Dimension, entropy rates, and compression
Hitchcock, J.M.
Vinodchandran, N.V.
Dept. of Comput. Sci., Wyoming Univ., USA;
This paper appears in: Computational Complexity, 2004. Proceedings. 19th IEEE Annual Conference on
Publication Date: 21-24 June 2004
On page(s): 174- 183
ISSN: 1093-0159
ISBN: 0-7695-2120-7
INSPEC Accession Number: 8199171
Digital Object Identifier: 10.1109/CCC.2004.1313835
Current Version Published: 2004-07-19
Abstract
This paper develops relationships between resource-bounded dimension, entropy rates, and compression. New tools for calculating dimensions are given and used to improve previous results about circuit-size complexity classes. Approximate counting of SpanP functions is used to prove that the NP-entropy rate is an upper bound for dimension in Δ3E, the third level of the exponential-time hierarchy. This general result is applied to simultaneously improve the results of Mayordomo (1994) on the measure on P/poly in Δ3E and of Lutz (2003) on the dimension of exponential-size circuit complexity classes in ESPACE. Entropy rates of efficiently rankable sets, sets that are optimally compressible, are studied in conjunction with time-bounded dimension. It is shown that rankable entropy rates give upper bounds for time-bounded dimensions. We use this to improve results of Lutz (1992) about polynomial-size circuit complexity classes from resource-bounded measure to dimension. Exact characterizations of the effective dimensions in terms of Kolmogorov complexity rates at the polynomial-space and higher levels have been established, but in the time-bounded setting no such equivalence is known. We introduce the concept of polynomial-time superranking as an extension of ranking. We show that superranking provides an equivalent definition of polynomial-time dimension. From this superranking characterization we show that polynomial-time Kolmogorov complexity rates give a lower bound on polynomial-time dimension.
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