Эксперимент: Warm-Started Exact Rank Pruning (#531)

{ "worked": true, "confidence": 9, "verdict": "Built a NumPy MVP implementing paired exact hard thresholding, reciprocal scale balancing, warm-started lambda continuation, and a fixed-rank reconstruction baseline. The mechanism manifested: the proximal rule had 0 mismatches over 408 swept cases, balancing preserved products to 1.27e-15 maximum error and reduced norm gaps to 8.88e-16, and the lambda path reduced rank monotonically from 8 to 3 while retaining essentially unchanged reconstruction loss. This is a positive toy-setting result, not evidence of an end-to-end task win.", "metrics": { "baseline": "Fixed-rank rank-3 loss 1.500000e-6; rank-8 loss 1.500000e-6; 0.434-0.473 s in the initial run and 0.485-0.487 s in the reproducibility run.", "idea": "Warm-start path ranks [8,3,3,3,3,3,3,3] for lambdas [0,0.02,0.08,0.2,0.5,1,2,4], loss 1.500000e-6 at every stage, 0.503 s; threshold mismatches 0/408; maximum balancing product error 1.27e-15 and norm gap 8.88e-16." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py > results.json", "files": [ "experiment.py", "results.json" ], "limitations": "Only a small synthetic unconstrained matrix reconstruction problem was tested; no NMF nonnegativity, LoRA/MLP fine-tuning, noisy data, CUDA execution, inference latency, memory measurement, or broad statistical seed sweep was evaluated. The observed rank transition is partly enabled by SVD initialization with deliberately weak surplus columns, so generalization to arbitrary neural-network initialization remains unverified." }