In this paper, we develop a simple yet effective screening rule strategy to
improve the computational efficiency in solving structured optimization
involving nonconvex βq,pβ regularization. Based on an iteratively
reweighted β1β (IRL1) framework, the proposed screening rule works like a
preprocessing module that potentially removes the inactive groups before
starting the subproblem solver, thereby reducing the computational time in
total. This is mainly achieved by heuristically exploiting the dual subproblem
information during each iteration.Moreover, we prove that our screening rule
can remove all inactive variables in a finite number of iterations of the IRL1
method. Numerical experiments illustrate the efficiency of our screening rule
strategy compared with several state-of-the-art algorithms