We propose a first-order augmented Lagrangian algorithm (FALC) to solve the composite norm minimization problem min |sigma(F(X)-G)|_alpha + |C(X)- d|_beta subject to A(X)-b in Q; where sigma(X) denotes the vector of singular values of X, the matrix norm |sigma(X)|_alpha denotes either the Frobenius, the nuclear, or the L2-operator norm of X, the vector norm |.|_beta denotes either the L1-norm, L2-norm or the L infty-norm; Q is a closed convex set and A(.), C(.), F(.) are linear operators from matrices to vector spaces of appropriate dimensions. Basis pursuit, matrix completion, robust principal component pursuit (PCP), and stable PCP problems are all special cases of the composite norm minimization problem. Thus, FALC is able to solve all these problems in a unified manner. We show that any limit point of FALC iterate sequence is an optimal solution of the composite norm minimization problem. We also show that for all epsilon > 0, the FALC iterates are epsilon-feasible and epsilon-optimal after O(log(1/epsilon)) iterations, which require O(1/epsilon) constrained shrinkage operations and Euclidean projection onto the set Q. Surprisingly, on the problem sets we tested, FALC required only O(log(1/epsilon)) constrained shrinkage, instead of the O(1/epsilon) worst case bound, to compute an epsilon-feasible and epsilon-optimal solution. To best of our knowledge, FALC is the first algorithm with a known complexity bound that solves the stable PCP problem.