AI RESEARCH
Decentralized Proximal Stochastic Gradient Langevin Dynamics
arXiv CS.LG
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ArXi:2605.00723v1 Announce Type: cross We propose Decentralized Proximal Stochastic Gradient Langevin Dynamics (DE-PSGLD), a decentralized Marko chain Monte Carlo (MCMC) algorithm for sampling from a log-concave probability distribution constrained to a convex domain. Constraints are enforced through a shared proximal regularization based on the Moreau-Yosida envelope, enabling unconstrained updates while preserving consistency with the target constrained posterior.