Design and Validation of Optimizer Parameter Critical Conditions via Coupled Transfer Functions and Phase Trajectories
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The design of optimizer parameters affects model performance and is widely applied in fields such as image analysis, autonomous driving, and security monitoring. However, the interpretability and generalizability of optimizers are insufficient, limiting their practical applications. To address these challenges, we introduce a novel approach using transfer function and phase trajectory methods to design the parameters and critical conditions for Stochastic Gradient Descent with Momentum (SGD-M) and Nesterov Accelerated Gradient (NAG). The proposed theory is verified through numerical examples and image recognition experiments. First, using the phase trajectory method, a qualitative analysis of the responses of SGD-M and NAG to initial states is conducted, revealing the influence of parameters on the phase trajectory. Then, through the transfer function method, a quantitative analysis of the unit step response of SGD-M and NAG is performed to explain the impact of parameters on system response. Finally, numerical examples and image recognition experiments verify the significant impact of the momentum control parameter g(μ) and momentum parameter α on optimizer performance, stability, and time-domain characteristics. Experimental results show that adjusting g(μ) or α improves image classification accuracy on the Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research (CIFAR-10) datasets. It reduces the loss value, validating the effectiveness of the proposed theory.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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