A.1 Algorithm Configuration Results In this section, we present the algorithm configuration results similar to the one in Section 5.1. Numbers represent improvement ratios /t for one decomposition, averaged over 5 random seeds. A.2 Visualization A natural question is what property a good decomposition has. Here we provide one interpretation for the risk-aware path planning. We use a slightly smaller instance with 20 obstacles for a clearer 13 k t 1 2 3 2 54358.

This paper studies how to design abstractions of large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways, and that are amenable to data-driven design. The goal is to arrive at new approaches that can reliably outperform existing solvers in wall-clock time. We focus on solving integer programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic approaches and their software implementations. We also show that one can learn a good neighborhood selector from training data. Through an extensive empirical validation, we demonstrate that our LNS framework can significantly outperform, in wall-clock time, compared to state-of-the-art commercial solvers such as Gurobi.