Dimension, entropy rates, and compression

This paper develops new 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 , the third level of the exponential-time hierarchy. This general result is applied to simultaneously improve the results of Mayordomo [E. Mayordomo, Contributions to the study of resource-bounded measure, PhD thesis, Universitat Politècnica de Catalunya, 1994] on the measure on P/poly in and of Lutz [J.H. Lutz, Dimension in complexity classes, SIAM J. Comput. 32 (5) (2003) 1236–1259] 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 [J.H. Lutz, Almost everywhere high nonuniform complexity, J. Comput. System Sci. 44 (2) (1992) 220–258] 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.