\(\)

We study the impact of nonconvexity on the complexity of nonsmooth optimization, emphasizing objectives such as piecewise linear functions, which may not be weakly convex. We focus on a dimension-independent analysis, slightly modifying a black-box algorithm of Zhang et al. that approximates an $\epsilon$-stationary point of any directionally differentiable Lipschitz objective using $O(\epsilon^{-4})$ calls to a specialized subgradient oracle and a randomized line search. Our simple black-box deterministic version, achieves $O(\epsilon^{-5})$ for any difference-of-convex objective, and $O(\epsilon^{-4})$ for the weakly convex case. Our complexity bound depends on a natural nonconvexity modulus, related, intriguingly, to the negative part of directional second derivatives of the objective, understood in the distributional sense.

## Article

View The cost of nonconvexity in deterministic nonsmooth optimization