Algorithmic Information Bounds for Distances and Orthogonal Projections

We develop quantitative algorithmic information bounds for orthogonal projections and distances in the plane. Under mild independence conditions, the distance $|x-y|$ and a projection coordinate $p_e x$ each retain at least half the algorithmic information content of $x$ in the sense of finite-precision Kolmogorov complexity, up to lower-order terms. Our bounds support conditioning on coarser approximations, enabling case analyses across precision scales. The proofs introduce a surrogate point selection step. Via the point-to-set principle we derive a new bound on the Hausdorff dimension of pinned distance sets, showing that every analytic set $E\subseteq\mathbb{R}^2$ with $\dim_H(E)\leq 1$ satisfies $$\sup_{x\in E}\dim_H(\Delta_x E)\geq \frac{3}{4}\dim_H(E).$$ We also extend Bourgain's theorem on exceptional sets for orthogonal projections to all sets that admit optimal Hausdorff oracles.