Gibbs sampling from log-smooth strongly log-concave targets mixes in O*(kappa^2 n^7.5 (max{1, sqrt(n^{-1} log(2M/gamma))})^2) steps from an M-warm start.
On the $\ell_0$ Isoperimetric Coefficient of Measurable Sets
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
In this paper we prove that the $\ell_0$ isoperimetric coefficient for any axis-aligned cubes, $\psi_{\mathcal{C}}$, is $\Theta(n^{-1/2})$ and that the isoperimetric coefficient for any measurable body $K$, $\psi_K$, is of order $O(n^{-1/2})$. As a corollary we deduce that axis-aligned cubes essentially "maximize" the $\ell_0$ isoperimetric coefficient: There exists a positive constant $q > 0$ such that $\psi_K \leq q \cdot \psi_{\mathcal{C}}$, whenever $\mathcal{C}$ is an axis-aligned cube and $K$ is any measurable set. Lastly, we give immediate applications of our results to the mixing time of Coordinate-Hit-and-Run for sampling points uniformly from convex bodies.
citation-role summary
citation-polarity summary
fields
math.ST 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
A mixing time bound for Gibbs sampling from log-smooth log-concave distributions
Gibbs sampling from log-smooth strongly log-concave targets mixes in O*(kappa^2 n^7.5 (max{1, sqrt(n^{-1} log(2M/gamma))})^2) steps from an M-warm start.