AdaGrad-Norm last iterate achieves O(1/N^{1/4}) suboptimality for convex non-smooth problems, with tight lower bounds.
ArXiv Preprint: 2508.07473 , Year =
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Establishes matching lower and upper oracle complexity bounds for scale-invariant methods with spectral norm under heavy-tailed noise, plus improved rates with higher-order smoothness, and practical tests on neural networks.
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Last Iterate Convergence of AdaGrad-Norm for Convex Non-Smooth Optimization
AdaGrad-Norm last iterate achieves O(1/N^{1/4}) suboptimality for convex non-smooth problems, with tight lower bounds.
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Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise
Establishes matching lower and upper oracle complexity bounds for scale-invariant methods with spectral norm under heavy-tailed noise, plus improved rates with higher-order smoothness, and practical tests on neural networks.