[Paper] Optimizing Explicit Unit-Distance Lower-Bound Certificates

Source: arXiv - 2606.03419v2

Overview

The 2026 disproof of Erdős’s unit-distance conjecture and Sawin’s subsequent explicit quantitative refinement show that the maximum number $u(n)$ of unit distances among $n$ planar points can exceed $n^{1+\varepsilon}$ for a fixed positive $\varepsilon$. Sawin’s explicit bound gives more than $n^{1.014}$ unit distances for arbitrarily large $n$ and exposes integer parameters whose choice is not fully optimized. This report starts from Sawin’s nonlinear integer optimization problem and develops an open-source Python verification pipeline. The pipeline is first validated by reproducing Sawin’s published parameter choice and is then applied to computationally improved certificates. We optimize and verify certificates involving sets of primes $T$ and $S_Q$, integer multiplicities $k(p)$, and a rationally encoded real parameter $R$. The implementation is deliberately lean, so that all results can be replicated on standard hardware and the procedures can be extended. We compare a deterministic greedy heuristic, a tailored integer evolution strategy with two-sided geometric, or discrete-Laplace, integer mutation and repair operators for number-theoretic feasibility, and a two-parent discrete-recombination variant. Four certificate levels are reported: Sawin’s published example with $δ=0.0141144286784982\ldots$, a greedy certificate with $δ=0.0151718056372133\ldots$, a tailored integer evolution strategy certificate with $R=6672416/100000$ and $δ=0.0152616610684193\ldots$, and a recombination variant with the same $R$ and $δ=0.0152628688170072\ldots$. Consequently, the best current certificate supports the cautious statement $u(n)>n^{1.0152}$ for arbitrarily large $n$. Beyond this unit-distance application, the work illustrates how randomized optimization heuristics can improve explicit certificates in pure mathematics and combinatorial geometry.

Key Contributions

This paper presents research in the following areas:

• math.OC
• cs.AI
• cs.CG
• cs.NE
• math.CO

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of math.OC.

Authors

• Michael T. M. Emmerich

Paper Information

• arXiv ID: 2606.03419v2
• Categories: math.OC, cs.AI, cs.CG, cs.NE, math.CO
• Published: June 2, 2026
• PDF: Download PDF