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arXiv · 2610.23775

Polytope Sampling at the Speed of Isoperimetry

Anming Gu·Bobby Shi·Kevin Tian·Hubert Yang
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Abstract

We give faster algorithms for sampling from the uniform distribution over a polytope. Our main result is an algorithm that, when applied to a polytope with $m$ constraints in $n$ dimensions, uses $m \cdot \mathrm{poly}\log(\frac m δ)$ iterations, each running in time $O(mn^{ω- 1})$, to return a sample within total variation distance $δ$ to the uniform distribution. When $m =O(n)$ and $δ= \mathrm{poly}(\frac 1 n)$, our runtime improves upon all prior works by an $Ω(n^{2/3})$ factor. Our sampler is based on the recent \emph{functional stochastic localization} framework of [GST26], and achieves an iteration count nearly matching the Poincaré constant of the uniform distribution with respect to the logarithmic barrier.

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Anming Gu, Bobby Shi, Kevin Tian, Hubert Yang. 2026-10-16. Polytope Sampling at the Speed of Isoperimetry. https://arxiv.org/abs/2610.23775

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