Minimizing a simple nonsmooth outer function composed with a smooth inner map offers a broad framework for structured optimization. A unifying algorithmic idea solves simple subproblems involving the linearized inner map and a proximal penalty on the step. This talk outlines basic convergence theory, hints at active-set strategies for acceleration, and describes computational experiments.