Optimal variable step-size diffusion LMS algorithms
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
We derive theoretical expressions of the optimum step-size for diffusion least-mean squares (LMS) algorithms. The resulting optimal step-size leads to the largest correction for the distributed LMS adaptive filter from iteration i to iteration i + 1. For practical computation, we use time-averaging filters and establish the mean-square stability for adapt-then-combine (ATC) and combine-then-adapt (CTA) strategies. We introduce optimal variable step-size diffusion LMS algorithms with detailed and practical guidelines for their implementation. Simulation results support the analysis and prove that the proposed algorithms significantly improve the performance in both transient phase and steady state. The numerical experiments reveal that, compared with the existing approaches, the proposed adaptive algorithms are less sensitive to control parameters and more robust with respect to statistical variations of the environment.
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 | 0.000 | 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