A parameterized Douglas–Rachford splitting algorithm for nonconvex optimization

In this paper, we study a parameterized Douglas–Rachford splitting method in Wang-Wang (2019)[5] for class of nonconvex optimization problem. A new merit function is constructed to establish the convergence whole sequence generated by method. As by-product, also provides results special case adaptive algorithm proposed Dao and Phan (2019)[22] settings. We then apply three important classes problems arising data science: sparsity constrained least squares problem, feasibility problem low rank matrix completion. Numerical validate effectiveness compared with some other classical methods.