Closely integrating ML and discrete optimization provides key advantages in improving our ability to solve NP-hard real-world optimization problems effectively. On one hand, we can rethink the traditional branch-and-bound tree search for Mixed Integer Programming through the lens of learning-driven algorithm design to create more flexible combinatorial solvers able to learn tailored solution strategies over distributions of instances. In the opposite direction, I will illustrate how combinatorial optimization can be directly integrated into deep learning pipelines to facilitate decision-focused learning -- where the training loss is a function of the quality of downstream optimization decisions based on parameters estimated by the ML model.
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