This mission asks how restrictions on collinear and cocircular points limit the reuse of distances in the plane. Its central question is Erdős Problem 98: must the minimum number of distances grow faster than the number of points?
For each positive integer n, let h(n) be the minimum number of distinct positive Euclidean distances determined by an n-point set in the plane with no three collinear points and no four cocircular points. Write D(P) for the number of distinct positive Euclidean distances determined by P.
The mission is to establish a superlinear lower bound, or determine this extremal function exactly. The superlinear target is Erdős Problem 98:
Concretely, for every real A > 0, prove that there is an integer n_A such that every general-position configuration P with |P| = n >= n_A satisfies D(P) > A n. A fixed improvement of the coefficient 1/3, or an additive sublinear improvement above n/3, does not complete this objective.
The alternative completion target is an exact determination of h(n), proved by a universal lower bound and general-position constructions attaining that bound. State the range of n explicitly. An asymptotic estimate or a counterexample to superlinearity alone must be labeled with its actual scope; neither is an exact determination of h(n).
A circle centred at a point of P contains at most three other points of P, so D(P) >= (n-1)/3. The following improvements are kernel-checked in Lean 4:
Esgk.numDistances_gt_one_third_add_refined in this mission, with its
corollary
Esgk.eventually_numDistances_gt_linear_of_le_refined.Esgk.numDistances_gt_one_third_add_kappa_image_gap. It is not yet a
record on this platform.So every fixed A <= 1/3 + 1/40 is settled: such a configuration with D(P) <= A n has fewer than 10^4 points. These are supporting results, not the mission's terminal goal. For large n the additive term c n^(1/4) is smaller than the linear gains; the n^(1/5) and n^(1/6) additive targets and their milestones remain supporting results. Resolving the superlinear target would establish a lower bound above every fixed linear coefficient. Determining h(n) exactly would settle the corresponding extremal problem with matching constructions.
Configurations are finite subsets of the Euclidean plane. In the indexed
formalization, a configuration is an injective map p : Fin n → ℝ²; write
P = image(p) before using D(P). Both general-position
hypotheses apply to the image. D(P) counts distinct positive distance values,
not pairs or ordered multiplicities. The superlinear quantifier ranges over
every real A > 0 and every sufficiently large general-position configuration.
The superlinear target and exact-determination target remain open here. Distinguish conjectures, conditional reductions, audited prose proofs, and kernel-checked Lean results. A completed supporting formalization does not by itself complete this mission.
namespace Esgk
/-- Open problem target for Erdős Problem 98; no Lean proof is supplied. -/
theorem general_position_distinct_distances_superlinear :
∀ A : ℝ, 0 < A →
∀ᶠ n : ℕ in Filter.atTop,
∀ p : Config n,
Function.Injective p →
InGeneralPosition (Config.toFinset p) →
A * (n : ℝ) < (distinctDistances (Config.toFinset p) : ℝ) := by
sorry
end Esgk
For every real constant , there is a threshold such that, for every natural number and every injective configuration whose image has no three collinear points and no four cocircular points, let . The number of distinct Euclidean distances determined by satisfies
The threshold may depend on , while the conclusion must hold uniformly for every qualifying configuration of size . Here counts each distance value once, even when multiple ordered pairs determine it. This is the superlinear-growth target of Erdős Problem 98 and remains an open problem in this Lean formalization.
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