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English(EN) Optimizing the Preconditioner: A Black-box Online-to-Nonconvex Conversion with Static Regret Minimization Oracles

新的机器学习优化技术解决了非凸问题和偏微分方程求解器

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了优化机器学习算法的新方法，特别是在非凸优化和科学计算的背景下。一篇论文介绍了一种黑盒在线到非凸转换技术，该技术利用静态遗憾最小化预言机，解决了开放性问题，并为 AdaGrad 和 Shampoo 等自适应优化方法提供了新视角。另一项研究提出了 PCGBandit，一种用于瞬态偏微分方程求解器的一步加速方法，该方法使用 bandit 算法直接从模拟数据中自适应地学习求解器配置，并在使用 OpenFOAM 的流体和磁流体动力学问题上得到了有效证明。 AI

影响 这些进展可能导致更有效的机器学习模型训练和更快的科学模拟。

排序理由 两篇发表在 arXiv 上的学术论文，详细介绍了新颖的机器学习优化技术。

在 arXiv stat.ML 阅读 →

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新的机器学习优化技术解决了非凸问题和偏微分方程求解器

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两篇发表在 arXiv 上的学术论文，详细介绍了新颖的机器学习优化技术。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Alexander Benanti, Xi Han, Hong Qin ·

    NeuraLSP：一种用于加速 PDE 求解器的神经谱预条件器

    arXiv:2601.20174v3 Announce Type: replace-cross Abstract: Solving large-scale sparse linear systems originating from partial differential equations (PDEs) is a fundamental topic in high-performance scientific computing, where preconditioners are crucial. Multigrid methods are amo…

  2. arXiv stat.ML TIER_1 English(EN) · Haichen Hu, David Simchi-Levi ·

    优化预条件器：具有静态遗憾最小化预言机的黑盒在线到非凸转换

    arXiv:2607.17607v1 Announce Type: cross Abstract: We study whether stochastic nonconvex optimization can be reduced to ordinary static regret minimization in online convex optimization in a black-box manner. For smooth nonconvex objectives, our reduction maintains a predictable g…

  3. arXiv stat.ML TIER_1 English(EN) · Mikhail Khodak, Min Ki Jung, Brian Wynne, Edmond Chow, Egemen Kolemen ·

    通过在线学习预条件子实现瞬态 PDE 求解器的一步加速

    arXiv:2509.08765v4 Announce Type: replace-cross Abstract: Data-driven acceleration of scientific computing workflows has been a high-profile aim of machine learning (ML) for science, with numerical simulation of transient partial differential equations (PDEs) being one of the mai…

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