  In this paper, we study a parameterized Douglas-Rachford splitting method for
a class of nonconvex optimization problem. A new merit function is constructed
to establish the convergence of the whole sequence generated by the
parameterized Douglas-Rachford splitting method. We then apply the
parameterized Douglas-Rachford splitting method to three important classes of
nonconvex optimization problems arising in data science: sparsity constrained
least squares problem, feasibility problem and low rank matrix completion.
Numerical results validate the effectiveness of the parameterized
Douglas-Rachford splitting method compared with some other classical methods.
