A Globally Convergent Composite-Step Trust-Region Framework for Real-Time Optimization with Plant-Model Mismatch
Bibliographic record
Abstract
Inaccurate models limit the performance of model-based real-time optimization (RTO) and even cause system instability. Therefore, a RTO framework that can guarantee global convergence with the presence of plant-model mismatch is desired. In this regard, the trust-region framework is simple to implement and guarantees globally convergent for unconstrained problems. However, it remains to be seen if the trust-region strategy can handle inequality constraints directly with the common model adaptation method. This paper addresses this issue and proposes a novel composite-step trust-region framework that guarantees global convergence for constrained RTO problems. The trial step is decomposed into a normal step that improves feasibility and a tangential step that reduces the cost function. In each iteration, the model optimization problem with relaxed constraints is solved. The proof of the global convergence property under structural plant-model mismatch is given.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".