skip to content

Linear and one-bit compressive sensing with subsampled random convolutions

Presented by: 
Holger Rauhut
Tuesday 18th June 2019 - 14:20 to 15:10
INI Seminar Room 1
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.
The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons