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Disentangling orthogonal matrices

Author(s): Zhang, Teng; Singer, Amit

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dc.contributor.authorZhang, Teng-
dc.contributor.authorSinger, Amit-
dc.date.accessioned2018-07-20T15:10:44Z-
dc.date.available2018-07-20T15:10:44Z-
dc.date.issued2017-07-01en_US
dc.identifier.citationZhang, Teng, Singer, Amit. (2017). Disentangling orthogonal matrices. LINEAR ALGEBRA AND ITS APPLICATIONS, 524 (159 - 181. doi:10.1016/j.laa.2017.03.002en_US
dc.identifier.issn0024-3795-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1s687-
dc.description.abstractMotivated by a certain molecular reconstruction methodology in cryo-electron microscopy, we consider the problem of solving a linear system with two unknown orthogonal matrices, which is a generalization of the well-known orthogonal Procrustes problem. We propose an algorithm based on a semi-definite programming (SDP) relaxation, and give a theoretical guarantee for its performance. Both theoretically and empirically, the proposed algorithm performs better than the naive approach of solving the linear system directly without the orthogonal constraints. We also consider the generalization to linear systems with more than two unknown orthogonal matrices. (C) 2017 Elsevier Inc. All rights reserved.en_US
dc.format.extent159 - 181en_US
dc.language.isoen_USen_US
dc.relation.ispartofLINEAR ALGEBRA AND ITS APPLICATIONSen_US
dc.rightsAuthor's manuscripten_US
dc.titleDisentangling orthogonal matricesen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1016/j.laa.2017.03.002-
dc.date.eissued2017-03-09en_US
dc.identifier.eissn1873-1856-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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