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Record W4285299972 · doi:10.23952/jano.4.2022.2.08

Adaptive modified inertial projection and contraction methods for pseudomonotone variational inequalities

2022· article· en· W4285299972 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of Applied and Numerical Optimization · 2022
Typearticle
Languageen
FieldComputer Science
TopicContact Mechanics and Variational Inequalities
Canadian institutionsUniversity of British Columbia
FundersChina Scholarship Council
KeywordsInertial frame of referenceVariational inequalityContraction (grammar)MathematicsApplied mathematicsMathematical analysisComputer sciencePhysicsClassical mechanicsMedicine

Abstract

fetched live from OpenAlex

To handle pseudomonotone variational inequality problems in real Hilbert spaces, four modified inertial projection and contraction algorithms with non-monotonic step sizes are suggested in this paper. The proposed algorithms take advantage of a novel non-monotonic step size criteria, allowing them to work without previous knowledge of the Lipschitz constant of the mapping involved. Under certain situations, the strong convergence of the iterative sequences generated by the suggested algorithms is established. Finally, several numerical experiments are offered to validate the theoretical conclusions.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.330

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 head0.033
GPT teacher head0.304
Teacher spread0.271 · 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