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Phaseless compressive sensing using partial support information
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (Mathematical Statistics)
2017 (English)Manuscript (preprint) (Other academic)
Abstract [en]

We study the recovery conditions of weighted l_1 minimization for real signal reconstruction fromphaseless compressed sensing measurements when partial support information is available. A Strong Restricted Isometry Property (SRIP) condition is provided to ensure the stable recovery. Moreover,we present the weighted null space property as the sucient and necessary condition for the success of k-sparse phase retrieval via weighted l_1 minimization

Place, publisher, year, edition, pages
2017. , p. 9
National Category
Probability Theory and Statistics Computational Mathematics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-141544OAI: oai:DiVA.org:umu-141544DiVA, id: diva2:1155310
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342
Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2018-06-09

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arXiv:1705.04048

Authority records BETA

Zhou, ZhiyongYu, Jun

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CiteExportLink to record
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Citation style
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