# Эксперимент: Impedance-Calibrated Learning-Rate Control (#1214)

{
  "worked": false,
  "confidence": 8,
  "verdict": "Built a reproducible lock-in transfer-function sanity check, quadratic stability test, and small CUDA digits-MLP comparison in experiment.py. The core math was confirmed: corrected lock-in recovery had 0.82% relative error, and the quadratic transition occurred at the predicted eta=2/lambda_max=0.4. The training controller showed a promising result versus the selected fixed and cosine baselines, with test loss 0.0755 versus 0.1098 and 0.1616, but this is not a demonstrated impedance-control win because the implementation used Hessian power estimates rather than fitted sinusoidal poles/residues, and fixed-rate eta=0.6 performed better.",
  "metrics": {
    "baseline": "Fixed SGD eta=0.08: test loss 0.1098, accuracy 97.78%; cosine SGD: test loss 0.1616, accuracy 96.67%; fixed-rate eta=0.6 reached sampled final loss 0.0265.",
    "idea": "Curvature-controlled SGD: test loss 0.0755, accuracy 98.00%, final learning rate 0.35; lock-in transfer relative error 0.0082; predicted critical eta 0.400, grid estimate 0.395; quadratic eta=0.39 decayed while eta=0.41 and 0.50 grew."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json",
    "run_output.txt"
  ],
  "limitations": "Only one random seed and one small sklearn digits dataset were tested. The real-training prototype does not implement the proposed multi-frequency probing, pole/residue nonlinear fit, or loss/gradient lock-in estimator; it estimates curvature directly with a short Hessian-vector power iteration. No FLOP-normalized or repeated-seed comparison, larger model, MNIST, or robust instability-boundary sweep was performed."
}