Fundamentals of convex optimization for compositional data
dc.contributor.author
dc.date.accessioned
2024-02-01T10:24:25Z
dc.date.available
2024-02-01T10:24:25Z
dc.date.issued
2023-12
dc.identifier.issn
1696-2281
dc.identifier.uri
dc.description.abstract
Many of the most popular statistical techniques incorporate optimisation problems in their inner workings. A convex optimisation problem is defned as the problem of minimising a convex function over a convex set. When traditional methods are applied to compositional data, misleading and incoherent results could be obtained. In this paper, we fll a gap in the specialised literature by introducing and rigorously defning novel concepts of convex optimisation for compositional data according to the Aitchison geometry. Convex sets and convex functions on the simplex are defned and illustrated
dc.format.extent
22 p.
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Institut d'Estadística de Catalunya (Idescat)
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.57645/20.8080.02.11
dc.relation.ispartof
SORT: statistics and operations research transactions, 2023, vol. 47, núm. 2, p. 323-344
dc.relation.ispartofseries
Articles publicats (D-IMAE)
dc.rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.rights.uri
dc.source
Saperas Riera, Jordi Martín Fernández, Josep Antoni Mateu i Figueras, Glòria 2023 Fundamentals of convex optimization for compositional data SORT: statistics and operations research transactions 47 2 323 344
dc.subject
dc.title
Fundamentals of convex optimization for compositional data
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
037486
dc.type.peerreviewed
peer-reviewed
dc.identifier.eissn
2013-8830