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Record W3183856668 · doi:10.1002/nla.2399

ODE‐based double‐preconditioning for solving linear systems Aαx=b and f(A)x=b

2021· article· en· W3183856668 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.

Bibliographic record

VenueNumerical Linear Algebra with Applications · 2021
Typearticle
Languageen
FieldComputer Science
TopicMatrix Theory and Algorithms
Canadian institutionsUniversité de MontréalCarleton UniversityStatistics Canada
Fundersnot available
KeywordsOdeMathematicsLinear systemComputationApplied mathematicsOrdinary differential equationExtension (predicate logic)Matrix (chemical analysis)Linear differential equationSystem of linear equationsCoefficient matrixAlgebraic numberAlgebra over a fieldAlgorithmDifferential equationPure mathematicsMathematical analysisComputer science

Abstract

fetched live from OpenAlex

Abstract This article is devoted to the computation of the solution to fractional linear algebraic systems using a differential‐based strategy to evaluate matrix–vector products , with . More specifically, we propose ODE‐based preconditioners for efficiently solving fractional linear systems in combination with traditional sparse linear system preconditioners. Different types of preconditioners are derived (Jacobi, incomplete LU, Padé) and numerically compared. The extension to systems is finally considered.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.968
Threshold uncertainty score0.624

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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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