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English(EN) $\gamma$-weakly $\theta$-up-concavity: A Unified Framework for Non-Convex Optimization Beyond DR-Submodular and OSS Functions

新框架统一了超越现有模型的非凸优化

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了一个名为 $\gamma$-弱 $\theta$-上凹性 的新框架，旨在统一非凸优化问题。该框架扩展了 DR-次模函数和 OSS 函数等现有概念，允许曲率随解的大小而变化。所提出的方法利用了代理梯度，并建立了一个上线性化不等式，从而为离线场景带来了近似算法，并为在线算法提供了遗憾保证。 AI

影响 引入了一个新的理论框架，可能带来改进的机器学习优化算法。

排序理由 该集群包含一篇详细介绍优化新理论框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新框架统一了超越现有模型的非凸优化

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该集群包含一篇详细介绍优化新理论框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mohammad Pedramfar, Vaneet Aggarwal ·

    $\gamma$-弱 $\theta$-上凹性：超越 DR-次模函数和 OSS 函数的非凸优化统一框架

    arXiv:2602.13506v3 Announce Type: replace-cross Abstract: Many objectives in machine learning and combinatorial optimization are non-concave. We introduce $\gamma$-weak $\theta$-up-concavity, a first-order condition for nonnegative monotone functions in which a scaling function $…

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