Hnm4lcp - Un solveur de problèmes de complémentarité linéaire fondé sur l'algorithme de Newton-min hybride
Bibliographic record
Abstract
Hnm4lcp is a Matlab code to solve a linear complementarity problem (LCP)of the form (0 ≤ x _|_ (M*x+q) ≥ 0,where x in Rn is the real vector of unknowns, M in Rnxn and q in Rn isthe data. This system means that the sought x must be nonnegativecomponentwise (x ≥ 0), y := M*x+q must be nonnegative componentwise (y ≥0) and x and y must be perpendicular for the Euclidean scalar product(x'*y = 0 or x.*y = 0).It is assumed that M is nondegenerate, meaning that all its principalminors are nonzero (i.e., det(M(I,I)) ~= 0 for all I in [1:n]). There isno verification (this is too expensive) and there is no provision in thecode to deal with a degenerate M. The LCP has a unique solution whateverq is if and only if M is a P-matrix (meaning that its principal minorsare positive: det(M(I,I)) > 0 for all I in [1:n]).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".