Speaker
Description
In recent years, machine learning has become a prevalent tool to
provide predictive models in many applications. In this talk, we are
interested in using such predictors to model relationships between
variables of an optimization model in Gurobi. For example, a
regression model may predict the demand of certain products as a
function of their prices and marketing budgets among other features.
We are interested in being able to build optimization models that
embed the regression so that the inputs of the regression are decision
variables, and the predicted demand can be satisfied.
We propose a python package that aims at making it easy to insert
regression models trained by popular frameworks (e.g., scikit-learn,
Keras, PyTorch) into a Gurobi model. The regression model may be a
linear or logistic regression, a neural network, or based on decision
trees. The resulting optimization models are often hard to solve with
the current technology. We also present computational results on
improvements that are specifically targeted for those types of models.
In particular, we consider optimization models with embedded neural
networks.
