Stochastic Optimization Under Power-Law Spectra: Tight Bounds and Shuffling Analysis

Recent work has established that power-law spectral conditions on data enable tight convergence bounds for deterministic gradient descent, resolving the conflict between classical exponential bounds and observed power-law learning curves.