Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications
Solving optimization problems with sparse or low-rank optimal solutions has been an important topic since the recent emergence of compressed sensing and its matrix extensions such as the matrix rank minimization and robust principal component analysis problems. Compressed sensing enables one to reco...
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| Language: | English |
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2011
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| Online Access: | https://doi.org/10.7916/D8PC38BZ |