In this work, we propose a novel particle-based variational inference (ParVI) method that accelerates the EVI-Im. Inspired by energy quadratization (EQ) and operator splitting techniques for gradient flows, our approach efficiently drives particles towards the target distribution. Unlike EVI-Im, which employs the implicit Euler method to solve variational-preserving particle dynamics for minimizing the KL divergence, derived using a "discretize-then-variational" approach, the proposed algorithm avoids repeated evaluation of inter-particle interaction terms, significantly reducing computational cost. The framework is also extensible to other gradient-based sampling techniques. Through several numerical experiments, we demonstrate that our method outperforms existing ParVI approaches in efficiency, robustness, and accuracy.
View on arXiv@article{bao2025_2504.03158,
title={ Accelerating Particle-based Energetic Variational Inference },
author={ Xuelian Bao and Lulu Kang and Chun Liu and Yiwei Wang },
journal={arXiv preprint arXiv:2504.03158},
year={ 2025 }
}