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Block-Activated Algorithms For Multicomponent Fully Nonsmooth Minimization
Under consideration are multicomponent minimization problems in- volving a separable nonsmooth convex function penalizing the com- ponents individually, and nonsmooth convex coupling terms penal- izing linear mixtures of the components. We investigate the appli- cation of block-activated proximal algorithms for solving such prob- lems, i.e., algorithms which, at each iteration, need to use only a block of the underlying functions, as opposed to all of them as in standard methods. For smooth coupling functions, several block- activated algorithms exist and they are well understood. By con- trast, in the fully nonsmooth case, few block-activated methods are available and little effort has been devoted to assessing them. Our goal is to shed more light on the implementation, the features, and the behavior of these algorithms, compare their merits, and provide machine learning and image recovery experiments illustrating their performance.
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- Award ID(s):
- 1715671
- PAR ID:
- 10335315
- Date Published:
- Journal Name:
- Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
- Volume:
- 2022
- Page Range / eLocation ID:
- 5428 to 5432
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP43922.2022.9747479
