English(EN) Efficient Online Inverse Optimization with $O(d)$ Regret

新算法在在线逆向优化中实现 $O(d)$ 遗憾界

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新的确定性算法，用于在线逆向线性优化，实现了 $O(d)$ 的遗憾界，相比先前的方法有了显著改进。该算法每轮运行时间为 $O(d^2)$，效率很高，使其具有实用性。这项工作建立在可变度量框架的基础上，并引入了一种新颖的自归一化秩一更新，用迹幂函数取代了对数行列式势函数，以获得更好的界限。 AI

影响优化算法方面的这项理论进展可能为未来更高效的 AI 模型训练和推理带来可能。

排序理由该集群包含一篇详细介绍具有理论性能改进的新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yang Cai, Anupam Gupta, Vineet Gupta, Guru Guruganesh, Yanchen Jiang, Christopher Liaw, Aranyak Mehta, Renato Paes Leme, Grigoris Velegkas, Di Wang · 2026-09-15 04:00

高效在线逆向优化，遗憾界限为 $O(d)$

arXiv:2609.13440v1 Announce Type: new Abstract: We give a deterministic algorithm for online inverse linear optimization with regret $O(d)$, uniform in the horizon and $O(d^{2})$ time per round. A bound of this order was obtained recently by Dewasurendra, settling a question of G…

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报道来源 [1]

高效在线逆向优化，遗憾界限为 $O(d)$

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