A Surrogate Objective Framework for Prediction+Optimization with Soft Constraints

Understand

Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem.

• However, the criteria by which the prediction model is trained are often inconsistent with the goal of the downstream optimization problem.
• Recently, decision-focused prediction approaches, such as SPO+ and direct optimization, have been proposed to fill this gap.
• However, they cannot directly handle the soft constraints with the $max$ operator required in many real-world objectives.