S-semigoodness for Low-Rank Semidefinite Matrix Recovery
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 a...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Yokohama Publishers
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/8702 |
| Summary: | 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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