Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials
Computation
2025-12-30 v1 Machine Learning
Machine Learning
Abstract
We introduce shielded Langevin Monte Carlo (LMC), a constrained sampler inspired by navigation functions, capable of sampling from unnormalized target distributions defined over punctured supports. In other words, this approach samples from non-convex spaces defined as convex sets with convex holes. This defines a novel and challenging problem in constrained sampling. To do so, the sampler incorporates a combination of a spatially adaptive temperature and a repulsive drift to ensure that samples remain within the feasible region. Experiments on a 2D Gaussian mixture and multiple-input multiple-output (MIMO) symbol detection showcase the advantages of the proposed shielded LMC in contrast to unconstrained cases.
Cite
@article{arxiv.2512.22153,
title = {Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials},
author = {Nicolas Zilberstein and Santiago Segarra and Luiz Chamon},
journal= {arXiv preprint arXiv:2512.22153},
year = {2025}
}