A Note on the Separation of Optimal Quantization and Control Policies in Networked Control
Bibliographic record
Abstract
For controlled $\mathbb{R}^n$-valued linear systems driven by Gaussian noise under quadratic cost criteria, we revisit the problem of the structure of optimal quantization and control policies. In a recent paper [IEEE Trans. Automat. Control, 59 (2014), pp. 1612--1617] by the author, for fully observed and partially observed systems, the global optimality of predictive encoders was established under quadratic cost criteria. Furthermore, optimal control policies were shown to be linear in the conditional estimate of the state, and a form of separation of estimation and control was established. The present note does not introduce any new results or new conditions but clarifies that the results have been mischaracterized in the recent paper [M. Rabi, C. Ramesh, and K. H. Johansson, SIAM J. Control Optim., 54 (2016), pp. 662--689]. Since perhaps the arguments in [IEEE Trans. Automat. Control, 59 (2014), pp. 1612--1617] were concise and this led to the confusion, its key result is presented here with a more detailed proof.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".