On Atomic Norms
How many linear measurements do you need to (efficiently) recover a low rank matrix? What about a sparse vector or an orthogonal matrix? Given that we know our object of interest has some ‘structure’, can we answer this question in a general manner? In this article, I will show you one approach to do so; regression using atomic norms. Most of the material I will cover was presented in the paper, “The Convex Geometry of Linear Inverse Problems” by Venkat Chandrasekaran, et. al. ...