New complexity bound simplifies logconcave distribution sampling

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-10 21:28

研究人员为对数凹分布采样开发了一种新的、统一的复杂性界限。该界限接近最优，并适用于各种情况，包括约束和适定密度。该分析为提升分布的庞加莱常数提供了改进的界限，从而提高了收敛速度。 AI

排序理由该集群包含一篇学术论文，详细介绍了对数凹分布采样的新理论界限。

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arXiv stat.ML TIER_1 English(EN) · Yunbum Kook, Santosh S. Vempala · 2026-06-12 04:00

logconcave采样的一个统一复杂度界限

arXiv:2606.12694v1 Announce Type: cross Abstract: We give a simple, unified, and nearly tight bound for sampling arbitrary logconcave distributions from a warm start using the In-and-Out algorithm along with exponential lifting. The main new ingredient in the analysis is an impro…
arXiv stat.ML TIER_1 English(EN) · Santosh S. Vempala · 2026-06-10 21:28

logconcave采样的一个统一复杂度界限

We give a simple, unified, and nearly tight bound for sampling arbitrary logconcave distributions from a warm start using the In-and-Out algorithm along with exponential lifting. The main new ingredient in the analysis is an improved bound on the Poincaré constant of a lifted dis…