A trust-region method and a curvilinear linesearch method are shown to converge to second-order stationary points of composite nonconvex nonsmooth problems, independent of initialization.
Smoothness of Subgradient Mappings and Its Applications in Parametric Optimization
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abstract
We demonstrate that the concept of strict proto-differentiability of subgradient mappings can play a similar role as smoothness of the gradient mapping of a function in the study of subgradient mappings of prox-regular functions. We then show that metric regularity and strong metric regularity are equivalent for a class of generalized equations when this condition is satisfied. For a class of composite functions, called C2-decomposable, we argue that strict proto-differentiability can be characterized via a simple relative interior condition. Leveraging this observation, we present a characterization of the continuous differentiability of the proximal mapping for this class of function via a certain relative interior condition. Applications to the study of strong metric regularity of the KKT system of a class of composite optimization problems are also provided.
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Second-order methods for provably escaping strict saddle points in composite nonconvex and nonsmooth optimization
A trust-region method and a curvilinear linesearch method are shown to converge to second-order stationary points of composite nonconvex nonsmooth problems, independent of initialization.