Title: Compressed sensing, RIP, and the null space property
Speaker: Xuemei Chen (Postdoctoral Fellow in the Department of Mathematics at University of Missouri, Columbia)
Abstract: Compressed Sensing is the problem to recover a sparse signal from a set of incomplete measurements. It has numerous applications and has been a big focus for almost the last decade in the field of applied harmonic analysis. In this talk, I will first give an introduction of Compressed Sensing, then I will focus on some desired properties of sensing matrices, like RIP and the null space property. I will explain some of my very recent work, especially on the gap between these two properties.