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Record W2013927575 · doi:10.1137/100812483

Monotonicity of Perturbed Tridiagonal $M$-Matrices

2012· article· en· W2013927575 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSIAM Journal on Matrix Analysis and Applications · 2012
Typearticle
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsTridiagonal matrixMathematicsRank (graph theory)Monotonic functionToeplitz matrixTridiagonal matrix algorithmApplied mathematicsMatrix (chemical analysis)InverseLinear algebraCombinatoricsPure mathematicsMathematical analysisGeometry

Abstract

fetched live from OpenAlex

A well-known property of an $M$-matrix is that its inverse is elementwise nonnegative, which we write as $M^{-1} \geq 0$. In a previous paper [Linear Algebra Appl., 434 (2011), pp. 131--143], we gave sufficient bounds on single element perturbations so that monotonicity persists for a perturbed tridiagonal $M$-matrix. Here we extend these results, presenting the actual maximum upper bounds on single element perturbations, as well as sufficient and necessary conditions for the maximum allowable higher rank perturbations. Perturbed Toeplitz tridiagonal $M$-matrices are considered as a special case. We compare our results to existing normwise bounds due to Bouchon and an iterative algorithm provided by Buffoni. We demonstrate the utility of these results by considering an application: ensuring a nonnegative solution of a discrete analogue of an integro-differential population model.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.293
Teacher spread0.285 · 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