Projections of self-similar sets with no separation condition
\'Abel Farkas

TL;DR
This paper studies how the Hausdorff dimension and measure of self-similar sets change under linear and smooth maps without separation conditions, revealing conditions for dimension preservation and measure zero results.
Contribution
It extends known results on self-similar sets by removing separation conditions and analyzing the effects of the orthogonal parts of the defining maps.
Findings
Linear images of self-similar sets with finite orthogonal group are graph directed attractors.
Dimension drops can occur under certain projections.
Under dense orthogonal groups, the Hausdorff measure of images can be zero.
Abstract
We investigate how the Hausdorff dimension and measure of a self-similar set behave under linear images. This depends on the nature of the group generated by the orthogonal parts of the defining maps of . We show that if is finite then every linear image of is a graph directed attractor and there exists at least one projection of such that the dimension drops under the image of the projection. In general, with no restrictions on we establish that for every element of the closure of , where is a linear map and . We also prove that for disjoint subsets and \textbf{} of we have that . Hochman and Shmerkin showed that if is dense in and the strong…
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