Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1995
DC FieldValueLanguage
dc.contributor.authorYanev, Petko I.-
dc.contributor.authorKontoghiorghes, Erricos John-
dc.date.accessioned2013-01-28T13:15:31Zen
dc.date.accessioned2013-05-16T08:22:27Z-
dc.date.accessioned2015-12-02T09:32:46Z-
dc.date.available2013-01-28T13:15:31Zen
dc.date.available2013-05-16T08:22:27Z-
dc.date.available2015-12-02T09:32:46Z-
dc.date.issued2008-
dc.identifier.citationParallel Computing, 2008, vol. 34, iss. 6-8, pp. 451-468en_US
dc.identifier.issn01678191-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/1995-
dc.description.abstractComputationally efficient parallel algorithms for downdating the least squares estimator of the ordinary linear regression are proposed. The algorithms, which are based on the QR decomposition, are block versions of sequential Givens strategies and efficiently exploit the triangular structure of the data matrices. The first strategy utilizes only part of the orthogonal matrix which is derived from the QR decomposition of the initial data matrix. The rest of the orthogonal matrix is not updated or explicitly computed. A modification of the parallel algorithm, which explicitly computes the whole orthogonal matrix in the downdated QR decomposition, is also considered. An efficient distribution of the matrices over the processors is proposed. Furthermore, the new algorithms do not require any inter-processor communication. The theoretical complexities are derived and experimental results are presented and analyzed. The parallel strategies are scalable and highly efficient for large scale downdating least squares problems. A new parallel block-hyperbolic downdating strategy is developed. The algorithm is rich in BLAS-3 computations, involves negligible duplicated computations and requires insignificant inter-processor communication. It is found to outperform the previous downdating strategies and to be highly efficient for large scale problems. The experimental results confirm the derived theoretical complexities.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofParallel Computingen_US
dc.rights© Elsevieren_US
dc.subjectLeast squaresen_US
dc.subjectParallel algorithmsen_US
dc.subjectCommunication systemsen_US
dc.subjectProblem solvingen_US
dc.subjectRegression analysisen_US
dc.titleParallel algorithms for downdating the least squares estimator of the regression modelen_US
dc.typeArticleen_US
dc.affiliationCyprus University of Technologyen
dc.collaborationUniversité de Neuchâtelen_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationPlovdiv University Paisii Hilendarskien_US
dc.collaborationBirkbeck University of Londonen_US
dc.subject.categoryEconomics and Businessen_US
dc.journalsSubscriptionen_US
dc.countrySwitzerlanden_US
dc.countryCyprusen_US
dc.countryBulgariaen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldSocial Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.parco.2008.01.002en_US
dc.dept.handle123456789/54en
dc.relation.issue6-8en_US
dc.relation.volume34en_US
cut.common.academicyear2007-2008en_US
dc.identifier.spage451en_US
dc.identifier.epage468en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en-
item.fulltextNo Fulltext-
crisitem.journal.journalissn0167-8191-
crisitem.journal.publisherElsevier-
crisitem.author.deptDepartment of Finance, Accounting and Management Science-
crisitem.author.facultyFaculty of Tourism Management, Hospitality and Entrepreneurship-
crisitem.author.orcid0000-0001-9704-9510-
crisitem.author.parentorgFaculty of Management and Economics-
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