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|Title:||Sublinear-time algorithms for compressive phase retrieval||Authors:||Li, Yi
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2020||Source:||Li, Y. & Nakos, V. (2020). Sublinear-time algorithms for compressive phase retrieval. IEEE Transactions On Information Theory, 66(11), 7302-7310. https://dx.doi.org/10.1109/TIT.2020.3020701||Journal:||IEEE Transactions on Information Theory||Abstract:||In the problem of compressed phase retrieval, the goal is to reconstruct a sparse or approximately k-sparse vector x in C n given access to y= |φ x|, where |v| denotes the vector obtained from taking the absolute value of v inCn coordinate-wise. In this paper we present sublinear-time algorithms for a few for-each variants of the compressive phase retrieval problem which are akin to the variants considered for the classical compressive sensing problem in theoretical computer science. Our algorithms use pure combinatorial techniques and near-optimal number of measurements.||URI:||https://hdl.handle.net/10356/161218||ISSN:||0018-9448||DOI:||10.1109/TIT.2020.3020701||Schools:||School of Physical and Mathematical Sciences||Rights:||© 2020 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SPMS Journal Articles|
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