Progressive Validity Trust Region Optimization
Bibliographic record
Abstract
This paper presents a cohesive metamodel trust region optimization (MTRO) strategy where the validity of the metamodel is used by the trust region to reduce the number of sample points needed to construct metamodels for each step of the optimization process. Lower validity metamodels are used for the larger trust regions at the beginning of the optimization, and higher validity metamodels are used for the smaller trust regions at the end of optimization. This progressive validity method minimizes the number of points in each stage of the metamodel. Tools created by other researchers are tested to determine the best possible metamodeling strategy for MTRO: optimal latin hypercube sampling is used to generate space lling experimental designs; inherited latin hypercube design allows the reuse of sample points from earlier in the trust region optimization; a quasi-Newton scheme is used to reduce the minimum set of sample points for the given metamodel; a kriging metamodel provides an accurate and robust representation of the design space; an ecient polynomial regression metamodel provides an alternative; and the leavekout cross-validation provides ecient validity measurements. MTRO is tested within the multidisciplinary optimization framework ( MDO) with single discipline problems. Results from MTRO are compared to a traditional trust region method.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".