Search for: [Abstract = "The article gives a nonderivative version of the gradient sampling algorithm of Burke, Lewis and Overton for minimizing a locally Lipschitz function f on Rn that is continuously differentiable on an open dense subset. Instead of gradients of f, estimates of gradients of the Steklov averages of f were used. It has been shown that the nonderivative version retains the convergence properties of the gradient sampling algorithm. In particular, with probability 1 it either drives the f\-values to \-∞ or each of its cluster points is Clarke stationary for f."]

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