A primal-dual fixed-point algorithm for minimization of the sum of three convex separable functions

Peijun Chen; Jianguo Huang; Xiaoqun Zhang

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arXiv · 1512.09235

A primal-dual fixed-point algorithm for minimization of the sum of three convex separable functions

Peijun Chen·Jianguo Huang·Xiaoqun Zhang

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Abstract

Many problems arising in image processing and signal recovery with multi-regularization 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 fully splitting symmetric scheme, only involving explicit gradient and linear operators without inner iteration, when the nonsmooth functions can be easily solved via their proximity operators, such as $\ell_1$ type regularization. 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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BibTeX RIS

Peijun Chen, Jianguo Huang, Xiaoqun Zhang. 2015-12-31. A primal-dual fixed-point algorithm for minimization of the sum of three convex separable functions. https://arxiv.org/abs/1512.09235

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