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English(EN) Learning-Based Surrogate Method for Stochastic Optimization under Decision-Dependent Uncertainty with Adaptive Random Designs

新的代理方法通过自适应设计增强了随机优化

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的基于学习的代理方法，用于解决具有决策依赖不确定性的复杂随机优化问题。该方法整合了仿真和统计学习，使用自适应随机设计来改进雅可比估计并提高收敛速度。提出的基于学习的随机代理线性（L-SPL）算法与现有方法相比，表现出更高的样本效率并实现了更低的目标值，这表明定制的统计设计可以显著提升优化性能。 AI

影响 为优化问题引入了一种新颖的算法方法，有可能提高AI模型训练和其他复杂系统的效率。

排序理由 该集群包含一篇详细介绍随机优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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

新的代理方法通过自适应设计增强了随机优化

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该集群包含一篇详细介绍随机优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Boyang Shen, Junyi Liu ·

    基于学习的随机优化代理方法，用于具有自适应随机设计的依赖决策不确定性

    arXiv:2505.07298v2 Announce Type: replace-cross Abstract: We study stochastic programs in which the latent decision-dependent uncertainty is described via a nonparametric regression model. The major challenge is that, without convexity assumptions on either the cost function or t…

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