Proximal Linearized Iteratively Reweighted Algorithms for Nonconvex and Nonsmooth Optimization Problem

The nonconvex and nonsmooth optimization problem has been attracting increasing attention in recent years image processing machine learning research. algorithm-based reweighted step widely used many applications. In this paper, we propose a new, extended version of the iterative convex majorization–minimization method (ICMM) for solving minimization problem, which involves famous methods. To prove convergence proposed algorithm, adopt general unified framework based on Kurdyka–ojasiewicz inequality. Numerical experiments validate effectiveness algorithm compared to existing