# Эксперимент: Structured-μ Robust Optimizer (#1122)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a readable scalar Structured-μ feedback optimizer MVP with an analytic frequency-domain μ estimate, conservative certificate, worst-case uncertainty simulation, and noisy matched baseline comparison. The core formula matched the frequency sweep to machine precision; the predicted boundary r=1-|a-c| was observed at 0.54 versus 0.55 (grid error 0.01), and the predicted optimal gain c=a=0.8 was recovered exactly. The idea reduced worst-case pole magnitude from 0.90 for the baseline to 0.45 and halved median final error in the toy experiment, but no MLP, Transformer, learned controller, delay model, or genuine structured multi-block μ computation was tested.",
  "metrics": {
    "baseline": "gain c=0.35; worst-case pole magnitude 0.90; median final |x|=0.01256; max-over-10 fraction=0.0",
    "idea": "gain c=0.8; estimated μ=0.45; worst-case pole magnitude 0.45; median final |x|=0.00636; max-over-10 fraction=0.0; predicted/observed uncertainty boundary 0.55/0.54"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 structured_robust_optimizer.py",
  "files": [
    "structured_robust_optimizer.py",
    "results.json"
  ],
  "limitations": "The implementation is a scalar zero-order controller rather than the proposed adaptive state-space controller. It does not fit local plants, estimate residual covariance, perform balanced truncation, or train MNIST/Transformer models; the random-perturbation divergence sweep also showed gradual rather than sharp probability growth because only a small subset of uniformly sampled perturbations are near worst case."
}