On random batch methods (RBM) for interacting particle systems driven by Lévy processes

In many real-world scenarios, the underlying random fluctuations are non-Gaussian, particularly in contexts where heavy-tailed data distributions arise. A typical example of such non-Gaussian phenomena calls for Lévy noise, which accommodates jumps and extreme variations. We propose the Random Batch Method for interacting particle systems driven by Lévy noises (RBM-Lévy), which can be viewed as an extension of the original RBM algorithm in [S. Jin, L. Li and J.-G. Liu, Random batch methods (RBM) for interacting particle systems, J. Comput. Phys. 400 (2020) 108877]. In our RBM-Lévy algorithm, [Formula: see text] particles are randomly grouped into small batches of size [Formula: see text], and interactions occur only within each batch for a short time. Then one reshuffles the particles and continues to repeat this shuffle-and-interact process. In other words, by replacing the weak interacting force by the strong and sparse interacting force, RBM-Lévy dramatically reduces the computational cost from [Formula: see text] to [Formula: see text] per time step. Meanwhile, the resulting dynamics converges to the original interacting particle system, even at the appearance of the Lévy jump. We rigorously prove this convergence in Wasserstein distance, assuming either a finite or infinite second moment of the Lévy measure. Some numerical examples are given to verify our convergence rate.

Duke Scholars

Author Jian-Guo Liu Physics