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Record W4412767577 · doi:10.23952/jano.7.2025.2.03

Strong convergence of an inertial Tseng’s extragradient algorithm for pseudomonotone variational inequalities with applications to optimal control problems

2025· article· en· W4412767577 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Applied and Numerical Optimization · 2025
Typearticle
Languageen
FieldComputer Science
TopicOptimization and Variational Analysis
Canadian institutionsnot available
FundersCentre Scientifique et Technique du BâtimentFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Chongqing
KeywordsVariational inequalityInertial frame of referenceConvergence (economics)AlgorithmMathematicsApplied mathematicsComputer sciencePhysicsEconomics

Abstract

fetched live from OpenAlex

We investigate an inertial viscosity-type Tseng's extragradient algorithm with a new step size for solving pseudomonotone variational inequality problems in real Hilbert spaces.A strong convergence theorem of the proposed algorithm is obtained without the prior information of the Lipschitz constant of the operator and also without any requirement of additional projections.Finally, several computational tests are carried out to demonstrate the reliability and benefits of the proposed algorithm and compare it with some existing ones.Moreover, our algorithm is also applied to address the variational inequality problem that appears in optimal control problems.

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 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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.248
Threshold uncertainty score0.417

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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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 abstractyes

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