A Unified Approach for Minimizing Composite Norms
Necdet Serhat Aybat, Garud Iyengar

TL;DR
This paper introduces FALC, a first-order augmented Lagrangian algorithm that unifies the solution of various composite norm minimization problems, including basis pursuit and matrix completion, with proven convergence and efficiency.
Contribution
FALC is the first algorithm with a known complexity bound that can solve the stable PCP problem and unifies multiple matrix optimization problems.
Findings
FALC converges to an optimal solution with O(log(1/epsilon)) iterations.
FALC requires only O(log(1/epsilon)) shrinkage operations in practice.
FALC successfully solves stable PCP problems, a novel achievement.
Abstract
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…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Radar Systems and Signal Processing · Complexity and Algorithms in Graphs
