Comparative Study of the Quick Convergent Inflow Algorithm (QCIA) and the Modified Quick Convergent Inflow Algorithm (MQCIA)
Bibliographic record
Abstract
The performance of two line search algorithms, the Quick Convergent Inflow Algorithm and the Modified Quick Convergent Inflow Algorithm, used in locating the optimizers of response functions is studied. The methodology requires the use of the same starting experimental design. The indicator variables are the number of iterations and the optimal point reached at each iteration. The Modified Quick Convergent Inflow Algorithm seems to perform generally better than the Quick Convergent Inflow Algorithm in the sense that solutions obtained are much closer to the exact solutions than those obtained using the Quick Convergent Inflow Algorithm. As a consequence to the study, a new algorithm is developed for solving Linear Programming problems. The algorithm iteratively eliminates from an N-sized starting design a point that contributes less to the process as measured by the predictive variances at the design points. The design size is immediately recoverd by adding to the resulting N-1 sized design a design point from the candidate set that optimizes performance. The new algorithm offers approximate solutions to Linear Programming problems as demonstrated with some numerical illustrations.
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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 | 0.005 | 0.014 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".