English(EN) Smooth Quasar-Convex Optimization with Constraints

新算法解决带约束的拟凸优化问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-16 04:00

研究人员开发了一种新的非精确加速近点算法，用于处理具有一般凸约束的光滑拟凸函数。该算法实现了最优的一阶查询复杂度 $\widetilde{O}(1/(\gamma\sqrt{\varepsilon}))$，解决了该领域的一个开放性问题。该工作还分析了在此约束设置下的投影梯度下降和 Frank-Wolfe 算法，首次对具有一般凸约束的光滑拟凸函数的一阶方法进行了分析。 AI

影响这项研究推进了适用于广义线性模型等机器学习模型的优化技术。

排序理由该集群包含一篇研究论文，详细介绍了一种针对特定类别数学优化问题的新算法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · David Mart\'inez-Rubio · 2026-07-16 04:00

带约束的平滑拟凸优化

arXiv:2510.01943v3 Announce Type: replace-cross Abstract: Quasar-convex functions form a broad nonconvex class with applications to linear dynamical systems, generalized linear models, and Riemannian optimization, among others. Current nearly optimal algorithms work only in affin…

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带约束的平滑拟凸优化

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