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Record W4211010636 · doi:10.21105/joss.03991

DCISolver.jl: A Julia Solver for Nonlinear Optimization using Dynamic Control of Infeasibility

2022· article· en· W4211010636 on OpenAlexafffund
Tangi Migot, Dominique Orban, Abel Soares Siqueira

Bibliographic record

VenueThe Journal of Open Source Software · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsPolytechnique MontréalGroup for Research in Decision AnalysisHEC Montréal
FundersNatural Sciences and Engineering Research Council of CanadaInstitut de Valorisation des DonnéesCanada First Research Excellence Fund
KeywordsSolverNonlinear systemMathematical optimizationComputer scienceMathematicsControl (management)Control theory (sociology)Applied mathematicsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

DCISolver.jl is a new Julia (Bezanson et al., 2017) implementation of the Dynamic Control of Infeasibility method (DCI), introduced by Bielschowsky & Gomes (2008), for solving the equality-constrained nonlinear optimization problem minimize* corresponding author

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0360.008

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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations2
Published2022
Admission routes2
Has abstractyes

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