Faster logconcave sampling from a cold start in high dimension
This addresses a fundamental bottleneck in computational geometry and machine learning for efficient sampling from complex distributions, representing a significant rather than incremental advance.
The paper tackles the problem of generating a warm start for sampling logconcave densities in high dimensions, achieving the first sub-cubic sampling algorithms for inputs in near-isotropic position by reducing the warm-start penalty from linear to sub-linear in dimension.
We present a faster algorithm to generate a warm start for sampling an arbitrary logconcave density specified by an evaluation oracle, leading to the first sub-cubic sampling algorithms for inputs in (near-)isotropic position. A long line of prior work incurred a warm-start penalty of at least linear in the dimension, hitting a cubic barrier, even for the special case of uniform sampling from convex bodies. Our improvement relies on two key ingredients of independent interest. (1) We show how to sample given a warm start in weaker notions of distance, in particular $q$-Rényi divergence for $q=\widetilde{\mathcal{O}}(1)$, whereas previous analyses required stringent $\infty$-Rényi divergence (with the exception of Hit-and-Run, whose known mixing time is higher). This marks the first improvement in the required warmness since Lovász and Simonovits (1991). (2) We refine and generalize the log-Sobolev inequality of Lee and Vempala (2018), originally established for isotropic logconcave distributions in terms of the diameter of the support, to logconcave distributions in terms of a geometric average of the support diameter and the largest eigenvalue of the covariance matrix.