(15); (d) uses the definition in eq

+ 2ηG2 0 (c) ≤ O h RG0√ K + 1 + [RG1]2F K + 1 i (d) ≤ O[ϵ], where (a) uses the convexity of the function f(x), the definition of xK, (b) uses the inequality above, the fact that ∥x∗∥2 ≤ R, x0 = 0, (c) uses the definitions in eq · 2010

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Stochastic Non-Smooth Convex Optimization with Unbounded Gradients

math.OC · 2026-05-15 · unverdicted · novelty 7.0

Clipped AdamW with exponentially weighted accumulation achieves superior global convergence rates for convex stochastic generalized Lipschitz optimization compared to SGD and AdaGrad.

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Stochastic Non-Smooth Convex Optimization with Unbounded Gradients math.OC · 2026-05-15 · unverdicted · none · ref 21
Clipped AdamW with exponentially weighted accumulation achieves superior global convergence rates for convex stochastic generalized Lipschitz optimization compared to SGD and AdaGrad.

(15); (d) uses the definition in eq

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