A splitting method for separable convex programming

We propose a splitting method for solving a separable convex minimization problem with linear constraints, where the objective function is expressed as the sum of m individual functions without coupled variables. Treating the functions in the objective separately, the new method belongs to the category of operator splitting methods. We show the global convergence and estimate a worst-case convergence rate for the new method, and then illustrate its numerical efficiency by some applications.