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Constructions in combinatorics via neural networks

  • Paper
  • Apr 29, 2021
  • #MachineLearning
Adam Zsolt Wagner
@AdamZsoltWagner
(Author)
arxiv.org
Read on arxiv.org
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1 Mention
We demonstrate how by using a reinforcement learning algorithm, the deep cross-entropy method, one can find explicit constructions and counterexamples to several open conjectures in... Show More

We demonstrate how by using a reinforcement learning algorithm, the deep cross-entropy method, one can find explicit constructions and counterexamples to several open conjectures in extremal combinatorics and graph theory. Amongst the conjectures we refute are a question of Brualdi and Cao about maximizing permanents of pattern avoiding matrices, and several problems related to the adjacency and distance eigenvalues of graphs.

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Timothy Gowers @wtgowers · May 1, 2021
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An interesting paper by Adam Wagner appeared on arXiv a couple of days ago (thanks to Imre Leader for drawing my attention to it), which uses reinforcement learning to find non-trivial counterexamples to several conjectures in graph theory. 1/
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