No abstract

No abstract

We study the gradient sampling algorithm of Burke, Lewis and Overton for minimizing a locally Lipschitz function fon JR.n that is continuously differentiable on an open dense subset. We strengthen the existing convergence results for this algorithm, and introduce a slightly revised version for which stronger results are established without requiring compactness of the level sets off. In particular, we show that with probability 1 the revised algorithm either drives the f-values to-oo, or each of its cluster points is Clarke stationary for f. We also consider a simplified variant in which the differentiability check is skipped and the user can control the number of f-evaluations per iteration.

No abstract