Bonsai: A class of effective methods for independent sampling of graph partitions
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912973722746880 |
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| author | Clelland, Jeanne Tapp, Kristopher |
| author_facet | Clelland, Jeanne Tapp, Kristopher |
| contents | We develop effective methods for constructing an ensemble of district plans via independent sampling from a reasonable probability distribution on the space of graph partitions. We compare the performance of our algorithms to that of standard Markov Chain based algorithms in the context of grid graphs and state congressional and legislative maps. For the case of perfect population balance between districts, we provide an explicit description of the distribution from which our method samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18347 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bonsai: A class of effective methods for independent sampling of graph partitions Clelland, Jeanne Tapp, Kristopher Data Structures and Algorithms Computers and Society Social and Information Networks We develop effective methods for constructing an ensemble of district plans via independent sampling from a reasonable probability distribution on the space of graph partitions. We compare the performance of our algorithms to that of standard Markov Chain based algorithms in the context of grid graphs and state congressional and legislative maps. For the case of perfect population balance between districts, we provide an explicit description of the distribution from which our method samples. |
| title | Bonsai: A class of effective methods for independent sampling of graph partitions |
| topic | Data Structures and Algorithms Computers and Society Social and Information Networks |
| url | https://arxiv.org/abs/2603.18347 |