An efficient quadratic programming implementation for cross directional control of large papermaking processes
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Bibliographic record
Abstract
In this paper, we consider a linearly constrained quadratic programming (QP) problem arising from cross directional control of large papermaking processes. Different from general-purpose QP solvers, we solve the optimization problem by taking advantage of the problem structure and features, such as positive-definiteness of the Hessian matrix, sparsity of the Hessian and constraint matrices. It is implemented based on a dual feasible, active-set algorithm, a Schur complement method and a warm start strategy. The Schur complement is proved to be nonsingular throughout iterations, which makes the solver numerically very reliable. In comparison with the standard Matlab QP solver, the proposed QP solver is much more efficient in the case studies we performed on real industrial papermaking processes.
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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 | 0.000 | 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