Inference in higher-order undirected graphical models and binary polynomial optimization
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909429238071296 |
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| author | Khajavirad, Aida Wang, Yakun |
| author_facet | Khajavirad, Aida Wang, Yakun |
| contents | We consider the problem of inference in higher-order undirected graphical models with binary labels. We formulate this problem as a binary polynomial optimization problem and propose several linear programming relaxations for it. We compare the strength of the proposed linear programming relaxations theoretically. Finally, we demonstrate the effectiveness of these relaxations by performing a computational study for two important applications, namely, image restoration and decoding error-correcting codes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09727 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inference in higher-order undirected graphical models and binary polynomial optimization Khajavirad, Aida Wang, Yakun Optimization and Control We consider the problem of inference in higher-order undirected graphical models with binary labels. We formulate this problem as a binary polynomial optimization problem and propose several linear programming relaxations for it. We compare the strength of the proposed linear programming relaxations theoretically. Finally, we demonstrate the effectiveness of these relaxations by performing a computational study for two important applications, namely, image restoration and decoding error-correcting codes. |
| title | Inference in higher-order undirected graphical models and binary polynomial optimization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2405.09727 |