IJCAI 2021poster473 citations
Combinatorial Optimization and Reasoning with Graph Neural Networks
Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Veličković
Abstract
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have mostly focused on solving problem instances in isolation, ignoring the fact that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks, as a key building block for combinatorial tasks, either directly as solvers or by enhancing the former. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at researchers in both optimization and machine learning.
Constraints and SAT: GeneralMachine learning: General
BibTeX
@inproceedings{ijcai2021p595,
title = {Combinatorial Optimization and Reasoning with Graph Neural Networks},
author = {Cappart, Quentin and Chételat, Didier and Khalil, Elias B. and Lodi, Andrea and Morris, Christopher and Veličković, Petar},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {4348--4355},
year = {2021},
month = {8},
note = {Survey Track},
doi = {10.24963/ijcai.2021/595},
url = {https://doi.org/10.24963/ijcai.2021/595},
}