English(EN) Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraints

新算法在非光滑非凸随机优化中实现了最优速率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员为具有非光滑和非凸目标以及凸约束的随机优化问题开发了新算法。这些算法实现了最优收敛速率，其中基于投影和无投影的方法使用随机梯度达到了 $\widetilde O(\delta^{-1}\varepsilon^{-3})$ 的速率，使用零阶查询达到了 $\widetilde O(d\delta^{-1}\varepsilon^{-3})$ 的速率。研究结果表明，在非凸随机优化中，约束和非光滑性不会降低收敛速率，该分析依赖于向在线线性优化的约简。 AI

排序理由详细介绍新算法和优化理论结果的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

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arXiv cs.LG TIER_1 English(EN) · Gon Buzaglo, Guy Kornowski · 2026-10-12 04:00

带约束的非光滑非凸随机优化的最优算法

arXiv:2610.13787v1 Announce Type: cross Abstract: We present new algorithms for stochastic optimization problems in which the objective is neither smooth nor convex, subject to a convex constraint set. Our algorithms return a $(\delta,\varepsilon)$-Goldstein stationary point, as …

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带约束的非光滑非凸随机优化的最优算法

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