Reinforcement learning for graph theory, I. Reimplementation of Wagner's approach
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914735710011392 |
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| author | Ghebleh, Mohammad Al-Yakoob, Salem Kanso, Ali Stevanovic, Dragan |
| author_facet | Ghebleh, Mohammad Al-Yakoob, Salem Kanso, Ali Stevanovic, Dragan |
| contents | We reimplement here the recent approach of Adam Zsolt Wagner [arXiv:2104.14516], which applies reinforcement learning to construct (counter)examples in graph theory, in order to make it more readable, more stable and much faster. The presented concepts are illustrated by constructing counterexamples for a number of published conjectured bounds for the Laplacian spectral radius of graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18429 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement learning for graph theory, I. Reimplementation of Wagner's approach Ghebleh, Mohammad Al-Yakoob, Salem Kanso, Ali Stevanovic, Dragan Combinatorics 05C50, 68T20 We reimplement here the recent approach of Adam Zsolt Wagner [arXiv:2104.14516], which applies reinforcement learning to construct (counter)examples in graph theory, in order to make it more readable, more stable and much faster. The presented concepts are illustrated by constructing counterexamples for a number of published conjectured bounds for the Laplacian spectral radius of graphs. |
| title | Reinforcement learning for graph theory, I. Reimplementation of Wagner's approach |
| topic | Combinatorics 05C50, 68T20 |
| url | https://arxiv.org/abs/2403.18429 |