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dc.contributor.authorNouy, Anthony
dc.date.accessioned2017-06-08T06:32:29Z
dc.date.available2017-06-08T06:32:29Z
dc.date.issued2016-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624855
dc.description.abstractTensor methods are among the most prominent tools for the numerical solution of high-dimensional problems where functions of multiple variables have to be approximated. Such high-dimensional approximation problems naturally arise in stochastic analysis and uncertainty quantification. In many practical situations, the approximation of high-dimensional functions is made computationally tractable by using rank-structured approximations. In this talk, we present algorithms for the approximation in hierarchical tensor format using statistical methods. Sparse representations in a given tensor format are obtained with adaptive or convex relaxation methods, with a selection of parameters using crossvalidation methods.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/3e4cc2792e114f5092acf36f2f60bac11d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleHierarchical low-rank approximation for high dimensional approximation
dc.typePresentation
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
dc.contributor.institutionEcole Centrale de Nantes
refterms.dateFOA2018-06-13T14:50:06Z


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