Unrolled Neural Networks for Constrained Optimization
This work develops unrolled neural networks called Constrained Dual Unrolling (CDU) to solve constrained optimization problems by combining primal and dual networks. The framework imposes descent and ascent constraints during training, leading to accelerated convergence compared to traditional dual ascent methods. This results in near-optimal solutions with strong generalization, outperforming unconstrained models.