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English(EN) Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization

新优化方法在高维问题上展现潜力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的Bregman线性增广拉格朗日方法，用于解决非凸约束随机零阶优化问题。该方法利用随机零阶梯度估计器和方差缩减技术来分析预言机复杂度。所提出的方法在高维设置下表现出改进的性能，实现了低于O(d)的维度依赖性，并与文献中关于容差\(\\epsilon\\)的最低复杂度阶数相匹配。在约束Lasso和对抗性攻击问题上的数值实验表明了有希望的结果。 AI

影响 这种新的优化方法可能导致在高维和受约束环境中更有效地训练AI模型。

排序理由 该集群包含一篇详细介绍新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新优化方法在高维问题上展现潜力

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该集群包含一篇详细介绍新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qiankun Shi, Han Yuan, Xiao Wang, Hao Wang ·

    Bregman线性增广拉格朗日法用于非凸约束随机零阶优化

    arXiv:2504.09409v2 Announce Type: replace-cross Abstract: In this paper, we study nonconvex constrained stochastic zeroth-order optimization problems, for which we have access to exact information of constraints and noisy function values of the objective. We propose a Bregman lin…

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