Nonlinear Optimization with GPU-Accelerated Neural Network Constraints

• URL: http://arxiv.org/abs/2509.22462v1
• Date: Fri, 26 Sep 2025 15:13:46 GMT
• Title: Nonlinear Optimization with GPU-Accelerated Neural Network Constraints
• Authors: Robert Parker, Oscar Dowson, Nicole LoGiudice, Manuel Garcia, Russell Bent,
• Abstract summary: We treat the neural network as a "gray box" where intermediate variables and constraints are not exposed to the optimization solver.<n>Compared to the full-space formulation, the reduced-space formulation leads to faster solves and fewer iterations in an interior point method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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