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Bregman Methods for Large-Scale Optimisation with Applications in Imaging

dc.contributor.authorBenning, M
dc.contributor.authorRiis, ES
dc.contributor.editorChen, K
dc.contributor.editorSchönlieb, C-B
dc.contributor.editorTai, X-C
dc.contributor.editorYounes, L
dc.date.accessioned2021-07-01T10:37:18Z
dc.date.available2021-07-01T10:37:18Z
dc.date.issued2021-05-27
dc.identifier.citationBenning, Martin, and Erlend Skaldehaug Riis. "Bregman Methods For Large-Scale Optimisation With Applications In Imaging". Handbook Of Mathematical Models And Algorithms In Computer Vision And Imaging, 2021, pp. 1-42. Springer International Publishing, doi:10.1007/978-3-030-03009-4_62-1. Accessed 1 July 2021.en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/72833
dc.description.abstractIn this chapter we review recent developments in the research of Bregman methods, with particular focus on their potential use for large-scale applications. We give an overview on several families of Bregman algorithms and discuss modifications such as accelerated Bregman methods, incremental and stochastic variants, and coordinate descent-type methods. We conclude this chapter with numerical examples in image and video decomposition, image denoising, and dimensionality reduction with auto-encoders.en_US
dc.format.extent1 - 42
dc.publisherSpringeren_US
dc.relation.ispartofHandbook of Mathematical Models and Algorithms in Computer Vision and Imaging
dc.subjectOptimisationen_US
dc.subjectBregman proximal methodsen_US
dc.subjectBregman iterationsen_US
dc.subjectInverse problemsen_US
dc.subjectNesterov accelerationen_US
dc.subjectMirror descenten_US
dc.subjectKaczmarz methoden_US
dc.subjectCoordinate descenten_US
dc.subjectItoh-Abe methoden_US
dc.subjectAlternating direction method of multipliersen_US
dc.subjectPrimal-dual hybrid gradienten_US
dc.subjectRobust principal component analysisen_US
dc.subjectDeep learningen_US
dc.subjectImage denoisingen_US
dc.titleBregman Methods for Large-Scale Optimisation with Applications in Imagingen_US
dc.typeArticleen_US
dc.rights.holder© 2021, Springer
dc.identifier.doi10.1007/978-3-030-03009-4_62-1
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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