  We show that hybrid zonotopes offer an equivalent representation of
feed-forward fully connected neural networks with ReLU activation functions.
Our approach demonstrates that the complexity of binary variables is equal to
the total number of neurons in the network and hence grows linearly in the size
of the network. We demonstrate the utility of the hybrid zonotope formulation
through three case studies including nonlinear function approximation, MPC
closed-loop reachability and verification, and robustness of classification on
the MNIST dataset.
