Эксперимент: Observer-Corrected Robust Optimizer (#524)

{ "worked": true, "confidence": 9, "verdict": "Built a scalar quadratic observer-corrected optimizer MVP using one-step transition residuals and EMA disturbance estimation with subtractive feedforward correction. The stability boundary h=2/lambda matched within 0.20%, measured frequency response matched the analytic EMA formula within 0.99%, and slow disturbance residual fell to 4.23% while high-frequency residual rose to 195.8% at aggressive gain. The mechanism is real in this toy setting, but it is not a universal optimizer win: slow-sine parameter RMS improved from 0.2344 to 0.0099, whereas high-sine RMS worsened from 0.0510 to 0.0999.", "metrics": { "baseline": "Stability critical h=2/lambda; slow-sine theta RMS=0.234423; high-sine theta RMS=0.051008.", "idea": "Observed critical-boundary max relative error=0.1956%; EMA frequency-response max relative error=0.982%; slow-sine theta RMS=0.009893 with residual ratio=0.042264; high-sine theta RMS=0.099892 with residual ratio=1.958366." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 observer_optimizer_experiment.py && /home/maxwelhelp/main/bin/python3 make_report.py", "files": [ "observer_optimizer_experiment.py", "make_report.py", "observer_results.json", "observer_report.json", "observer_report.txt", "run_output.txt" ], "limitations": "Only a scalar quadratic was tested. No MLP/MNIST training, momentum state, curvature-regime scheduling, noisy transition measurements, multidimensional Hessian, or realistic minibatch gradient estimation was evaluated; exact transition identification is more favorable than a practical neural optimizer." }