Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials

Nicolas Zilberstein; Santiago Segarra; Luiz Chamon

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arXiv · 2512.22153

Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials

Nicolas Zilberstein·Santiago Segarra·Luiz Chamon

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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.

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Nicolas Zilberstein, Santiago Segarra, Luiz Chamon. 2025-12-15. Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials. https://arxiv.org/abs/2512.22153

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