  Finding a better feasible solution in a shorter time is an integral part of
solving Mixed Integer Programs. We present a post-hoc method based on Neural
Diving to build heuristics more flexibly. We hypothesize that variables with
higher confidence scores are more definite to be included in the optimal
solution. For our hypothesis, we provide empirical evidence that confidence
threshold technique produces partial solutions leading to final solutions with
better primal objective values. Our method won 2nd place in the primal task on
the NeurIPS 2021 ML4CO competition. Also, our method shows the best score among
other learning-based methods in the competition.
