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English(EN) Lower Bound on the Cumulative Constrained Violation for the OGD+Projection algorithm for Constrained Online Convex Optimization (COCO)

为 COCO 算法建立新的理论下界

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为受限在线凸优化中的 OGD+Projection 算法建立了一个新的理论下界。这项工作表明，OGD+Projection 算法的累积约束违反 (CCV) 为 $\Omega (T^{\frac{d-1}{2d}})$，这是第一个此类下界结果。这一发现意义重大，因为它为算法在涉及凸损失和约束函数的场景中的性能提供了理论限制。 AI

影响 为机器学习中使用的优化算法设定了理论限制。

排序理由 该条目是一篇研究论文，详细介绍了算法的理论下界。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

为 COCO 算法建立新的理论下界

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该条目是一篇研究论文，详细介绍了算法的理论下界。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Haricharan Balasundaram, Karthick Krishna Mahendran, Rahul Vaze ·

    受限在线凸优化（COCO）的 OGD+Projection 算法的累积受限违反的下界

    arXiv:2607.10808v1 Announce Type: new Abstract: The problem of constrained online convex optimization is considered, where at each round, once a learner commits to an action $x_t \in \mathcal{X} \subset \mathbb{R}^d$, a convex loss function $f_t$ and a convex constraint function …

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