DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers

In three linesDisjunctiveNet introduces a neuro-symbolic framework to embed hard mixed-integer linear constraints and logical rules directly into neural networks using differentiable optimization layers. Through hierarchical convex relaxations, the approach ensures exact rule satisfaction while maintaining strong predictive performance on real-world datasets.

Summary generated by Claude — human-verified