Deep learning can accelerate hard optimization problems by providing intelligent warm-start solutions that reduce the search space, rather than replacing traditional solvers entirely.
This paper uses a transformer neural network to predict electricity generator schedules 72 hours ahead, then refines those predictions with rule-based corrections and feeds them to a traditional optimization solver as a starting point.