An Ensemble Learning Framework for Model Fitting and Evaluation in Inverse Linear Optimization

Understand

We develop a generalized inverse optimization framework for fitting the cost vector of a single linear optimization problem given multiple observed decisions.

• This setting is motivated by ensemble learning, where building consensus from base learners can yield better predictions.
• We unify several models in the inverse optimization literature under a single framework and derive assumption-free and exact solution methods for each one.
• We extend a goodness-of-fit metric previously introduced for the problem with a single observed decision to this new setting, and demonstrate several important properties.