This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …

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

The optimization and machine learning toolkit (OMLT) is an open-source software package
incorporating neural network and gradient-boosted tree surrogate models, which have been …

Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

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 …

Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …

We establish a broad methodological foundation for mixed-integer optimization with learned
constraints. We propose an end-to-end pipeline for data-driven decision making in which …

Many real-life optimization problems frequently contain one or more constraints or objectives
for which there are no explicit formulae. If however data on feasible and/or infeasible states …

Decision trees are among the most popular machine learning models and are used routinely
in applications ranging from revenue management and medicine to bioinformatics. In this …