Search for: [Abstract = "The paper deals with 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. The authors strengthened 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 of f. In particular, it has been shown that with probability 1 the revised algorithm either drives the f \-values to \-∞, or each of its cluster points is Clarke stationary for f. A simplified variant was also considered in which the differentiability check is skipped and the user can control the number of f \-evaluations per iteration."]

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