Constrained Diffusion Models with Primal-Dual Inference
This work develops constrained diffusion models using primal-dual inference (PDI) to sample from optimal distributions for optimization problems with average constraints. PDI interleaves dual ascent and reverse diffusion, using a dual variable to guide sampling. This method yields better feasibility and performance than unconstrained samplers by learning a family of score fields conditioned on the dual variable.