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Record W1919986261 · doi:10.1002/nla.1845

The power and Arnoldi methods in an algebra of circulants

2012· article· en· W1919986261 on OpenAlexafffund
David F. Gleich, Chen Greif, J. M. Varah

Bibliographic record

VenueNumerical Linear Algebra with Applications · 2012
Typearticle
Languageen
FieldMathematics
TopicTensor decomposition and applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCentre International de Recherche sur le Cancer
KeywordsMathematicsCirculant matrixAlgebra over a fieldLinear algebraScalar (mathematics)Dimension (graph theory)Matrix (chemical analysis)Numerical linear algebraPure mathematicsDiscrete mathematicsNumerical analysisMathematical analysis

Abstract

fetched live from OpenAlex

SUMMARY Circulant matrices play a central role in a recently proposed formulation of three‐way data computations. In this setting, a three‐way table corresponds to a matrix where each ‘scalar’ is a vector of parameters defining a circulant. This interpretation provides many generalizations of results from matrix or vector‐space algebra. These results and algorithms are closely related to standard decoupling techniques on block‐circulant matrices using the fast Fourier transform. We derive the power and Arnoldi methods in this algebra. In the course of our derivation, we define inner products, norms, and other notions. These extensions are straightforward in an algebraic sense, but the implications are dramatically different from the standard matrix case. For example, the number of eigenpairs may exceed the dimension of the matrix, although it is still polynomial in it. It is thus necessary to take an extra step and carefully select a smaller, canonical set of size equal to the dimension of the matrix from which all possible eigenpairs can be formed. Copyright © 2012 John Wiley & Sons, Ltd.

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)0.005
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.376
Teacher spread0.339 · 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
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

Citations46
Published2012
Admission routes2
Has abstractyes

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