A primal-dual fixed point algorithm for minimization of the sum of three convex separable functions
Many problems arising in image processing and signal recovery with multi-regularization and constraints can be formulated as minimization of a sum of three convex separable functions. Typically, the objective function involves a smooth function with Lipschitz continuous gradient, a linear composite...
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| Vydané v: | Fixed point theory and algorithms for sciences and engineering Ročník 2016; číslo 1; s. 1 - 18 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Cham
Springer International Publishing
26.04.2016
Springer Nature B.V |
| Predmet: | |
| ISSN: | 1687-1812, 1687-1812, 2730-5422 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Many problems arising in image processing and signal recovery with multi-regularization and constraints can be formulated as minimization of a sum of three convex separable functions. Typically, the objective function involves a smooth function with Lipschitz continuous gradient, a linear composite nonsmooth function, and a nonsmooth function. In this paper, we propose a primal-dual fixed point (PDFP) scheme to solve the above class of problems. The proposed algorithm for three-block problems is a symmetric and fully splitting scheme, only involving an explicit gradient, a linear transform, and the proximity operators which may have a closed-form solution. We study the convergence of the proposed algorithm and illustrate its efficiency through examples on fused LASSO and image restoration with non-negative constraint and sparse regularization. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 1687-1812 1687-1812 2730-5422 |
| DOI: | 10.1186/s13663-016-0543-2 |