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Distance sets bounds for polyhedral norms via effective dimension
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abstract
We prove that, for every norm on $\mathbb{R}^d$ and every $E \subseteq \mathbb{R}^d$, the Hausdorff dimension of the distance set of $E$ with respect to that norm is at least $\dim_{\mathrm{H}} E - (d-1)$. An explicit construction follows, demonstrating that this bound is sharp for every polyhedral norm on $\mathbb{R}^d$. The techniques of algorithmic complexity theory underlie both the computations and the construction.
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Algorithmic Information Bounds for Distances and Orthogonal Projections
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