Smoothness parameter of power of Euclidean norm
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Abstract (arXiv)
In this paper, we study derivatives of powers of Euclidean norm. We prove their H"older continuity and establish explicit expressions for the corresponding constants. We show that these constants are optimal for odd derivatives and at most two times suboptimal for the even ones. In the particular case of integer powers, when the H"older continuity transforms into the Lipschitz continuity, we improve this result and obtain the optimal constants.
Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). First posted on arXiv 29 Jul 2019.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 1.
6 papers.
- 2025Can SGD Handle Heavy-Tailed Noise?cited by 17
- 2025Complexity of normalized stochastic first-order methods with momentum under heavy-tailed noisecited by 11
- 2025Accelerated stochastic first-order method for convex optimization under heavy-tailed noisecited by 4
- 2025DADA: Dual Averaging with Distance Adaptationcited by 3
- 2024The inexact power augmented Lagrangian method for constrained nonconvex optimizationcited by 1
- 2026Vanilla SGD with Momentum Survives Heavy-Tailed Noise: Convergence Analysis without Gradient Clipping or Normalizationcited by 1
- 2026Vanilla SGD with Momentum Survives Heavy-Tailed Noise: Convergence Analysis without Gradient Clipping or Normalization9 Jul 2026
- 2025Accelerated stochastic first-order method for convex optimization under heavy-tailed noise13 Oct 2025
- 2025Can SGD Handle Heavy-Tailed Noise?6 Aug 2025
- 2025Complexity of normalized stochastic first-order methods with momentum under heavy-tailed noise12 Jun 2025
- 2025DADA: Dual Averaging with Distance Adaptation17 Jan 2025
- 2024The inexact power augmented Lagrangian method for constrained nonconvex optimization26 Oct 2024