CAffNet: Hard Constraint-Affine Neural Networks
We present a novel framework for embedding hard constraint satisfaction into neural network (NN) architectures, specifically feedforward neural networks and transformers, with input-dependent affine constraints of arbitrary cardinality. Traditional constraint enforcement approaches either rely on penalty-based soft constraints, which offer no guarantee of satisfaction, or on post-processing methods that enforce constraints after the NN is trained, which may lead to suboptimality. We introduce a trainable constraint-affine (CAffine) layer into NNs, yielding CAffNet, which goes beyond enforcing affine constraints via fixed orthogonal or parallel projections and enables joint optimization wi...
| Authors | Yang Zhao, Jungeun Lee, Jeong hwan Jeon et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 May 2026 |
| Last updated at source | 21 September 2026 |
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