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dc.contributor.authorKong, L.
dc.contributor.authorSun, Jie
dc.contributor.authorxiu, N.
dc.date.accessioned2017-01-30T11:08:15Z
dc.date.available2017-01-30T11:08:15Z
dc.date.created2015-04-23T03:53:29Z
dc.date.issued2014
dc.identifier.citationKong, L. and Sun, J. and xiu, N. 2014. S-semigoodness for Low-Rank Semidefinite Matrix Recovery. Pacific Journal of Optimization. 10 (1): pp. 73-83.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/8702
dc.description.abstract

We extend and characterize the concept of s-semigoodness for a sensing matrix in sparse nonnegative recovery (proposed by Juditsky , Karzan and Nemirovski [Math Program, 2011]) to the linear transformations in low-rank semidefinite matrix recovery. We show that ssemigoodnessis not only a necessary and sufficient condition for exact s-rank semidefinitematrix recovery by a semidefinite program, but also provides a stable recovery under someconditions. We also show that both s-semigoodness and semiNSP are equivalent.

dc.publisherYokohama Publishers
dc.subjects-semigoodness
dc.subjectnecessary and sufficient - condition
dc.subjectexact and stable recovery
dc.subjectunitary property
dc.subjectlow-rank semidefinite matrix recovery
dc.titleS-semigoodness for Low-Rank Semidefinite Matrix Recovery
dc.typeJournal Article
dcterms.source.volume10
dcterms.source.number1
dcterms.source.startPage73
dcterms.source.endPage83
dcterms.source.issn1348-9151
dcterms.source.titlePacific Journal of Optimization
curtin.accessStatusFulltext not available


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