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Record W3157614119 · doi:10.3934/cpaa.2021076

Hadamard Semidifferential, Oriented Distance Function, and some Applications

2021· article· en· W3157614119 on OpenAlex

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affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunications on Pure &amp Applied Analysis · 2021
Typearticle
Languageen
FieldComputer Science
TopicOptimization and Variational Analysis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMathematicsCombinatoricsDifferentiable functionArithmeticDiscrete mathematicsPure mathematics

Abstract

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<p style='text-indent:20px;'>The <i>Hadamard semidifferential calculus</i> preserves all the operations of the classical differential calculus including the chain rule for a large family of non-differentiable functions including the continuous convex functions. It naturally extends from the <inline-formula><tex-math id="M1">\begin{document}$ n $\end{document}</tex-math></inline-formula>-dimensional Euclidean space <inline-formula><tex-math id="M2">\begin{document}$ \operatorname{\mathbb R}^n $\end{document}</tex-math></inline-formula> to subsets of topological vector spaces. This includes most function spaces used in <i>Optimization</i> and the <i>Calculus of Variations</i>, the metric groups used in <i>Shape and Topological Optimization</i>, and functions defined on submanifolds.</p><p style='text-indent:20px;'>Certain set-parametrized functions such as the <i>characteristic function</i> <inline-formula><tex-math id="M3">\begin{document}$ \chi_A $\end{document}</tex-math></inline-formula>of a set <inline-formula><tex-math id="M4">\begin{document}$ A $\end{document}</tex-math></inline-formula>, the <i>distance function</i> <inline-formula><tex-math id="M5">\begin{document}$ d_A $\end{document}</tex-math></inline-formula> to <inline-formula><tex-math id="M6">\begin{document}$ A $\end{document}</tex-math></inline-formula>, and the <i>oriented (signed) distance function</i> <inline-formula><tex-math id="M7">\begin{document}$ b_A = d_A-d_{ \operatorname{\mathbb R}^n\backslash A} $\end{document}</tex-math></inline-formula> can be used to identify a space of subsets of <inline-formula><tex-math id="M8">\begin{document}$ \operatorname{\mathbb R}^n $\end{document}</tex-math></inline-formula> with a metric space of set-parametrized functions. Many geometrical properties of domains (convexity, outward unit normal, curvatures, tangent space, smoothness of boundaries) can be expressed in terms of the analytical properties of <inline-formula><tex-math id="M9">\begin{document}$ b_A $\end{document}</tex-math></inline-formula> and a simple intrinsic differential calculus is available for functions defined on hypersurfaces without appealing to local bases or Christoffel symbols.</p><p style='text-indent:20px;'>The object of this paper is to extend the use of the Hadamard semidifferential and of the oriented distance function from finite to infinite dimensional spaces with some selected illustrative applications from shapes and geometries, plasma physics, and optimization.</p>

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.250
Teacher spread0.234 · 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