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Noisy Euclidean Distance Matrix Completion with a Single Missing Node
Department of Combinatorics and Optimization, Faculty of Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.ORCID-id: 0000-0001-8978-5649
Department of Combinatorics and Optimization, Faculty of Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
KTH, Skolan för teknikvetenskap (SCI), Fysik.
2019 (engelsk)Inngår i: Journal of Global Optimization, ISSN 0925-5001, E-ISSN 1573-2916Artikkel i tidsskrift (Fagfellevurdert) Submitted
Abstract [en]

We present several solution techniques for the noisy single source localization problem, i.e. the Euclidean distance matrix completion problem with a single missing node to locate under noisy data. For the case that the sensor locations are fixed, we show that this problem is implicitly convex, and we provide a purification algorithm along with the SDP relaxation to solve it efficiently and accurately. For the case that the sensor locations are relaxed, we study a model based on facial reduction. We present several approaches to solve this problem efficiently, and we compare their performance with existing techniques in the literature. Our tools are semidefinite programming, Euclidean distance matrices, facial reduction, and the generalized trust region subproblem. We include extensive numerical tests.

sted, utgiver, år, opplag, sider
2019.
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URN: urn:nbn:se:kth:diva-247942DOI: 10.1007/s10898-019-00825-7ISI: 000496694500004Scopus ID: 2-s2.0-85071485649OAI: oai:DiVA.org:kth-247942DiVA, id: diva2:1299811
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QS 20190403

Tilgjengelig fra: 2019-03-28 Laget: 2019-03-28 Sist oppdatert: 2019-12-19bibliografisk kontrollert

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