dc.contributor.author | Beiser, Florian | |
dc.contributor.author | Keith, Brendan | |
dc.contributor.author | Urbainczyk, Simon | |
dc.contributor.author | Wohlmuth, Barbara | |
dc.date.accessioned | 2023-12-13T10:18:49Z | |
dc.date.available | 2023-12-13T10:18:49Z | |
dc.date.created | 2023-01-27T12:04:38Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | IMA Journal of Numerical Analysis. 2023, 43 (6), 3729-3765. | en_US |
dc.identifier.issn | 0272-4979 | |
dc.identifier.uri | https://hdl.handle.net/11250/3107316 | |
dc.description.abstract | We introduce adaptive sampling methods for stochastic programs with deterministic constraints. First, we propose and analyze a variant of the stochastic projected gradient method, where the sample size used to approximate the reduced gradient is determined on-the-fly and updated adaptively. This method is applicable to a broad class of expectation-based risk measures, and leads to a significant reduction in the individual gradient evaluations used to estimate the objective function gradient. Numerical experiments with expected risk minimization and conditional value-at-risk minimization support this conclusion, and demonstrate practical performance and efficacy for both risk-neutral and risk-averse problems. Second, we propose an SQP-type method based on similar adaptive sampling principles. The benefits of this method are demonstrated in a simplified engineering design application, featuring risk-averse shape optimization of a steel shell structure subject to uncertain loading conditions and model uncertainty. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Oxford University Press | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Adaptive sampling strategies for risk-averse stochastic optimization with constraints | en_US |
dc.title.alternative | Adaptive sampling strategies for risk-averse stochastic optimization with constraints | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © The Author(s) 2023. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. | en_US |
dc.source.pagenumber | 3729-3765 | en_US |
dc.source.volume | 43 | en_US |
dc.source.journal | IMA Journal of Numerical Analysis | en_US |
dc.source.issue | 6 | en_US |
dc.identifier.doi | 10.1093/imanum/drac083 | |
dc.identifier.cristin | 2116404 | |
dc.relation.project | EC/H2020/800898 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |