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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, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.ORCID iD: 0000-0001-8978-5649
Department of Combinatorics and Optimization, Faculty of Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
KTH, School of Engineering Sciences (SCI), Physics.
2019 (English)In: Journal of Global Optimization, ISSN 0925-5001, E-ISSN 1573-2916Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2019.
National Category
Natural Sciences
Identifiers
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
Note

QS 20190403

Available from: 2019-03-28 Created: 2019-03-28 Last updated: 2019-12-19Bibliographically approved

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Wang, FeiPettersson, Lucas

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