新的MCMC算法提供灵活的图划分采样

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员推出了一种新的马尔可夫链蒙特卡洛（MCMC）算法——标记边行走（MEW），用于采样图划分。与之前偏好与生成树相关的分布的RevReCom和MFR等方法不同，MEW在带有标记边的生成树上运行。这使得Metropolis-Hastings算法内的转移概率可计算，从而能够生成更灵活的集合。在真实世界双图上的实证测试表明，MEW能够在更广泛的目标分布下收敛，包括用于竞争性、紧凑性和党派对称性的基于策略的分布，同时减少对生成树计数的偏见。 AI

影响这种新算法可以改进用于各种计算任务的复杂数据结构的生成。

排序理由该集群包含一篇详细介绍新算法的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Atticus McWhorter, Daryl DeFord · 2026-09-15 04:00

The Marked Edge Walk：一种用于图划分采样的创新MCMC算法

arXiv:2510.17714v3 Announce Type: replace-cross Abstract: Novel Markov Chain Monte Carlo (MCMC) methods have enabled the generation of large ensembles of redistricting plans modeled as a graph partitioning problem. However, existing algorithms such as Reversible Recombination (Re…

本文如何被排名

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The Marked Edge Walk：一种用于图划分采样的创新MCMC算法

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