Convex Submodular Minimization with Indicator Variables
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866909677744291840 |
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| author | Han, Shaoning Gómez, Andrés |
| author_facet | Han, Shaoning Gómez, Andrés |
| contents | We study a general class of convex submodular optimization problems with indicator variables. Many applications such as the problem of inferring Markov random fields (MRFs) with a sparsity or robustness prior can be naturally modeled in this form. We show that these problems can be reduced to binary submodular minimization problems, possibly after a suitable reformulation, and thus are strongly polynomially solvable. Furthermore, we develop a parametric approach for computing the associated extreme bases under certain smoothness conditions. This leads to a fast solution method, whose efficiency is demonstrated through numerical experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_13161 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Convex Submodular Minimization with Indicator Variables Han, Shaoning Gómez, Andrés Optimization and Control We study a general class of convex submodular optimization problems with indicator variables. Many applications such as the problem of inferring Markov random fields (MRFs) with a sparsity or robustness prior can be naturally modeled in this form. We show that these problems can be reduced to binary submodular minimization problems, possibly after a suitable reformulation, and thus are strongly polynomially solvable. Furthermore, we develop a parametric approach for computing the associated extreme bases under certain smoothness conditions. This leads to a fast solution method, whose efficiency is demonstrated through numerical experiments. |
| title | Convex Submodular Minimization with Indicator Variables |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2209.13161 |