# Эксперимент: Boundary-Compressed Approximate Pruning (#702)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a readable boundary-compressed decision-diagram prototype for cardinality-constrained quadratic pruning, with exact subset enumeration, local-boundary truncation, backtracking, and fixed-seed sweeps. The mechanism manifested: at eta=3 the gap was 0.0315 versus 0.9886 for magnitude pruning, eta=4 reached the exact objective on the main instance, and fixed eta=3 state count scaled exactly as 4n-4 (R²=1.0). Interaction sensitivity also appeared: the gap was 0 at rho=0 and increased to 0.0366 at rho=0.9, with correlation 0.86; this supports locality-dependent approximation, though it is not a full neural-network pruning validation.",
  "metrics": {
    "baseline": "Magnitude selection: objective 3.1511, gap 0.9886 above exact optimum 2.1625.",
    "idea": "Boundary DP, n=20 and k=10: eta=3 objective gap 0.0315 with 76 peak states; eta=4 gap 0.0 with 144 peak states. At eta=3, peak states were 36, 52, 68, 84, 100, 116, 156, and 196 for widths 10, 14, 18, 22, 26, 30, 40, and 50 respectively, exactly matching 4n-4."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 boundary_pruning.py",
  "files": [
    "boundary_pruning.py",
    "results.json"
  ],
  "limitations": "This is a toy quadratic subset-selection experiment, not a trained neural network or physical structured-pruning benchmark. It uses binary z decisions with fixed cardinality and does not implement continuous rescaling x, learned interaction estimation, inverse-precision graphs, epsilon-rounded residual keys, fine-tuning, wall-clock comparisons, or the theorem's formal epsilon-exact guarantee. The observed eta transition was tested empirically rather than fit to a log(1/epsilon) law."
}