Fast Schulze Voting Using Quickselect
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929608321925120 |
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| author | Arora, Arushi Eppstein, David Huynh, Randy Le |
| author_facet | Arora, Arushi Eppstein, David Huynh, Randy Le |
| contents | The Schulze voting method aggregates voter preference data using maxmin-weight graph paths, achieving the Condorcet property that a candidate who would win every head-to-head contest will also win the overall election. Once the voter preferences among $m$ candidates have been arranged into an $m\times m$ matrix of pairwise election outcomes, a previous algorithm of Sornat, Vassilevska Williams and Xu (EC '21) determines the Schulze winner in randomized expected time $O(m^2\log^4 m)$. We improve this to randomized expected time $O(m^2\log m)$ using a modified version of quickselect. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18790 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Schulze Voting Using Quickselect Arora, Arushi Eppstein, David Huynh, Randy Le Data Structures and Algorithms The Schulze voting method aggregates voter preference data using maxmin-weight graph paths, achieving the Condorcet property that a candidate who would win every head-to-head contest will also win the overall election. Once the voter preferences among $m$ candidates have been arranged into an $m\times m$ matrix of pairwise election outcomes, a previous algorithm of Sornat, Vassilevska Williams and Xu (EC '21) determines the Schulze winner in randomized expected time $O(m^2\log^4 m)$. We improve this to randomized expected time $O(m^2\log m)$ using a modified version of quickselect. |
| title | Fast Schulze Voting Using Quickselect |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2411.18790 |