@misc{Kiwiel_Krzysztof_Convergence_2005,
 author={Kiwiel, Krzysztof},
 copyright={Creative Commons Attribution BY 4.0 license},
 address={Warszawa},
 journal={Raport Badawczy = Research Report},
 howpublished={online},
 year={2005},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 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.},
 title={Convergence of the Gradient Sampling Algorithm for Nonsmooth Nonconvex Optimization},
 type={Text},
 URL={http://rcin.org.pl/Content/139664/PDF/RB-2005-48.pdf},
 keywords={Nonsmooth optimization, Gradient sampling, Generalized gradient, Nonconvex, Subgradient, Gradient uogólniony, Optymalizacja niegładka, Próbkowanie gradientowe, Niewypukły},
}