SOURCE-LINKED INTELLIGENCE

Thin-shell stability of Gaussian cooling: logconcave sampling with sesteric complexity from a cold start

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

We show that logconcave probability measures along the Gaussian cooling path have thin-shell stability, generalizing the thin-shell theorem. This result leads to improved complexity for the fundamental problem of sampling an arbitrary logconcave distribution from a cold start. For (near-)isotropic logconcave distributions, the complexity is nearly $n^{2.5}$, improving the previous bound of $n^{2.75}$, and matching the complexity of the abstract Speedy walk.

Read original source ↗ Open in workspace

recordType

paper

region

Global