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4. Discrete geometry

    1. Equi-isoclinic subspaces

          Given k-dimensional linear subspaces U,V\leq \mathbb{R}^d, we say that U,V are equi-isoclinic with angle \alpha if every unit vector in U projects down onto V a vector of length \alpha.

      Conjecture 4.1.

      [Yufei Zhao] Let N_\alpha^k(d) be the maximum number of k-dimensional subspaces of \mathbb{R}^d, every pair of which is equi-isoclinic with angle \alpha. Then

      (1) N_{\alpha}^k(d)=O_{\alpha,k}(d).

      (2) N_{\alpha}^k(d)=O_{k}(d)+o_{\alpha,k}(d).
          The case k=1 gives equiangular lines, which is known, using Ramsey Theory.

      k=2 generalizes "complex equiangular lines". Conjecture 1 is known for complex equiangular lines.
        • Distinct distances and related problems

          Conjecture 4.2.

          [Cosmin Pohoata] (1) Let S\subset\mathbb{R}^2 with no three points collinear. Then S determines at least |S|/2 distinct distances.

          (2) Given n points in \mathbb{R}^2 with no three collinear. Then one can remove O(1)-many points so that the number of isosceles triangles is at most 1.99\binom{n}{2}.

          (3) Given a set S of n points in \mathbb{R}^2 with no three collinear. Then S determines \Omega(n^2) perpendicular bisectors containing at most 1 point of S each, unless S is the vertices of a regular polygon together with its center.
              (1) is known with |S|/3 distinct distances, by double counting isosceles triangles.
            • Kupitz conjecture in three dimensions

                  (Kupitz conjecture) Given n points in \mathbb{R}^2, is there always a line through at least two points, such that the number of points on each side differ by O(1)?

              Problem 4.3.

              [Jeck Lim] Given n points in \mathbb{R}^3, is there always a plane through at least two points, such that the number of points on each side differ by O(1)?
                  For Kupitz conjecture, if the optimal bound is C, then it is known that 2\leq C\leq O(\log\log n). For pseudoline arrangements, C=\Theta(\log\log n).

              For 3D, the same upper bound O(\log\log n) follows by projection.

                  Cite this as: AimPL: High-dimensional phenomena in discrete analysis, available at http://aimpl.org/highdimdiscrete.