  Integer Linear Programs (ILPs) are powerful tools for modeling and solving a
large number of combinatorial optimization problems. Recently, it has been
shown that Large Neighborhood Search (LNS), as a heuristic algorithm, can find
high quality solutions to ILPs faster than Branch and Bound. However, how to
find the right heuristics to maximize the performance of LNS remains an open
problem. In this paper, we propose a novel approach, CL-LNS, that delivers
state-of-the-art anytime performance on several ILP benchmarks measured by
metrics including the primal gap, the primal integral, survival rates and the
best performing rate. Specifically, CL-LNS collects positive and negative
solution samples from an expert heuristic that is slow to compute and learns a
new one with a contrastive loss. We use graph attention networks and a richer
set of features to further improve its performance.
