AI RESEARCH
HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
arXiv CS.LG
•
ArXi:2604.13179v1 Announce Type: cross This paper presents HUANet, a constrained deep neural network architecture that unrolls the iterations of the Alternating Direction Method of Multipliers (ADMM) into a trainable neural network for solving constrained convex optimization problems. Existing end-to-end learning methods operate as black-box mappings from parameters to solutions, often lacking explicit optimality principles and failing to enforce constraints.