As a powerful modelling method, piecewise linear neural networks (PWLNNs) have proven
successful in various fields, most recently in deep learning. To apply PWLNN methods, both …

We present strong mixed-integer programming (MIP) formulations for high-dimensional
piecewise linear functions that correspond to trained neural networks. These formulations …

Data-driven decision-making has garnered growing interest as a result of the increasing
availability of data in recent years. With that growth many opportunities and challenges have …

We consider the embedding of piecewise-linear deep neural networks (ReLU networks) as
surrogate models in mixed-integer linear programming (MILP) problems. A MILP formulation …

In the past decade, deep learning became the prevalent methodology for predictive
modeling thanks to the remarkable accuracy of deep neural networks in tasks such as …

ReLU neural networks have been modelled as constraints in mixed integer linear
programming (MILP), enabling surrogate-based optimisation in various domains and …

Deep neural networks have been successful in many predictive modeling tasks, such as
image and language recognition, where large neural networks are often used to obtain good …

Value-based reinforcement learning (RL) methods like Q-learning have shown success in a
variety of domains. One challenge in applying Q-learning to continuous-action RL problems …

We can compare the expressiveness of neural networks that use rectified linear units
(ReLUs) by the number of linear regions, which reflect the number of pieces of the piecewise …

We study the problem of optimizing a tree-based ensemble objective with the feasible
decisions lie in a polyhedral set. We model this optimization problem as a Mixed Integer …