A General Large Neighborhood Search Framework for Solving Integer Programs
Abstract
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.
Additional Information
We thank the anonymous reviewers for their suggestions for improvements. Dilkina was supported partially by NSF #1763108, DARPA, DHS Center of Excellence "Critical Infrastructure Resilience Institute", and Microsoft. This research was also supported in part by funding from NSF #1645832, Raytheon, Beyond Limits, and JPL.Additional details
- Eprint ID
- 118581
- Resolver ID
- CaltechAUTHORS:20221222-181855228
- NSF
- CMMI-1763108
- Defense Advanced Research Projects Agency (DARPA)
- Department of Homeland Security
- Microsoft
- NSF
- CNS-1645832
- Raytheon Company
- Beyond Limits
- JPL
- Created
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2022-12-22Created from EPrint's datestamp field
- Updated
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2022-12-22Created from EPrint's last_modified field
- Caltech groups
- Center for Autonomous Systems and Technologies (CAST)