On Hadwiger’s covering problem in small dimensions
Bibliographic record
Abstract
Abstract Let $H_n$ be the minimal number such that any n -dimensional convex body can be covered by $H_n$ translates of the interior of that body. Similarly $H_n^s$ is the corresponding quantity for symmetric bodies. It is possible to define $H_n$ and $H_n^s$ in terms of illumination of the boundary of the body using external light sources, and the famous Hadwiger’s covering conjecture (illumination conjecture) states that $H_n=H_{n}^s=2^n$ . In this note, we obtain new upper bounds on $H_n$ and $H_{n}^s$ for small dimensions n . Our main idea is to cover the body by translates of John’s ellipsoid (the inscribed ellipsoid of the largest volume). Using specific lattice coverings, estimates of quermassintegrals for convex bodies in John’s position, and calculations of mean widths of regular simplexes, we prove the following new upper bounds on $H_n$ and $H_n^s$ : $H_5\le 933$ , $H_6\le 6137$ , $H_7\le 41377$ , $H_8\le 284096$ , $H_4^s\le 72$ , $H_5^s\le 305$ , and $H_6^s\le 1292$ . For larger n , we describe how the general asymptotic bounds $H_n\le \binom {2n}{n}n(\ln n+\ln \ln n+5)$ and $H_n^s\le 2^n n(\ln n+\ln \ln n+5)$ due to Rogers, Shephard and Roger, Zong, respectively, can be improved for specific values of n .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".