Compressibility and Resource Bounded Measure
SIAM Journal on Computing
A Kolmogorov complexity characterization of constructive Hausdorff dimension
Information Processing Letters
MAX3SAT is exponentially hard to approximate if NP has positive dimension
Theoretical Computer Science
Hausdorff Dimension in Exponential Time
CCC '01 Proceedings of the 16th Annual Conference on Computational Complexity
Note: fractal dimension and logarithmic loss unpredictability
Theoretical Computer Science
Dimension in Complexity Classes
SIAM Journal on Computing
The dimensions of individual strings and sequences
Information and Computation
Effective fractal dimension: foundations and applications
Effective fractal dimension: foundations and applications
Dimension, Entropy Rates, and Compression
CCC '04 Proceedings of the 19th IEEE Annual Conference on Computational Complexity
Grammar-based codes: a new class of universal lossless source codes
IEEE Transactions on Information Theory
SIGACT news complexity theory column 48
ACM SIGACT News
Dimension, entropy rates, and compression
Journal of Computer and System Sciences
Theoretical Computer Science
Polylog Space Compression Is Incomparable with Lempel-Ziv and Pushdown Compression
SOFSEM '09 Proceedings of the 35th Conference on Current Trends in Theory and Practice of Computer Science
Lempel-ziv dimension for lempel-ziv compression
MFCS'06 Proceedings of the 31st international conference on Mathematical Foundations of Computer Science
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Effective fractal dimension was defined by Lutz (2003) in order to quantitatively analyze the structure of complexity classes. Interesting connections of effective dimension with information theory were also found, in fact the cases of polynomial-space and constructive dimension can be precisely characterized in terms of Kolmogorov complexity, while analogous results for polynomial-time dimension haven't been found. In this paper we remedy the situation by using the natural concept of reversible time-bounded compression for finite strings. We completely characterize polynomial-time dimension in terms of polynomial-time compressors.