Extreme point inequalities and geometry of the rank sparsity ball
We investigate geometric features of the unit ball corresponding to the sum of the nuclear norm of a matrix and the \(l_1\) norm of its entriesa common penalty function encouraging joint low rank and high sparsity. As a byproduct of this effort, we develop a calculus (or algebra) of faces for general convex functions, yielding a simple and unified approach for deriving inequalities balancing the various features of the optimization problem at hand, at the extreme points of the solution set.
rank sparsity nuclear norm compressed sensing exposed face convex analysis 90c25 47n10 68p30