Mehdi Taghizadeh

Learning Discrete Decisions for MIPs with Constraint-Aware Diffusion

Constrained Graph Diffusion, a graph-based generative diffusion model that learns the discrete component of mixed-integer optimization problems while integrating a training-free feasibility projection operator directly into the reverse diffusion process to steer intermediate samples toward the feasible set throughout generation, is proposed.