Smooth Learning with Hard Constraints via Legendre-Regularized Policies
We revisit contextual optimization from the perspective of policy class design. A desirable policy class should be expressive enough to learn rich context-decision relationships, should enforce hard feasibility constraints rather than soft penalty terms, and should remain smooth enough for gradient-based training on downstream decision losses. Existing approaches usually emphasize only part of these requirements. We propose Legendre-regularized policies, which parameterize decisions as solutions of regularized optimization problems over the original feasible region. This construction yields policies that are feasible by construction and differentiable with respect to learned latent parame...
| Authors | Zikun Lin, Rui Chen, Yijie Wang |
|---|---|
| Primary category | math.OC |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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