The author proposes DIFFOPT, a method that utilizes diffusion models to learn the feasible space from data and reformulates optimization problems as sampling problems. The two-stage framework provides better initialization within the data manifold for efficient sampling.
Optimizing problems with unknown constraints using diffusion models.
Optimierung mit unbekannten Einschränkungen durch Diffusionsmodelle.