# Эксперимент: Delay-aware event-triggered optimizer (#1073)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built a delayed event-triggered SGD MVP with queued stale corrections, practical Lyapunov-proxy triggering, and deterministic toy sweeps. The predicted delayed-contraction boundary matched the observed sweep within 0.09–4.22%, and inter-event gaps matched the predicted linear sigma*sqrt(V)/B scaling exactly. On a 20-dimensional quadratic, event triggering reduced transmissions by 66.8% while reaching final loss 4.61e-18, although this is only a small deterministic toy result.",
  "metrics": {
    "baseline": "Ordinary SGD: final loss 2.91e-13 after 250 steps; fixed-delay SGD: 2.70e-30 with 250/250 events.",
    "idea": "Event-triggered delayed SGD: final loss 4.61e-18 after 250 steps, 83/250 events, 66.8% communication reduction, minimum event gap 3 steps. Contraction-boundary relative errors were 0.09%, 0.12%, 0.24%, and 4.22% for epsilon values 0, 0.2, 0.5, and 0.75."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json",
    "REPORT.md"
  ],
  "limitations": "The Lyapunov boundary uses an exact scalar worst-case recurrence rather than independently estimated dynamics. The optimizer test is a small diagonal quadratic with deterministic gradients; MNIST, stochastic gradients, wall-clock communication cost, learned or measured Lyapunov constants, and distributed hardware were not tested."
}