用GPU加速神经网络约束优化，提升求解速度与效率。
Nonlinear Optimization with GPU-Accelerated Neural Network Constraints
- 将神经网络视为黑箱，仅通过输出和导数进行优化
- 相比全空间法，求解更快、迭代次数更少
- 适用于对抗样本生成与电力系统安全优化
我们提出一种基于缩减空间的神经网络优化方法，利用GPU计算神经网络的输出及其导数。该方法将神经网络视为‘黑箱’，不向优化求解器暴露中间变量和约束。与暴露中间变量的全空间法相比，缩减空间法在内点法中实现更快求解和更少迭代次数。我们在两个优化问题上验证了该方法的优势：基于MNIST训练的分类器的对抗样本生成，以及使用神经网络代理模型确保暂态可行性的安全约束最优潮流问题。
原文摘要 · Abstract (English)
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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