Static Optimization based on Output Feedback
Bibliographic record
Abstract
In the framework of process optimization, the use of measurements to compensate the effect of uncertainty has re-emerged as an active area of research. One of the ideas therein is to adapt the inputs in order to track the active constraints and push certain sensitivities to zero. In perturbation-based optimization, the sensitivities are evaluated by perturbation of the inputs and measurement of the cost function, which can be experimentally time consuming. However, since more measurements (typically the outputs) than just the cost function are available, the idea developed in this paper is to incorporate the outputs in a measurement-based optimization framework. This is done using an extension to the neighboring-extremal scheme for the case of output measurements. If measurement noise can be neglected, the approach is shown to converge to the optimum in at most two input updates. The effect of measurement noise is ∗Current address: Department of Chemical Engineering, Ecole Polytechnique Montreal, Montreal, Canada also investigated. The strength of neighboring-extremal output feedback for optimization is illustrated on a continuous chemical reactor example.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".