The Gauss-Newton direction in semidefinite programming
Bibliographic record
Abstract
Most of the directions used in practical interior-point methods for semideinite programming try to follow the approach used in linear programming, i.e.f they are defined using the optimally conditions which are modified with a symmetrization of the perturbed complementarity conditions to allow for application of Newton's method. It is now understood that all the Moeteiro-Zhang family, which include, among others, the popular AHO, NT, HKM, Gu, and Toh directions, can be expressed as a scaling of the problem data and of the iterate followed by the solution of the AHO system of equations, followed by the inverse scaling. All these directions therefore share a defining system of equations. The focus of this work is to propose a defining system of equations that is essentially different from the AHO system: the over-determined system obtained from the minimization of a nonlinear equation. The resulting solution is called the Gauss-Newton
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".