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Non-convex sampling from matrix spaces is now possible with provable exponential convergence using momentum preconditioning that spectrally tames gradients.
SG-TULA achieves competitive performance in sampling from complex non-convex distributions while offering explicit convergence guarantees that traditional methods fail to provide.
