First-order methods for sparse covariance selection

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dc.creatord'Aspremont, Alexandre
dc.creatorBanerjee, Onureena
dc.creatorGhaoui, Laurent El
dc.date2006-09-28
dc.date.accessioned2026-07-07T08:08:14Z
dc.date.available2026-07-07T08:08:14Z
dc.descriptionGiven a sample covariance matrix, we solve a maximum likelihood problem penalized by the number of nonzero coefficients in the inverse covariance matrix. Our objective is to find a sparse representation of the sample data and to highlight conditional independence relationships between the sample variables. We first formulate a convex relaxation of this combinatorial problem, we then detail two efficient first-order algorithms with low memory requirements to solve large-scale, dense problem instances.
dc.identifierhttps://arxiv.org/abs/math/0609812
dc.identifierhttp://arxiv.org/abs/math/0609812
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131194
dc.subjectOptimization and Control
dc.subjectStatistics Theory
dc.subject90C22, 62H20, 90C59
dc.titleFirst-order methods for sparse covariance selection
dc.typetext

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