{"ID":2882248,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10480","arxiv_id":"2508.10480","title":"Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers","abstract":"We introduce an output layer for neural networks that ensures satisfaction of convex constraints. Our approach, $Π$net, leverages operator splitting for rapid and reliable projections in the forward pass, and the implicit function theorem for backpropagation. We deploy $Π$net as a feasible-by-design optimization proxy for parametric constrained optimization problems and obtain modest-accuracy solutions faster than traditional solvers when solving a single problem, and significantly faster for a batch of problems. We surpass state-of-the-art learning approaches by orders of magnitude in terms of training time, solution quality, and robustness to hyperparameter tuning, while maintaining similar inference times. Finally, we tackle multi-vehicle motion planning with non-convex trajectory preferences and provide $Π$net as a GPU-ready package implemented in JAX.","short_abstract":"We introduce an output layer for neural networks that ensures satisfaction of convex constraints. Our approach, $Π$net, leverages operator splitting for rapid and reliable projections in the forward pass, and the implicit function theorem for backpropagation. We deploy $Π$net as a feasible-by-design optimization proxy...","url_abs":"https://arxiv.org/abs/2508.10480","url_pdf":"https://arxiv.org/pdf/2508.10480v2","authors":"[\"Panagiotis D. Grontas\",\"Antonio Terpin\",\"Efe C. Balta\",\"Raffaello D'Andrea\",\"John Lygeros\"]","published":"2025-08-14T09:32:09Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"math.OC\"]","methods":"[]","has_code":false}