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Record W4282973889 · doi:10.48550/arxiv.2206.06861

The Stieltjes--Fekete problem and degenerate orthogonal polynomials

2022· preprint· en· W4282973889 on OpenAlexfundno aff
Marco Bertola, Eduardo Chavez-Heredia, Тамара Грава

Bibliographic record

VenueBristol Research (University of Bristol) · 2022
Typepreprint
Languageen
FieldMathematics
TopicMathematical functions and polynomials
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaGruppo Nazionale per la Fisica MatematicaHorizon 2020 Framework ProgrammeEuropean CommissionIstituto Nazionale di Alta Matematica "Francesco Severi"
KeywordsRiemann–Stieltjes integralOrthogonal polynomialsMathematicsOrthogonalityDegenerate energy levelsLogarithmClassical orthogonal polynomialsDiscrete orthogonal polynomialsPure mathematicsField (mathematics)Algebraic numberMathematical analysisDiscrete mathematicsIntegral equationGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

A famous result of Stieltjes relates the zeroes of the classical orthogonal polynomials with the configurations of points on the line that minimize a suitable energy. The energy has logarithmic interactions and an external field whose exponential is related to the weight of the classical orthogonal polynomials. The optimal configuration satisfies an algebraic set of equations: we call this set of algebraic equations the Stieltjes--Fekete problem or equivalently the Stieltjes--Bethe equations. In this work we consider the Stieltjes-Fekete problem when the derivative of the external field is an arbitrary rational complex function. We show that its solutions are in one-to-one correspondence with the zeroes of certain non-hermitean orthogonal polynomials that satisfy an excess of orthogonality conditions and are thus termed "degenerate". This generalizes the original result of Stieltjes.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.176
GPT teacher head0.380
Teacher spread0.204 · 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.

Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2022
Admission routes1
Has abstractyes

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