Reflected Anchored Langevin Algorithm

A Langevin dynamics method for sampling non-smooth target distributions over constrained domains. It circumvents non-differentiable barriers by introducing a smooth anchored reference potential and a state-dependent scaling factor, and yields an executable sampling algorithm through Euler–Maruyama discretization.