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    S-semigoodness for Low-Rank Semidefinite Matrix Recovery

    Access Status
    Fulltext not available
    Authors
    Kong, L.
    Sun, Jie
    xiu, N.
    Date
    2014
    Type
    Journal Article
    
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    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.
    Source Title
    Pacific Journal of Optimization
    ISSN
    1348-9151
    URI
    http://hdl.handle.net/20.500.11937/8702
    Collection
    • Curtin Research Publications
    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.

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