Alexandrov’s estimate revisited
Bibliographic record
Abstract
Abstract Alexandrov’s estimate states that if $\Omega $ is a bounded open convex domain in $\mathbb {R}^n$ and $u:\bar \Omega \to \mathbb {R}$ is a convex solution of the Monge-Ampère equation $\det D^2 u = f$ that vanishes on $\partial \Omega $ , then $$\begin{align*}|u(x) - u(y)| \le \omega(|x-y|)(\int_\Omega f)^{1/n} \qquad \text{for }\omega(\delta) = C_n\,\text{diam}(\Omega)^{\frac{n-1}n} \delta^{1/n}. \end{align*}$$ We establish a variety of improvements of this, depending on the geometry of $\partial \Omega $ . For example, we show that if the curvature is bounded away from $0$ , then the estimate remains valid if $\omega (\delta )$ is replaced by $C_\Omega \delta ^{\frac 12 + \frac 1{2n}}$ . We determine the sharp constant $C_\Omega $ when $n=2$ , and when $n\ge 3$ and $\partial \Omega $ is $C^2$ , we determine the sharp asymptotics of the optimal modulus of continuity $\omega _\Omega (\delta )$ as $\delta \to 0$ . For arbitrary convex domains, we characterize the scaling of the optimal modulus $\omega _\Omega $ . Our results imply in particular that unless $\partial \Omega $ has a flat spot, $\omega _\Omega (\delta ) = o(\delta ^{1/n})$ as $\delta \to 0$ , and under very mild nondegeneracy conditions, they yield the improved Hölder estimate, $\omega _\Omega (\delta ) \le C \delta ^\alpha
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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 source (direct Gemma or distilled Codex), 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".