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Record W1964920841 · doi:10.1108/02644400410554344

Optimal minimax algorithm for integrating fast oscillatory functions in two dimensions

2004· article· en· W1964920841 on OpenAlexaff
K.N. Zotsenko, Roderick Melnik

Bibliographic record

VenueEngineering Computations · 2004
Typearticle
Languageen
FieldMathematics
TopicMathematical Approximation and Integration
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsBilinear interpolationPiecewiseMinimaxMathematicsInterpolation (computer graphics)Range (aeronautics)AlgorithmNumerical integrationConstant (computer programming)Lipschitz continuityFocus (optics)Applied mathematicsMathematical optimizationSpline (mechanical)Computer scienceMathematical analysisImage (mathematics)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we give a complete description of efficient formulae for the numerical integration of fast oscillating functions of two variables. The focus is on the case encountered frequently in many engineering applications where an accurate value of the Lipschitz constant is not available. Using spline approximations, we demonstrate the main idea of our approach on the example of piecewise bilinear interpolation, and propose optimal‐by‐order (with a constant not exceeding two) cubature formulae that are applicable for a wide range of oscillatory patterns. This property makes the formulae indispensable in many engineering applications dealing with signal processing and image recognition. Illustrative results of numerical experiments are presented.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.300
Teacher spread0.276 · 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 designTheoretical or conceptual
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

Citations5
Published2004
Admission routes1
Has abstractyes

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