Discrepancy Minimization via Regularization
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866918446176927744 |
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| author | Pesenti, Lucas Vladu, Adrian |
| author_facet | Pesenti, Lucas Vladu, Adrian |
| contents | We introduce a new algorithmic framework for discrepancy minimization based on regularization. We demonstrate how varying the regularizer allows us to re-interpret several breakthrough works in algorithmic discrepancy, ranging from Spencer's theorem [Spencer 1985, Bansal 2010] to Banaszczyk's bounds [Banaszczyk 1998, Bansal-Dadush-Garg 2016]. Using our techniques, we also show that the Beck-Fiala and Komlos conjectures are true in a new regime of pseudorandom instances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_05509 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Discrepancy Minimization via Regularization Pesenti, Lucas Vladu, Adrian Data Structures and Algorithms Discrete Mathematics We introduce a new algorithmic framework for discrepancy minimization based on regularization. We demonstrate how varying the regularizer allows us to re-interpret several breakthrough works in algorithmic discrepancy, ranging from Spencer's theorem [Spencer 1985, Bansal 2010] to Banaszczyk's bounds [Banaszczyk 1998, Bansal-Dadush-Garg 2016]. Using our techniques, we also show that the Beck-Fiala and Komlos conjectures are true in a new regime of pseudorandom instances. |
| title | Discrepancy Minimization via Regularization |
| topic | Data Structures and Algorithms Discrete Mathematics |
| url | https://arxiv.org/abs/2211.05509 |