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Record W2023329664 · doi:10.5539/jmr.v1n2p184

Formulation of Matrix Pade Approximation in Rectangular Full Packed Storage

2009· article· en· W2023329664 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Mathematics Research · 2009
Typearticle
Languageen
FieldEngineering
TopicOptimization and Packing Problems
Canadian institutionsnot available
Fundersnot available
KeywordsPadé approximantTriangular matrixMathematicsMatrix (chemical analysis)Square matrixPolynomial matrixInverseBand matrixSingle-entry matrixMatrix polynomialPolynomialCombinatoricsMathematical analysisGeometrySymmetric matrixPure mathematicsPhysicsMaterials science

Abstract

fetched live from OpenAlex

The Extended Euclidean algorithm for matrix Pade approximants is applied to compute matrix Pade approximants in rectangularfull packed format (RFP) if the coefficient matrices of the input matrix polynomial are triangular. The proceduregiven by Gustavson et al for packing a triangular matrix in rectangular full packed format is applied to pack sequenceof lower triangular matrices of a matrix polynomial in Rectangular Full Packed format. This RFP format of a matrixpolynomial is applied to compute matrix Pade approximants of the matrix polynomial using Matrix Pade Extended EuclideanAlgorithm. Algorithms for the multiplication of two triangular matrices and inverse of a triangular matrix in RFPformat are also presented. The CPU time and memory comparison in computing the matrix Pade approximants of a matrixpolynomial between RFP format case and non packed case are elucidated in detail.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.055
GPT teacher head0.355
Teacher spread0.300 · 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