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Artículo
Restart of accelerated first order methods with linear convergence under a quadratic functional growth condition
dc.creator | Alamo, Teodoro | es |
dc.creator | Krupa García, Pablo | es |
dc.creator | Limón Marruedo, Daniel | es |
dc.date.accessioned | 2022-09-19T17:37:27Z | |
dc.date.available | 2022-09-19T17:37:27Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 0018-9286 | es |
dc.identifier.issn | 1558-2523 | es |
dc.identifier.uri | https://hdl.handle.net/11441/137219 | |
dc.description.abstract | Accelerated first order methods, also called fast gradient methods, are popular optimization methods in the field of convex optimization. However, they are prone to suffer from oscillatory behaviour that slows their convergence when medium to high accuracy is desired. In order to address this, restart schemes have been proposed in the literature, which seek to improve the practical convergence by suppressing the oscillatory behaviour. This paper presents a restart scheme for accelerated first order methods for which we show linear convergence under the satisfaction of a quadratic functional growth condition, thus encompassing a broad class of non-necessarily strongly convex optimization problems. Moreover, the worst-case convergence rate is comparable to the one obtained using a (generally non-implementable) optimal fixed-rate restart strategy. We compare the proposed algorithm with other restart schemes by applying them to a model predictive control case study. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación PID2019-106212RB-C41 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación PDC2021-121120-C21 | es |
dc.description.sponsorship | Junta de Andalucía -FEDER P20_00546 | es |
dc.format | application/pdf | es |
dc.format.extent | 8 p. | es |
dc.language.iso | eng | es |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Convex Optimization | es |
dc.subject | Accelerated First Order Methods | es |
dc.subject | Restart Schemes | es |
dc.subject | Linear Convergence | es |
dc.title | Restart of accelerated first order methods with linear convergence under a quadratic functional growth condition | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | PID2019-106212RB-C41 | es |
dc.relation.projectID | PDC2021-121120-C21 | es |
dc.relation.projectID | P20 00546 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9691931 | es |
dc.identifier.doi | 10.1109/TAC.2022.3146054 | es |
dc.journaltitle | IEEE Transactions on Automatic Control | es |
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