Accelerate Langevin Sampling with Birth-Death Process and Exploration Component

Sampling a probability distribution with known likelihood is a fundamental task in computational science and engineering. Aiming at multimodality, we propose a new sampling method that takes advantage of both the birth-death process and exploration component. The main idea of this method is look before you leap. We keep two sets of samplers, one at a warmer temperature and one at the original temperature. The former one serves as the pioneer in exploring new modes and passing useful information to the other, while the latter one samples the target distribution after receiving the information. We derive a mean-field limit and show how the exploration component accelerates the sampling process. Moreover, we prove exponential asymptotic convergence under mild assumption. Finally, we test this on experiments from previous literature and compare our methodology to previous ones.