Object

Title: Convergence of the Gradient Sampling Algorithm for Nonsmooth Nonconvex Optimization

Creator:

Kiwiel, Krzysztof : Autor

Date issued/created:

2006

Resource type:

Tekst

Subtitle:

Raport Badawczy = Research Report ; RB/53/2006

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:

We study 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. We strengthen 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, we show that with probability 1 the revised algorithm either drives the f -values to -∞, or each of its cluster points is Clarke stationary for f. We also consider a simplified variant 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:139712

Source:

RB-2006-53

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

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