Global convergence of a proximal linearized algorithm for difference of convex functions - Souza, João Carlos O.; Oliveira, Paulo Roberto; Soubeyran, Antoine (Optimization letters, 2016)
A proximal linearized algorithm for minimizing difference of two convex functions is proposed. If the sequence generated by the algorithm is bounded it is proved that every cluster point is a critical point of the function under consideration, even if the auxiliary minimizations are performed inexactly at each iteration. Linear convergence of the sequence is established under suitable additional assumptions.