Iterative Regularization via Dual Diagonal Descent
In the context of linear inverse problems, we propose and study a general iterative regularization method allowing to consider large classes of data-fit terms and regularizers. The algorithm we propose is based on a primal-dual diagonal descent method. Our analysis establishes convergence as well as...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer US
2018
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| Online Access: | http://hdl.handle.net/1721.1/113873 https://orcid.org/0000-0001-6376-4786 |
| _version_ | 1811083361865695232 |
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| author | Garrigos, Guillaume Rosasco, Lorenzo Villa, Silvia |
| author2 | McGovern Institute for Brain Research at MIT |
| author_facet | McGovern Institute for Brain Research at MIT Garrigos, Guillaume Rosasco, Lorenzo Villa, Silvia |
| author_sort | Garrigos, Guillaume |
| collection | MIT |
| description | In the context of linear inverse problems, we propose and study a general iterative regularization method allowing to consider large classes of data-fit terms and regularizers. The algorithm we propose is based on a primal-dual diagonal descent method. Our analysis establishes convergence as well as stability results. Theoretical findings are complemented with numerical experiments showing state-of-the-art performances. |
| first_indexed | 2024-09-23T12:31:51Z |
| format | Article |
| id | mit-1721.1/113873 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:31:51Z |
| publishDate | 2018 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1138732022-10-01T09:35:43Z Iterative Regularization via Dual Diagonal Descent Garrigos, Guillaume Rosasco, Lorenzo Villa, Silvia McGovern Institute for Brain Research at MIT Rosasco, Lorenzo Garrigos, Guillaume In the context of linear inverse problems, we propose and study a general iterative regularization method allowing to consider large classes of data-fit terms and regularizers. The algorithm we propose is based on a primal-dual diagonal descent method. Our analysis establishes convergence as well as stability results. Theoretical findings are complemented with numerical experiments showing state-of-the-art performances. 2018-02-22T19:48:36Z 2018-06-03T05:00:08Z 2017-08 2018-02-09T04:48:20Z Article http://purl.org/eprint/type/JournalArticle 0924-9907 1573-7683 http://hdl.handle.net/1721.1/113873 Garrigos, Guillaume, et al. “Iterative Regularization via Dual Diagonal Descent.” Journal of Mathematical Imaging and Vision, vol. 60, no. 2, Feb. 2018, pp. 189–215. https://orcid.org/0000-0001-6376-4786 en http://dx.doi.org/10.1007/s10851-017-0754-0 Journal of Mathematical Imaging and Vision Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer Science+Business Media, LLC application/pdf Springer US Springer US |
| spellingShingle | Garrigos, Guillaume Rosasco, Lorenzo Villa, Silvia Iterative Regularization via Dual Diagonal Descent |
| title | Iterative Regularization via Dual Diagonal Descent |
| title_full | Iterative Regularization via Dual Diagonal Descent |
| title_fullStr | Iterative Regularization via Dual Diagonal Descent |
| title_full_unstemmed | Iterative Regularization via Dual Diagonal Descent |
| title_short | Iterative Regularization via Dual Diagonal Descent |
| title_sort | iterative regularization via dual diagonal descent |
| url | http://hdl.handle.net/1721.1/113873 https://orcid.org/0000-0001-6376-4786 |
| work_keys_str_mv | AT garrigosguillaume iterativeregularizationviadualdiagonaldescent AT rosascolorenzo iterativeregularizationviadualdiagonaldescent AT villasilvia iterativeregularizationviadualdiagonaldescent |