Iterative Regularization via Dual Diagonal Descent
Name
10851_2017_754_ReferencePDF.pdf
Size
6.82 MB
Format
Adobe PDF
Checksum (MD5)
2f705acd1913ceeb05bd6bf0d4912c54
Author(s)
Garrigos, Guillaume
Rosasco, Lorenzo
Villa, Silvia
Date Issued
August 2017
Journal
Journal of Mathematical Imaging and Vision
Publisher
Springer US
Citation
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.
Version
Author's final manuscript
Abstract
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.
MIT Department
McGovern Institute for Brain Research at MIT
Terms of Use
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.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10851-017-0754-0
