  We introduce a new convex formulation for stable principal component pursuit
(SPCP) to decompose noisy signals into low-rank and sparse representations. For
numerical solutions of our SPCP formulation, we first develop a convex
variational framework and then accelerate it with quasi-Newton methods. We
show, via synthetic and real data experiments, that our approach offers
advantages over the classical SPCP formulations in scalability and practical
parameter selection.
