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Linear and one-bit compressive sensing with subsampled random convolutions

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Compressive sensing predicts that sparse vectors can recovered from incomplete linear measurements with efficient algorithms in a stable way. While many theoretical results work with Gaussian random measurement matrices, practical applications usually demand for structure. The talk covers the particular case of structured random measurements defined via convolution with a random vector and subsampling (deterministic or random as well). We will give an overview on the corresponding theory and will cover also recent results concerning recovery from one-bit measurements arising in quantized compressive sensing.
Based on joint works with Felix Krahmer, Shahar Mendelson, Sjoerd Dirksen and Hans-Christian Jung.

This talk is part of the Isaac Newton Institute Seminar Series series.

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