Globally Optimal Neural Network Training completed

Training artificial neural networks is a key optimization task in deep learning. This project aims to compute globally optimal solutions to improve generalization, robustness, and explainability using integer programming methods, exploiting mixed-integer nonlinear programming and enhancing techniques like spatial branch-and-cut while leveraging symmetry and sparsity.