提出新方法加速粒子变分推断，提升效率与稳定性。
Accelerating Particle-based Energetic Variational Inference
- 基于能量二次化与算子分裂思想，优化粒子演化路径。
- 避免每步重复计算粒子间交互，显著降低计算开销。
- 适用于多种梯度采样方法，适合高效推断场景。
本文提出一种新型粒子基变分推断（ParVI）方法，用于加速隐式格式的能量变分推断（EVI-Im）。受能量二次化（EQ）和梯度流算子分裂技术启发，该方法高效引导粒子逼近目标分布，同时保持有意义的稳定性机制。与EVI-Im不同，后者采用隐式欧拉法求解通过“离散后变分”得到的保变分粒子动力学以最小化KL散度，本方法在每步时间更新中避免重复评估粒子间相互作用项，显著降低计算成本。该框架还可推广至其他基于梯度的采样技术。多个数值实验表明，所提方法在性能上与现有ParVI方法相当，且在特定情形下具备更高的效率与鲁棒性。
原文摘要 · Abstract (English)
In this work, we propose a new particle-based variational inference (ParVI) method for accelerating the Energetic Variational Inference with Implicit scheme (EVI-Im) introduced in Ref. \cite{wang2021particle}. Inspired by energy quadratization (EQ) and operator splitting techniques for gradient flows, the proposed method efficiently drives particles towards the target distribution, while retaining a meaningful stability mechanism. Unlike EVI-Im, which employs the implicit Euler method to solve variational-preserving particle dynamics obtained from a "discretization-then-variation" approach for minimizing the Kullback--Leibler divergence, the proposed algorithm avoids repeated evaluation of inter-particle interaction terms within each time step, significantly reducing computational cost. The framework is also extensible to other gradient-based sampling techniques. Through several numerical experiments, we demonstrate that the proposed method achieves competitive performance compared with existing ParVI approaches, while offering advantages in efficiency and robustness in certain regimes.
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