Convex Optimization

Sparse Recovery for Overcomplete Frames: Sensing Matrices and Recovery Guarantees
Signal models formed as linear combinations of few atoms from an over-complete dictionary or few frame vectors from a redundant frame …
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On the robustness of noise-blind low-rank recovery from rank-one measurements
We prove new results about the robustness of well-known convex noise-blind optimization formulations for the reconstruction of low-rank …
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Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
The recovery of sparse data is at the core of many applications in machine learning and signal processing. While such problems can be …
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