A linearly convergent Gauss-Newton subgradient method for ill-conditioned problems

We analyze a preconditioned subgradient method for optimizing composite functions $h\circ c$, where $h$ is a locally Lipschitz function and $c$ is a smooth nonlinear mapping. We prove that when $c$ satisfies a constant rank property and $h$ is semismooth and sharp on the image of $c$, the method converges linearly. In contrast to standard subgradient methods, its oracle complexity is invariant under reparameterizations of $c$.