Optimization Over Trained Neural Networks: Taking a Relaxing Walk
August 28, 2024

Besides training, mathematical optimization is also used in deep learning to model and solve formulations over trained neural networks for purposes such as verification, compression, and optimization with learned constraints. However, solving these formulations soon becomes difficult as the network size grows due to the weak linear relaxation and dense constraint matrix. We have seen improvements in recent years with cutting plane algorithms, reformulations, and a heuristic based on Mixed-Integer Linear Programming (MILP).

Join Dr. Thiago Serra for this webinar where he proposes a more scalable heuristic based on exploring global and local linear relaxations of the neural network model.