Comparative Study of the Quick Convergent Inflow Algorithm (QCIA) and the Modified Quick Convergent Inflow Algorithm (MQCIA)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it