A proximal linearized algorithm with a quasi distance as regularization term for minimizing a DC function (difference of two convex functions) is proposed. If the sequence generated by our algorithm is bounded, it is proved that every cluster point is a critical point of the function under consideration, even if minimizations are performed inexactly at each iteration. A sufficient condition for global convergence is given for a particular case. Finally, an application is given, in a dynamic setting, to determine the limit of the firm, when increasing returns matter in the short run.
March 8, 2015
View A GENERALIZED PROXIMAL LINEARIZED ALGORITHM FOR DC FUNCTIONS WITH APPLICATION TO THE OPTIMAL SIZE OF THE FIRM PROBLEM