Two-step inertial Bregman proximal alternating linearized minimization algorithm for nonconvex and nonsmooth problems

In this paper, we study an algorithm for solving a class of nonconvex and nonsmooth nonseparable optimization problems. Based on proximal alternating linearized minimization (PALM), we propose a new iterative algorithm which combines two-step inertial extrapolation and Bregman distance. By constructing appropriate benefit function, with the help of Kurdyka–Łojasiewicz property we establish the convergence of the whole sequence generated by proposed algorithm. We apply the algorithm to signal recovery, quadratic fractional programming problem and show the effectiveness of proposed algorithm.