@misc{Kiwiel_Krzysztof_A_2009,
 author={Kiwiel, Krzysztof},
 copyright={Creative Commons Attribution BY 4.0 license},
 address={Warszawa},
 journal={Raport Badawczy = Research Report},
 howpublished={online},
 year={2009},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 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.},
 type={Text},
 title={A nonderivative version of the gradient sampling algorithm for nonsmooth nonconvex optimization},
 URL={http://www.rcin.org.pl/Content/144781/PDF/RB-2009-35.pdf},
 keywords={Nonsmooth optimization, Gradient sampling, Generalized gradient, Nonconvex, Subgradient, Gradient uogólniony, Optymalizacja niegładka, Próbkowanie gradientowe, Niewypukły, Funkcje uśrednione, Averaged functions},
}