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
Hlavní autori: Chen, Peijun, Huang, Jianguo, Zhang, Xiaoqun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 26.04.2016
Springer Nature B.V
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ISSN:1687-1812, 1687-1812, 2730-5422
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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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ISSN:1687-1812
1687-1812
2730-5422
DOI:10.1186/s13663-016-0543-2