A Proximal Algorithm for Sampling from Non-convex Potentials

Understand

We study sampling problems associated with non-convex potentials that meanwhile lack smoothness.

• In particular, we consider target distributions that satisfy either logarithmic-Sobolev inequality or Poincar\'e inequality.
• Rather than smooth, the potentials are assumed to be semi-smooth or the summation of multiple semi-smooth functions.
• We develop a sampling algorithm that resembles proximal algorithms in optimization for this challenging sampling task.