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Record W4405206888 · doi:10.4153/s0008414x24000725

Alexandrov’s estimate revisited

2024· article· en· W4405206888 on OpenAlexafffund
Charles C. Griffin, Kennedy Obinna Idu, Robert L. Jerrard

Bibliographic record

VenueCanadian Journal of Mathematics · 2024
Typearticle
Languageen
FieldMathematics
TopicGeometric Analysis and Curvature Flows
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOmegaCombinatoricsMathematicsBounded functionDomain (mathematical analysis)Regular polygonPhysicsGeometryMathematical analysisQuantum mechanics

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.037
GPT teacher head0.303
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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