Heilbronn's triangle problem: record configurations
Place n points in a square or triangle so the smallest triangle they form is as large as possible; beat a record or prove one optimal.
Heilbronn's triangle problem: record configurations
Place points in a square (or a triangle, or any convex region) so that the smallest triangle formed by three of them is as large as possible. Optimal configurations are proved only up to in the square and in the other containers, while records for larger change almost daily. Every candidate is verified exactly in milliseconds.
Problem
For a container of unit area and points , let
The Heilbronn number is the maximum of over all configurations of points in . The record table tracks three variants: a unit square, a triangle of unit area (the problem is affine-invariant, so all triangles are equivalent), and the "convex" variant, in which the points may lie in any convex region of unit area, chosen together with the points.
Task: find configurations that beat the current records, or prove that a record is optimal. The smallest open cases are in the square and in the triangle and in the convex variant.
Background
Heilbronn asked how large the smallest triangle can be for points in a unit region; the asymptotic question, between the lower bound of order of Komlós, Pintz and Szemerédi and the upper bound of Cohen, Pohoata and Zakharov (2024), is a famous problem in its own right; an OpenAI preprint (September 2026, not refereed or formalized) claims a power improvement of the lower bound, to for a tiny fixed . For small the exact optima are hard: the configurations have many tied minimal triangles, and certified answers have needed symbolic computation or global mixed-integer optimization. Erich Friedman's Packing Center kept the record tables for decades; the current site rebuilds them from exact coordinates.
What is known
From the record site (October 2026):
- Square: optimal configurations are certified for ; Sudermann-Merx (2026) certified all of them by mixed-integer nonlinear optimization and recovered exact coordinates, after earlier proofs for (Zeng and Chen, 2011) and (Dehbi and Zeng, 2022). For the record, (a root of ), is Comellas and Yebra's configuration from 2001–2002.
- Triangle: optimal for (Sudermann-Merx, 2026). The record for is (Cantrell, 2006).
- Convex: optimal values for were formalized in Lean 4 by Stead (2026). The record for is about (Cantrell, 2007).
- Larger n: records are listed up to in each variant and were still being improved in October 2026.
What would count as progress
- A configuration beating a record for some and container. The site's verifier computes all triangle areas in exact rational arithmetic, so a candidate given by rational coordinates is checked in milliseconds.
- A proof that a record is optimal, for example the square with or the triangle with , as a certified global optimization run whose model and certificate can be rerun, or as a Lean proof.
- Exact algebraic values for records known only numerically.
References
- F. Comellas and J. L. A. Yebra, "New lower bounds for Heilbronn numbers", Electronic Journal of Combinatorics 9 (2002), R6. doi:10.37236/1623
- L. Dehbi and Z. Zeng, "Heilbronn's problem of eight points in the square", Journal of Systems Science and Complexity 35 (2022), 2452–2480. doi:10.1007/s11424-022-1220-7
- N. Sudermann-Merx, "From computational certification to exact coordinates: Heilbronn's triangle problem on the unit square using mixed-integer optimization" (2026). arXiv:2603.11107
- N. Sudermann-Merx, "Heilbronn's problem in the unit triangle: certified optimal configurations for up to n ≤ 8" (2026). arXiv:2607.15021
- A. Cohen, C. Pohoata and D. Zakharov, "Lower bounds for incidences" (2024), which gives the Heilbronn upper bound . arXiv:2409.07658
Source
Imported from The Heilbronn Problem, record tables by Tej Stead, curated by Tej Stead. Open according to the source as of 2026-10-20. The statement above is written in our own words; see the source for the original wording, history and discussion.