{"ID":2865431,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.22462","arxiv_id":"2509.22462","title":"Nonlinear Optimization with GPU-Accelerated Neural Network Constraints","abstract":"We propose a reduced-space formulation for optimizing over trained neural networks where the network's outputs and derivatives are evaluated on a GPU. To do this, we treat the neural network as a \"gray box\" where intermediate variables and constraints are not exposed to the optimization solver. Compared to the full-space formulation, in which intermediate variables and constraints are exposed to the optimization solver, the reduced-space formulation leads to faster solves and fewer iterations in an interior point method. We demonstrate the benefits of this method on two optimization problems: Adversarial generation for a classifier trained on MNIST images and security-constrained optimal power flow with transient feasibility enforced using a neural network surrogate.","short_abstract":"We propose a reduced-space formulation for optimizing over trained neural networks where the network's outputs and derivatives are evaluated on a GPU. To do this, we treat the neural network as a \"gray box\" where intermediate variables and constraints are not exposed to the optimization solver. Compared to the full-spa...","url_abs":"https://arxiv.org/abs/2509.22462","url_pdf":"https://arxiv.org/pdf/2509.22462v2","authors":"[\"Robert Parker\",\"Oscar Dowson\",\"Nicole LoGiudice\",\"Manuel Garcia\",\"Russell Bent\"]","published":"2025-09-26T15:13:46Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}