Branch-locking AD techniques for nonsmooth composite functions and nonsmooth implicit functions

A recent nonsmooth vector forward mode of algorithmic differentiation (AD) computes Nesterov's L-derivatives for nonsmooth composite functions; these L-derivatives provide useful sensitivity information to methods for nonsmooth optimization and equation solving. The established reverse AD mode evaluates gradients efficiently for smooth functions, but it does not extend directly to nonsmooth functions. Thus, this article examines branch-locking …