Trust-region methods without using derivatives: Worst case complexity and the non-smooth case

Trust-region methods are a broad class of methods for continuous optimization that found application in a variety of problems and contexts. In particular, they have been studied and applied for problems without using derivatives. The analysis of trust-region derivative-free methods has focused on global con-vergence, and they have been proved to generate a sequence of iterates converging to stationarity independently of the starting point. Most of such an analysis is car-ried out in the smooth case, and, moreover, little is known about the complexity or global rate of these methods. In this paper, we start by analyzing the worst case complexity of general trust-region derivative-free methods for smooth functions. For the non-smooth case, we propose a smoothing approach, for which we prove global convergence and bound the worst case complexity effort. For the special case of non-smooth functions that result of the composition of smooth and non-smooth/convex components, we show how to improve the existing results of the literature and make them applicable to the general methodology