Combinatorial optimization and reasoning with graph neural networks
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by
Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, Petar Veličković
2021
Abstract
Combinatorial optimization is a well-established area in operations research
and computer science. Until recently, its methods have 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 (GNNs),
as a key building block for combinatorial tasks, either directly as solvers or
by enhancing exact solvers. The inductive bias of GNNs effectively encodes
combinatorial and relational input due to their invariance to permutations and
awareness of input sparsity. This paper presents a conceptual review of recent
key advancements in this emerging field, aiming at researchers in both
optimization and machine learning.
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