S-semigoodness for Low-Rank Semidefinite Matrix Recovery
| dc.contributor.author | Kong, L. | |
| dc.contributor.author | Sun, Jie | |
| dc.contributor.author | xiu, N. | |
| dc.date.accessioned | 2017-01-30T11:08:15Z | |
| dc.date.available | 2017-01-30T11:08:15Z | |
| dc.date.created | 2015-04-23T03:53:29Z | |
| dc.date.issued | 2014 | |
| dc.identifier.citation | Kong, 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.uri | http://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.publisher | Yokohama Publishers | |
| dc.subject | s-semigoodness | |
| dc.subject | necessary and sufficient - condition | |
| dc.subject | exact and stable recovery | |
| dc.subject | unitary property | |
| dc.subject | low-rank semidefinite matrix recovery | |
| dc.title | S-semigoodness for Low-Rank Semidefinite Matrix Recovery | |
| dc.type | Journal Article | |
| dcterms.source.volume | 10 | |
| dcterms.source.number | 1 | |
| dcterms.source.startPage | 73 | |
| dcterms.source.endPage | 83 | |
| dcterms.source.issn | 1348-9151 | |
| dcterms.source.title | Pacific Journal of Optimization | |
| curtin.accessStatus | Fulltext not available |