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Record W4229775072 · doi:10.23952/jano.1.2019.2.03

A Henig conical regularization approach for circumventing the Slater conundrum in linearly $\ell_{+}^{p}$-constrained least squares problems

2019· article· en· W4229775072 on OpenAlex
L. Huerga, Akhtar A. Khan, Miguel Sama

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Applied and Numerical Optimization · 2019
Typearticle
Languageen
FieldMathematics
TopicStatistical and numerical algorithms
Canadian institutionsnot available
FundersEuropean Regional Development FundMinisterio de Ciencia, Innovación y UniversidadesAgencia Estatal de InvestigaciónNational Science Foundation
KeywordsConical surfaceRegularization (linguistics)MathematicsPhysicsCombinatoricsApplied mathematicsGeometryComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we study a linearly p + -constrained least-squares problem. We develop the Henig conical regularization approach as a unified framework to deal with the lack of Slater-type constraint qualification. We establish some stability estimates for the regularized problems. For the separable case, p [1, ), we provide an explicit characterization of the Henig dilating cones associated with p + and the associated regularized KKT systems. For the non-separable case + , we give a condition which ensures that the solution of the least-squares problem can be approximated by regularized solutions whose dual solutions do not contain any finitely additive singular part.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.622
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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