The Subset Sum Matching Problem
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866916919383162880 |
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| author | Wu, Yufei Torres, Manuel R. Zehtabi, Parisa Lancho, Alberto Pozanco Cashmore, Michael Borrajo, Daniel Veloso, Manuela |
| author_facet | Wu, Yufei Torres, Manuel R. Zehtabi, Parisa Lancho, Alberto Pozanco Cashmore, Michael Borrajo, Daniel Veloso, Manuela |
| contents | This paper presents a new combinatorial optimisation task, the Subset Sum Matching Problem (SSMP), which is an abstraction of common financial applications such as trades reconciliation. We present three algorithms, two suboptimal and one optimal, to solve this problem. We also generate a benchmark to cover different instances of SSMP varying in complexity, and carry out an experimental evaluation to assess the performance of the approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19218 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Subset Sum Matching Problem Wu, Yufei Torres, Manuel R. Zehtabi, Parisa Lancho, Alberto Pozanco Cashmore, Michael Borrajo, Daniel Veloso, Manuela Artificial Intelligence This paper presents a new combinatorial optimisation task, the Subset Sum Matching Problem (SSMP), which is an abstraction of common financial applications such as trades reconciliation. We present three algorithms, two suboptimal and one optimal, to solve this problem. We also generate a benchmark to cover different instances of SSMP varying in complexity, and carry out an experimental evaluation to assess the performance of the approaches. |
| title | The Subset Sum Matching Problem |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.19218 |