SCS

Abstract

The problem of estimating a high dimensional vector from a set of linear observations arises in a number of engineering disciplines. It becomes particularly challenging when the underlying signal has some non-linear structure that needs to be exploited. I will present a new class of iterative algorithms inspired by probabilistic graphical models ideas, that appear to be asymptotically optimal in specific contexts. I will discuss in particular the application to compressed sensing problems. [Joint work with David L. Donoho and Arian Maleki]