stat.CO · 2026-08-04 · No. 74
Computation, 2026-08-04.
1 new papers in stat.CO. Titles, authors,
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01 — The papers
1 entries-
01
Wasserstein mixing time of the unadjusted Langevin algorithm
Francesco Pedrotti, Peter A. Whalley
stat.CO · cs.LG · math.NA · math.PR
We provide new estimates in Wasserstein distance for the asymptotic bias of the unadjusted Langevin algorithm, in the classical setting of log-smooth strongly log-concave measures. Our bound implies a Wasserstein mixing time of order $κ\sqrt{d}/\varepsilon$, where $κ$ is the condition number, $d$ is the dimension, and $\varepsilon$ is the target precision: this improves by a factor of $\sqrt{d}/\varepsilon$ over the previous state-of-the-art results.
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