# Эксперимент: Primal-Dual Active-Set Optimizer Filter (#894)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a primal-dual active-set solver for strictly convex inequality QPs with warm-started active sets, multiplier checks, violated-constraint additions, and negative-multiplier removals. The activity transition matched the predicted alpha=0.4625 within the 0.05 sweep resolution, dual scaling matched the predicted slope 0.8 to machine precision, and smooth trajectories used one iteration on 92.5% of steps with 2 active-set changes and maximum KKT residual 5.6e-17. On the small toy comparison, PDAS reached the same objective as sequential projected gradient while averaging 1.59 ms versus 44.16 ms, but this is not evidence of a neural-training speedup.",
  "metrics": {
    "baseline": "Sequential projected-gradient approximation: mean objective 1.1247975962, mean time 44.16 ms, max constraint violation 0",
    "idea": "PDAS: mean objective 1.1247975962, mean time 1.59 ms, mean warm-start iterations 1.10, 92.5% one-iteration solves, max KKT residual 5.6e-17; independent SLSQP solution difference <1.9e-6"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py",
  "files": [
    "pdas_filter.py",
    "run_experiment.py",
    "results.json"
  ],
  "limitations": "Only small synthetic QPs were tested. No CIFAR-10/MLP integration, full-parameter or autodiff barrier gradients, OSQP comparison, GPU benchmark, scaling study, infeasibility handling, or high-dimensional matrix-free implementation was tested."
}