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Record W2962704982 · doi:10.48550/arxiv.1402.5460

Linear and strong convergence of algorithms involving averaged\n nonexpansive operators

2014· article· en· W2962704982 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuearXiv (Cornell University) · 2014
Typearticle
Languageen
FieldComputer Science
TopicOptimization and Variational Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersDivision of Mathematical SciencesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMathematicsConvergence (economics)Regular polygonFixed pointApplied mathematicsAlgorithmPoint (geometry)Mathematical optimizationMathematical analysisGeometry

Abstract

fetched live from OpenAlex

We introduce regularity notions for averaged nonexpansive operators. Combined\nwith regularity notions of their fixed point sets, we obtain linear and strong\nconvergence results for quasicyclic, cyclic, and random iterations. New\nconvergence results on the Borwein-Tam method (BTM) and on the cylically\nanchored Douglas-Rachford algorithm (CADRA) are also presented. Finally, we\nprovide a numerical comparison of BTM, CADRA and the classical method of cyclic\nprojections for solving convex feasibility problems.\n

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.173
Teacher spread0.139 · 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