English(EN) Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functions

新算法解决演化中的凸优化问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一种新的在线算法，用于处理具有演化可行集和时变损失函数的凸优化问题。该算法是先前引入的 CONES 框架的扩展，旨在最小化针对静态最优基准的遗憾和总移动成本。当损失函数为凸函数时，投影近端算法在时间 T 内实现了 O(T^{1-eta}) 的遗憾和 O(T^eta) 的移动成本，其中 {eta} 在 [0,1) 范围内。对于强凸损失函数，该算法实现了 O(1) 的遗憾和 O(log T) 的移动成本。 AI

影响为与机器学习相关的复杂优化任务引入了一种新颖的算法方法。

排序理由学术论文，详细介绍了一种针对特定优化问题的新算法。[lever_c_demoted from research: ic=1 ai=1.0]

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新算法解决演化中的凸优化问题

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Newsworthiness bucket: Tool; 学术论文，详细介绍了一种针对特定优化问题的新算法。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.LG TIER_1 English(EN) · Rahul Vaze · 2026-09-11 04:00

具有时变损失函数的嵌套演化可行集（CONES）凸优化

arXiv:2609.11207v1 Announce Type: new Abstract: Convex Optimization with Nested Evolving Feasible Sets (CONES)} was introduced in \cite{CONESVaze} where the objective function \(f\) remains fixed but the feasible region evolves over time as a nested sequence \(S_1 \supseteq S_2 \…