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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2507.02117 |
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| _version_ | 1866916824080187392 |
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| author | Vanderkam, Dan |
| author_facet | Vanderkam, Dan |
| contents | Finding all the words on a Boggle board is a classic computer programming problem. With a fast Boggle solver, local optimization techniques such as hillclimbing and simulated annealing can be used to find particularly high-scoring boards. The sheer number of possible Boggle boards has historically prevented an exhaustive search for the global optimum board. We apply Branch and Bound and a decision diagram-like data structure to perform the first such search. We find that the highest-scoring boards found via hillclimbing are, in fact, the global optima. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02117 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Computational Proof of the Highest-Scoring Boggle Board Vanderkam, Dan Data Structures and Algorithms I.2.8, E.1 Finding all the words on a Boggle board is a classic computer programming problem. With a fast Boggle solver, local optimization techniques such as hillclimbing and simulated annealing can be used to find particularly high-scoring boards. The sheer number of possible Boggle boards has historically prevented an exhaustive search for the global optimum board. We apply Branch and Bound and a decision diagram-like data structure to perform the first such search. We find that the highest-scoring boards found via hillclimbing are, in fact, the global optima. |
| title | A Computational Proof of the Highest-Scoring Boggle Board |
| topic | Data Structures and Algorithms I.2.8, E.1 |
| url | https://arxiv.org/abs/2507.02117 |