Bibliographic record
Abstract
This special issue of NLAA is devoted to the important theme of 'Preconditioning Techniques for Large Sparse Matrix Problems in Industrial Applications' dubbed 'sparse99'.An international conference with the same title was held on 10-12 June, 1999 at the University of Minnesota (Humphrey Institute), in Minneapolis.The conference was sponsored by the University of Minnesota Supercomputing Institute for Digital Simulation and Advanced Computation, the University of Waterloo and the Lawrence Livermore National Laboratory.Eighty-seven participants came from all over the world: 39 from Africa, Australia, Asia, Europe and South America and 48 from the United States and Canada.The conference aimed at bringing together researchers to discuss the latest developments in preconditioning methods and the use of iterative procedures in scientific and engineering applications.The organizing committee, Yousef Saad (University of Minnesota), Daniel Pierce (Boeing Company), and Wei-Pai Tang (University of Waterloo), strived for an equal array of talks on applications and algorithms.This goal was reached with 15 of the 32 talks falling into the applications category.In order to give a general idea of the main topics represented at the meeting, I will next give a brief overview of the presentations.The opening talk, in the category of applications, was given by Charbel Farhat of the University of Colorado at Boulder.His talk, '10 years of FETI (Finite Element Tearing Interconnections)' was an overview of the impressive work done by a team at the University of Colorado at Boulder in solving a large variety of problems in mechanics.Peter Forsyth of the University of Waterloo, Canada, presented an introduction to problems in computational finance and discussed his experience in dealing with these problems.Professor Forsyth presented some experiments in using iterative methods for the solution of Partial Differential Equations resulting from problems in computational finance.Presentations on recent improvements or applications to the class of approximate inverse methods were given by Alex Yeremin from the Russian Academy of Sciences, Russia, Edmond Chow from Lawrence
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.037 | 0.033 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".