SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints

• URL: http://arxiv.org/abs/2602.09317v1
• Date: Tue, 10 Feb 2026 01:24:32 GMT
• Title: SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints
• Authors: Ya-Chi Chu, Alkiviades Boukas, Madeleine Udell,
• Abstract summary: We propose SnareNet, a feasibility-controlled architecture for learning mappings.<n>It appends a differentiable repair layer that navigates in the constraint map's range space, steering toward feasibility and producing a repaired output that satisfies constraints to a user-specified tolerance.<n>It consistently attains improved objective quality while satisfying constraints more reliably than prior work.
• Score: 10.762945956640051
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Neural networks are increasingly used as surrogate solvers and control policies, but unconstrained predictions can violate physical, operational, or safety requirements. We propose SnareNet, a feasibility-controlled architecture for learning mappings whose outputs must satisfy input-dependent nonlinear constraints. SnareNet appends a differentiable repair layer that navigates in the constraint map's range space, steering iterates toward feasibility and producing a repaired output that satisfies constraints to a user-specified tolerance. To stabilize end-to-end training, we introduce adaptive relaxation, which designs a relaxed feasible set that snares the neural network at initialization and shrinks it into the feasible set, enabling early exploration and strict feasibility later in training. On optimization-learning and trajectory planning benchmarks, SnareNet consistently attains improved objective quality while satisfying constraints more reliably than prior work.

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