Toeplitz/Hankel

Learning Transition Operators From Sparse Space-Time Samples
We consider the nonlinear inverse problem of learning a transition operator A from partial observations at T different times, in the …
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Understanding and Enhancing Data Recovery Algorithms - From Noise-Blind Sparse Recovery to Reweighted Methods for Low-Rank Matrix Optimization
We prove new results about the robustness of noise-blind decoders for the problem of re- constructing a sparse vector from …
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Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares
We propose a new Iteratively Reweighted Least Squares (IRLS) algorithm for the problem of completing a low-rank matrix that is linearly …
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Denoising and Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares
We propose a new Iteratively Reweighted Least Squares (IRLS) algorithm for the problem of completing or denoising low-rank matrices …
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