Low rank matrix recovery from rank one measurements

We study the recovery of Hermitian low rank matrices X∈Cn×n from undersampled measurements via nuclear norm minimization. We consider the particular scenario where the measurements are Frobenius inner products with random rank-one matrices of the form ajaj⁎ for some measurement vectors a1,…,am, i.e....

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Bibliographic Details
Published in:	Applied and computational harmonic analysis Vol. 42; no. 1; pp. 88 - 116
Main Authors:	Kueng, Richard, Rauhut, Holger, Terstiege, Ulrich
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.01.2017
Subjects:	Complex projective designs Convex optimization Low rank matrix recovery Matrix completion Phase retrieval Quantum state tomography Random measurements 90C25 Convex optimization Phase retrieval Complex projective designs 60B20 Random measurements 94A12 Quantum state tomography 81P50 Low rank matrix recovery 94A20 Matrix completion
ISSN:	1063-5203, 1096-603X
Online Access:	Get full text
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