Subtitle:

Raport Badawczy = Research Report ; RB/48/2005

Publisher:

Instytut Badań Systemowych. Polska Akademia Nauk ; Systems Research Institute. Polish Academy of Sciences

Place of publishing:

Warszawa

Description:

11 pages ; 21 cm ; Bibliography p. 10-11

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.

Relation:

Raport Badawczy = Research Report

Detailed Resource Type:

Report

Resource Identifier:

oai:rcin.org.pl:139664

Source:

RB-2005-48

Language:

eng

Language of abstract:

eng

Rights:

Creative Commons Attribution BY 4.0 license

Terms of use:

Copyright-protected material. [CC BY 4.0] May be used within the scope specified in Creative Commons Attribution BY 4.0 license, full text available at: ; -

Digitizing institution:

Systems Research Institute of the Polish Academy of Sciences

Original in:

Library of Systems Research Institute PAS

Projects co-financed by:

Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.

Access:

Open