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新算法提高了变分不等式的收敛速率

研究人员开发了一种新的随机算法,该算法结合了Halpern锚定技术,以解决约束凸凹问题和单调变分不等式。该算法采用单循环、单调用方式,在每次迭代中使用梯度算子的无偏样本,适用于具有噪声反馈的单调博弈。该算法在梯度映射范数和受限差距方面均实现了O(t^{-1/4})的任意最后迭代收敛速率,优于先前受限差距的O(t^{-1/5})最佳速率。 AI

影响 这项研究推进了与机器学习和博弈论相关的优化技术。

排序理由 该集群包含一篇详细介绍新算法及其理论保证的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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新算法提高了变分不等式的收敛速率

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该集群包含一篇详细介绍新算法及其理论保证的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jun-Hyun Kim, Ahmet Alacaoglu ·

    改进任意时间算法在随机单调变分不等式上的最终迭代保证

    arXiv:2609.15257v1 Announce Type: cross Abstract: We analyze a stochastic algorithm with Halpern anchoring for constrained convex-concave problems and monotone variational inequalities. This algorithm is single-loop and single-call since it uses one unbiased sample of the gradien…