Trust-Region Methods Without Using Derivatives: Worst Case Complexity and the NonSmooth Case
分析了无导数信赖域方法在光滑函数上的最坏情况复杂性，并针对非光滑情形提出平滑方法，证明了全局收敛性并界定了最坏情况复杂性。
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 convergence, and they have been proven to generate a sequence of iterates converging to stationarity independently of the starting point. Most of such an analysis is carried 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 nonsmooth case, we propose a smoothing approach, for which we prove global convergence and bound the worst case complexity effort. For the special case of nonsmooth functions that result from the composition of smooth and nonsmooth/convex components, we show how to improve the existing results of the literature and make them applicable to the general methodology.