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English(EN) Riemannian Optimization on Unknown Manifolds via Stochastic Proximal Refinement

新的PRISM框架可在未知流形上实现黎曼优化

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为PRISM（Proximal Refinement on Implicit Smooth Manifolds，隐式光滑流形上的近邻精炼）的新优化框架，该框架仅使用可行样本即可在未知流形上工作。该方法解决了在切空间和投影不可直接获得的情况下进行黎曼优化的挑战。PRISM通过从目标感知近邻分布中采样来迭代地精炼解决方案，理论上可确保目标下降、可行性和收敛到一阶平稳点。该框架已在合成实验和数据驱动的建筑控制等实际应用中证明了其有效性。 AI

影响 该框架通过改进模型如何从有限的可行样本中学习，有望在科学和工程领域实现更有效的数据驱动优化。

排序理由 该条目是一篇学术论文，详细介绍了一个新的优化框架。[lever_c_demoted from research: ic=1 ai=0.7]

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 →

新的PRISM框架可在未知流形上实现黎曼优化

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该条目是一篇学术论文，详细介绍了一个新的优化框架。[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) · Ruixiang Wu, Jiahao Ai, Tinko Sebastian Bartels, Xuanzhuo Liu, Yifei Wu, Yu Mao, Tongxin Li, Costas Courcoubetis ·

    Riemannian Optimization on Unknown Manifolds via Stochastic Proximal Refinement

    arXiv:2610.41379v1 Announce Type: cross Abstract: We study Riemannian optimization on an unknown manifold accessed only through feasible samples. The central challenge is to turn feasible samples into optimization steps, i.e., the samples are drawn from the unknown feasible manif…

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