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Projection Theorems Using Effective Dimension

Authors Neil Lutz, Donald M. Stull

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Author Details

Neil Lutz
  • Department of Computer and Information Science, University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA 19104, USA
Donald M. Stull
  • Inria Nancy-Grand Est, 615 rue du jardin botanique, 54600 Villers-les-Nancy, France

Funding

  • Stull, Donald M.: Research supported in part by National Science Foundation Grants 1247051 and 1545028.

Cite AsGet BibTex

Neil Lutz and Donald M. Stull. Projection Theorems Using Effective Dimension. In 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 117, pp. 71:1-71:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.MFCS.2018.71

Abstract

In this paper we use the theory of computing to study fractal dimensions of projections in Euclidean spaces. A fundamental result in fractal geometry is Marstrand's projection theorem, which shows that for every analytic set E, for almost every line L, the Hausdorff dimension of the orthogonal projection of E onto L is maximal. We use Kolmogorov complexity to give two new results on the Hausdorff and packing dimensions of orthogonal projections onto lines. The first shows that the conclusion of Marstrand's theorem holds whenever the Hausdorff and packing dimensions agree on the set E, even if E is not analytic. Our second result gives a lower bound on the packing dimension of projections of arbitrary sets. Finally, we give a new proof of Marstrand's theorem using the theory of computing.

Subject Classification

ACM Subject Classification
  • Theory of computation → Complexity theory and logic
Keywords
  • algorithmic randomness
  • geometric measure theory
  • Hausdorff dimension
  • Kolmogorov complexity

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