Эксперимент: Armijo acceptance with decoupled LM damping (#16)

{ "worked": true, "confidence": 8, "verdict": "Built a NumPy nonlinear least-squares benchmark comparing decoupled Armijo LM against conventional LM that increases damping and re-solves after rejected full steps. The quadratic-model and reduction-ratio identities passed at approximately 1e-15 error. Across five seeds, the idea used 35 solves every run versus 42.6 baseline solves on average (17.6% fewer), and mean final loss was lower (0.1181 versus 0.1426), but it won on final loss only 2/5 runs and was generally not faster because Armijo backtracking added function evaluations.", "metrics": { "baseline": "5-seed mean final loss 0.1426035; mean 42.6 LM solves; mean rejected trials 7.6; representative runtime 1.067 s", "idea": "5-seed mean final loss 0.1181096; exactly 35 LM solves; mean rejected trials 12.6; representative runtime 0.977 s; lower loss in 2/5 seeds" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py" ], "limitations": "Tiny full-batch NumPy MLP regression only; identity projection was used rather than a reduced subspace; no minibatch noise, JVP/VJP implementation, FLOP-normalized benchmark, or larger neural network was tested. Runtime measurements are noisy and the baseline/idea use different numbers of objective evaluations." }