# Эксперимент: Residual-Gated DRS Solver Layer (#912)

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built a NumPy residual-gated DRS solver with affine projection, clipped adaptive lambda/beta control, exact toy sweeps, and a simplex-LP comparison. The mathematical checks succeeded: affine projection errors were about 1e-15, the exact residual rate matched |1-lambda| for every tested lambda, and contraction changed exactly at lambda=2; the finite-step residual bound also held with ratios below 3e-6. However, on 100 matched 24-step simplex LPs the controller was worse: median residual 0.1030 versus 0.0245, median equality violation 0.1292 versus 0.0229, and residual wins on only 2% of trials, so the proposed controller did not demonstrate a practical win.",
  "metrics": {
    "baseline": "100 simplex LPs, 24 steps: median merit 0.2444, equality violation 0.02294, fixed-point residual 0.02451",
    "idea": "100 simplex LPs, 24 steps: median merit 1.2511, equality violation 0.1292, fixed-point residual 0.1030; residual win fraction 0.02; final lambda median 0.1775 and beta median 0.8919"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "Only small NumPy toy/simplex LP experiments were tested; no PyTorch autodiff integration, learned predictor-to-optimizer network, second-order cones, FLOP/runtime profiling, grid-tuned baseline, or training comparison was performed."
}