite Self-Information , 2012, Comput..

[15]  Stephen G. Simpson,et al.  Almost everywhere domination , 2004, Journal of Symbolic Logic.

[16]  Elvira Mayordomo,et al.  A Kolmogorov complexity characterization of constructive Hausdorff dimension , 2002, Inf. Process. Lett..

[17]  Jack H. Lutz,et al.  Gales and the Constructive Dimension of Individual Sequences , 2000, ICALP.

[18]  André Nies,et al.  Randomness notions and partial relativization , 2012 .

[19]  Ian Herbert,et al.  A Perfect Set of Reals with Finite Self-Information , 2012, The Journal of Symbolic Logic.

[20]  A. Kucera Measure, Π10-classes and complete extensions of PA , 1985 .

[21]  André Nies,et al.  Reals which Compute Little , 2002 .

[22]  Noam Greenberg,et al.  Strong jump-traceability II: K-triviality , 2012 .