# Эксперимент: Fully-corrective greedy neuron growth (#1011)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a fully-corrective greedy ReLU neuron-growth optimizer with constrained atom parameterization, residual-correlation search, and joint ridge refitting. Three mechanism checks passed: normalized-score scale invariance held to about 1e-12, the orthogonalized least-squares reduction identity held to 1.1e-13 relative error, and the best correlation increased monotonically with candidate-pool size (2.09 to 3.20). The width-error curve improved monotonically, but its observed log-log slope was -3.69 rather than the predicted -1 regime; greedy growth was faster than the separately trained MLP at width 16 (10.8s vs 26.6s), while the MLP sometimes achieved lower validation error.",
  "metrics": {
    "baseline": "End-to-end width-specific tanh MLP: validation MSE at widths 0/4/8/16 = 0.5223/0.00166/0.00522/0.000128; width-16 cumulative runtime 26.62s.",
    "idea": "Fully-corrective greedy ReLU growth: validation MSE at widths 0/4/8/16 = 0.5223/0.04478/0.003558/0.000170; width-16 cumulative runtime 10.82s. Exact LS reduction relative error 1.06e-13; normalized scaling ratios approximately 1.0; candidate-pool best scores 2.09, 2.53, 3.06, 3.20 for pool sizes 20, 100, 500, 2000; width log-log slope -3.69."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json",
    "run_output.txt"
  ],
  "limitations": "Only a small one-dimensional noiseless function-regression task was tested; no Burgers/operator dataset, L1-constrained refit, larger-dimensional projection, or rigorous FLOP-matched comparison was performed. The MLP comparison used one seed per width, while the greedy candidate search used a small fixed number of restarts; the observed 1/K scaling prediction was not confirmed."
}