English(EN) Lower Bounds for Randomized Linear-Oracle Optimization over Strongly Convex Sets

新研究确立了随机优化方法的下界

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

一篇新发表在arXiv上的论文介绍了一种分布式的下界，适用于应用于强凸集的随机一阶线性最小化预言器方法。这项研究填补了现有文献中的一个空白，现有文献主要关注确定性方法或像标准Frank--Wolfe算法这样的特定算法。研究结果为这些随机优化技术的性能设定了一个理论上限，表明随机化可能无法规避已建立的误差界限。 AI

排序理由该集群包含一篇详细介绍优化算法理论界限的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Sebastian Pokutta · 2026-10-12 04:00

强凸集上随机线性预言机优化的下界

arXiv:2610.13590v1 Announce Type: cross Abstract: Recent results [arXiv:2602.04378, arXiv:2602.22608] have established worst-case objective-error lower bounds of order $1/T^2$ after $T$ linear minimization oracle (LMO) calls, matching the upper bound [arXiv:1406.1305] in its depe…

本文如何被排名

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强凸集上随机线性预言机优化的下界

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