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Record W4408945734 · doi:10.1137/24m1631572

Semidifferential of Minimax of Lagrangians with Respect to Parameters, Controls, or Geometric Variables

2025· article· en· W4408945734 on OpenAlexfundno aff
Michel C. Delfour

Bibliographic record

VenueSIAM Journal on Control and Optimization · 2025
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Mathematical Modeling in Engineering
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMathematicsMinimaxApplied mathematicsMathematical economicsMathematical optimizationCalculus (dental)

Abstract

fetched live from OpenAlex

Abstract. The object of this paper is to further investigate the use of the minimax of a parametrized Lagrangian to get the semidifferential, that is, the one-sided directional derivative, of objective functions constrained by variational equations or inequalities, which depend on parameters, controls, or the shape/topology of the underlying geometric domain. It extends earlier results to Lagrangians with or without saddle point arising from state constrained objective functions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0010.004
Scholarly communication0.0040.005
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractno

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Same venueSIAM Journal on Control and OptimizationSame topicAdvanced Mathematical Modeling in EngineeringFrench-language works237,207