Collecting intermediate solver iterations during optimization—not just final solutions—significantly increases training data for learning-based optimization without additional computational cost, with theoretical guarantees on generalization.
This paper addresses data scarcity in training models that generate initial guesses for optimization problems. The authors propose collecting intermediate solver steps (k-neighborhoods) alongside final solutions to increase training data without extra computation.