Эксперимент: Rank-One Proximal Quasi-Newton Optimizer (#15)

{ "worked": false, "confidence": 9, "verdict": "Built a rank-one proximal-gradient optimizer with Sherman–Morrison preconditioning, exact diagonal-metric group-lasso proximal evaluation, safeguarded bisection for the rank-one residual, and a synthetic block-sparse regression benchmark. The math check was successful: root residual 1.87e-16, direct proximal-solution max error 1.35e-7, and Sherman–Morrison identity error 3.33e-15. In the benchmark the idea reached objective 7.19 versus baseline 11.16, but required 8.26 s versus 0.77 s and selected the same 10 active groups; therefore the lower objective was not an efficiency or sparsity win.", "metrics": { "baseline": "final objective 11.1612; objective at step 30 10.8749; 0.766 s; 10 active groups; no rank-one root solves", "idea": "final objective 7.1905; objective at step 30 4.1682; 8.255 s; 10 active groups; mean root residual 3.74e-4, max 8.60e-4" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "run_experiment.py", "results.json" ], "limitations": "Only a small synthetic convex group-sparse regression problem was tested, not an MLP, MNIST, AdamW, L-BFGS, equal-FLOP neural-network training, or multiple random seeds. The benchmark's rank-one method used repeated exact scalar solves, so its computational overhead was not optimized." }