English(EN) Nested Convex-Body Chasing for Online Optimization with Evolving Feasible Sets

新算法解决具有演化可行集的在线优化问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了用于涉及嵌套收缩可行区域的在线优化问题的新算法。这些算法专为嵌套演化可行集（CONES）的凸优化和对抗性约束在线凸优化（COCO）等设置而设计，将损失控制与几何移动分开。所提出的方法通过用多项式维度依赖性替换复杂的投影路径因子，在更高维度下实现了改进的遗憾保证和减小的移动界限。 AI

影响为与AI研究相关的复杂优化任务引入了新颖的算法方法。

排序理由该条目是一篇学术论文，详细介绍了优化问题的新算法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Dhruv Sarkar, Aprameyo Chakrabartty · 2026-09-01 04:00

具有演化可行集的在线优化嵌套凸体追踪

arXiv:2608.29074v1 Announce Type: new Abstract: We study online optimization with nested shrinking feasible regions in two settings: convex optimization with nested evolving feasible sets (CONES) and adversarial constrained online convex optimization (COCO). Our algorithms separa…