Эксперимент: Spectrally safeguarded DFP (#36)

{ "worked": false, "confidence": 9, "verdict": "Built a dense small-network spectrally safeguarded DFP optimizer with eigenvalue flooring, condition control, and smallest-eigenspace rotation monitoring. The numerical checks passed: DFP satisfied H_new y=s with error 5.9e-15, and the safeguard repaired a 1e-8 eigenvalue to the 1e-3 floor. However, at equal 100 gradient evaluations on the fixed synthetic classification dataset, ordinary DFP reached loss 1.77e-5 while safeguarded DFP reached 1.52e-2 and triggered 30 safeguards, so the proposed safeguard did not produce a win in this MVP.", "metrics": { "baseline": "Ordinary DFP: final loss 1.77e-5, gradient norm 5.19e-4, minimum observed H eigenvalue 1.96e-2, 100 gradient evaluations. SGD: final loss 0.6931, gradient norm 0.1505, 100 evaluations.", "idea": "Safeguarded DFP: final loss 1.52e-2, gradient norm 1.39e-2, minimum observed H eigenvalue 3.11e-2, 30 safeguards, 100 gradient evaluations. Math check: secant error 5.86e-15; safeguarded minimum eigenvalue 1e-3." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only one fixed synthetic classification dataset, one seed, one small dense model, and one short CPU run were tested. The implementation uses full dense eigendecompositions rather than blockwise or limited-memory spectral estimates, and its line search is Armijo-only rather than a complete strong-Wolfe search; no MNIST, 2D paper counterexample, FLOP accounting, or multi-seed statistics were evaluated." }