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Adaptive Normalized Gradient Method for $(L_0,L_1)$-smooth Problems: Estimating Problem Parameters as a Region

R. Okazaki, Shotaro Yagishita, Hideitsu Hino · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27681 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

我们提出了一个无参数和高效的降级算法,用于 $(L_0,L_1)$的平滑优化。这种平滑的概念,由Zhang et al.(2020)引入,允许平滑与降级标准一起增长。类似于尖端的定位,我们的方法使用观察设置的序列来维持 $(L_0,L_1)$的可信值的区域,并使用该区域来选择单个步骤大小。在圆形的设置中,我们的方法达到与降级标准相连的迭代复杂性 $\de{\mathcal{O}}\left(left(L_0/ε+ L_1^2\right)\ t\ t\ t\ t\ t\ t\ t\ t\ t\ t\ t\ t\ t\ t\ t\ t\ t

Machine translation (Meta M2M100); the English original follows.

Abstract

We propose a parameter-free and efficient gradient descent algorithm for $(L_0,L_1)$-smooth optimization. This notion of smoothness, introduced by Zhang et al. (2020), allows the smoothness bound to grow with the gradient norm. Similar to cutting-plane localization, our method uses a sequence of observations to maintain a region of plausible values for $(L_0,L_1)$ and uses this region to select step sizes. In the convex setting, our method attains the standard iteration complexity bound $\tilde{\mathcal{O}}\left(\left(L_0/ε+ L_1^2\right)\|x^0-x^*\|^2\right)$ for finding an $ε$-optimal solution, while in the nonconvex setting it finds an $ε$-stationary point within $\tilde{\mathcal{O}}\left(\left(L_0/ε^2 + L_1/ε\right)(f(x^0)-f^*)\right)$ iterations. To the best of our knowledge, this is the first work to establish, for a single parameter-free algorithm, the best-known iteration-complexity guarantee in the nonconvex setting while also providing the above iteration-complexity guarantee in the convex setting. Experiments on logistic regression and real phase retrieval demonstrate that our method can exploit $(L_0,L_1)$-smoothness and perform efficiently on nonconvex problems.

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Topics: Smoothness, Gradient descent, Mathematics, Regular polygon, Mathematical optimization, Gradient method

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