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 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. © 2016, Chen et al.