A smoothing-aware AdaGrad Riemannian gradient method achieves O(ε^{p-4}) global complexity for non-Lipschitz manifold optimization with p-norm penalties (p in (0,1]), recovering the known O(ε^{-3}) rate when p=1.
Boumal.An Introduction to Optimization on Smooth Manifolds
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An Adaptive Smoothing Algorithm for Non-Lipschitz Optimization on Manifolds with Complexity Guarantees
A smoothing-aware AdaGrad Riemannian gradient method achieves O(ε^{p-4}) global complexity for non-Lipschitz manifold optimization with p-norm penalties (p in (0,1]), recovering the known O(ε^{-3}) rate when p=1.