Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems

no code implementations • 19 Mar 2022 • Kanji Sato, Akiko Takeda, Reiichiro Kawai, Taiji Suzuki

Gradient Langevin dynamics and a variety of its variants have attracted increasing attention owing to their convergence towards the global optimal solution, initially in the unconstrained convex framework while recently even in convex constrained non-convex problems.

Dimension-free convergence rates for gradient Langevin dynamics in RKHS

no code implementations • 29 Feb 2020 • Boris Muzellec, Kanji Sato, Mathurin Massias, Taiji Suzuki

In this work, we provide a convergence analysis of GLD and SGLD when the optimization space is an infinite dimensional Hilbert space.