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Award ID contains: 2307827

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  1. (, Information and Inference: A Journal of the IMA)
    Abstract In many applications, we seek to recover signals from linear measurements far fewer than the ambient dimension, given the signals have exploitable structures such as sparse vectors or low rank matrices. In this paper, we work in a general setting where signals are approximately sparse in a so-called atomic set. We provide general recovery results stating that a convex programming can stably and robustly recover signals if the null space of the sensing map satisfies certain properties. Moreover, we argue that such null space property can be satisfied with high probability if each measurement is sub-Gaussian even when the number of measurements are very few. Some new results for recovering signals sparse in a frame, and recovering low rank matrices are also derived as a result. 
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  2. ; ; ; ; (, SIAM Journal on Discrete Mathematics)
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