Convergence holds for momentum μ less than 1 in SPRING under mild assumptions, but μ=1 risks divergence; PRIME-SR adapts momentum via spectral dimension and subspace overlap to match tuned performance with better robustness.
A simple linear algebra identity to optimize large-scale neural network quantum states
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Momentum Stability and Adaptive Control in Stochastic Reconfiguration
Convergence holds for momentum μ less than 1 in SPRING under mild assumptions, but μ=1 risks divergence; PRIME-SR adapts momentum via spectral dimension and subspace overlap to match tuned performance with better robustness.