English(EN) Mirror Descent-Ascent for mean-field min-max problems

新算法为均值场极小极大问题提供改进的收敛性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 04:00

研究人员开发了镜像下降-上升（MDA）算法的两个变体，以解决度量空间内的极小极大问题。该研究为同步和交替MDA建立了非渐近收敛率，其中交替版本表现出改进的性能。一项关键的技术贡献涉及无限维对偶空间分析，该分析将度量上的Bregman散度与有界连续函数上的Bregman散度联系起来，从而能够更好地控制交替更新项。 AI

排序理由这是一篇研究论文，详细介绍了一种新的优化问题算法方法。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Razvan-Andrei Lascu, Mateusz B. Majka, {\L}ukasz Szpruch · 2026-05-08 04:00

均场极小极大问题的镜像下降-上升法

arXiv:2402.08106v3 Announce Type: replace-cross Abstract: We study two variants of the mirror descent-ascent (MDA) algorithm for solving min-max problems on the space of measures: simultaneous and alternating. We work under assumptions of convexity-concavity and relative smoothne…

本文如何被排名

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均场极小极大问题的镜像下降-上升法

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