The eigenstep method: A new iterative method for unconstrained quadratic optimization.
Bibliographic record
Abstract
This thesis presents a new method for the unconstrained minimization of convex quadratic programming problems. The method is an iterative method that is a modification of the classical steepest descent method. The methods are the same in the choice of the negative gradient as the search direction, but differ in the choice of step size. The steepest descent method uses the optimal step size, and the proposed method uses the reciprocal of the eigenvalues of the Hessian matrix as step sizes. Thus, the proposed method is referred to as the eigenstep method. It will be shown that the eigenstep method has finite termination with the number of iterations required being equal to the dimension of the problem, that is, the number of variables. Numerical examples will be provided to illustrate the algorithm, and a comparison is made to other standard optimization methods, including the steepest descent method.Dept. of Mathematics and Statistics. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .B38. Source: Masters Abstracts International, Volume: 44-01, page: 0364. Thesis (M.Sc.)--University of Windsor (Canada), 2005.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".