Graph Neural Networks for Maximum Constraint Satisfaction [article]

Jan Toenshoff, Martin Ritzert, Hinrikus Wolf, Martin Grohe
<span title="2020-02-10">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Many combinatorial optimization problems can be phrased in the language of constraint satisfaction problems. We introduce a graph neural network architecture for solving such optimization problems. The architecture is generic; it works for all binary constraint satisfaction problems. Training is unsupervised, and it is sufficient to train on relatively small instances; the resulting networks perform well on much larger instances (at least 10-times larger). We experimentally evaluate our
more &raquo; ... for a variety of problems, including Maximum Cut and Maximum Independent Set. Despite being generic, we show that our approach matches or surpasses most greedy and semi-definite programming based algorithms and sometimes even outperforms state-of-the-art heuristics for the specific problems.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1909.08387v3</a> <a target="_blank" rel="external noopener" href="">fatcat:gb5kx67bvncfxai2jya5ljudku</a> </span>
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