Temat: nonconvex optimization

Skocz do pozycji: 1.

Tytuł:: Convergence of the Gradient Sampling Algorithm for Nonsmooth Nonconvex Optimization
Raport Badawczy = Research Report ; RB/53/2006
Autorzy:: Kiwiel, Krzysztof
Wydawca:: Instytut Badań Systemowych. Polska Akademia Nauk
Systems Research Institute. Polish Academy of Sciences
Powiązania:: Raport Badawczy = Research Report
Opis:: The paper investigates 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 existing convergence results for this algorithm were reinforced. A slightly revised version has been introduced 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.
Bibliografia s. 10-11
11 pages ; 21 cm
Bibliography p. 10-11
11 stron ; 21 cm
Dostawca treści:: RCIN - Repozytorium Cyfrowe Instytutów Naukowych

Książka

Skocz do pozycji: 2.

Tytuł:: Convergence of the Gradient Sampling Algorithm for Nonsmooth Nonconvex Optimization
Raport Badawczy = Research Report ; RB/48/2005
Autorzy:: Kiwiel, Krzysztof
Wydawca:: Instytut Badań Systemowych. Polska Akademia Nauk
Systems Research Institute. Polish Academy of Sciences
Powiązania:: Raport Badawczy = Research Report
Opis:: Bibliografia s. 10-11
11 pages ; 21 cm
Bibliography p. 10-11
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.
11 stron ; 21 cm
Dostawca treści:: RCIN - Repozytorium Cyfrowe Instytutów Naukowych

Książka

Skocz do pozycji: 3.

Tytuł:: A nonderivative version of the gradient sampling algorithm for nonsmooth nonconvex optimization
Raport Badawczy = Research Report ; RB/35/2009
Autorzy:: Kiwiel, Krzysztof
Wydawca:: Instytut Badań Systemowych. Polska Akademia Nauk
Systems Research Institute. Polish Academy of Sciences
Powiązania:: Raport Badawczy = Research Report
Opis:: Bibliography p. 11-12
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.
Bibliografia s. 11-12
12 stron ; 21 cm
12 pages ; 21 cm
Dostawca treści:: RCIN - Repozytorium Cyfrowe Instytutów Naukowych

Książka

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