English(EN) Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model

新分析解释Bradley-Terry模型迭代的加速收敛

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-27 04:00

研究人员分析了一族Zermelo型迭代方法在Bradley-Terry模型上的应用，旨在提高收敛速度。该研究为解释为何特定参数选择alpha=0通常比经典的Zermelo算法（alpha=1）收敛更快提供了理论见解。分析表明，异步更新与alpha=0结合是加速的关键，并在特定条件下证明了其在二分比较图上的最优性。 AI

影响为优化统计建模中使用的迭代算法提供了理论基础，可能影响机器学习应用。

排序理由学术论文，详细阐述了算法的理论收敛性分析。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv stat.ML TIER_1 English(EN) · Ruijian Han, Ding Lu, Yiming Xu · 2026-07-27 04:00

一族 Zermelo 型迭代用于 Bradley--Terry 模型的收敛性分析

arXiv:2607.22221v1 Announce Type: new Abstract: Zermelo's algorithm is a classical method for computing the maximum likelihood estimator in the Bradley--Terry (BT) model, but its convergence can be slow in practice. To accelerate computation, Newman introduced a family of Zermelo…

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一族 Zermelo 型迭代用于 Bradley--Terry 模型的收敛性分析

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