A dual algorithm for a class of augmented convex signal recovery models
Hui Zhang, Lizhi Cheng, Wotao Yin · Communications in Mathematical Sciences · 2014
Convex optimization models find interesting applications, especially in signal/image processing and compressive sensing.We study some augmented convex models, which are perturbed by strongly convex functions, and propose a dual gradient algorithm.The proposed algorithm includes the linearized Bregman algorithm and the singular value thresholding algorithm as special cases.Based on fundamental properties of proximal operators, we present a concise approach to establish the convergence of both primal and dual sequences, improving the results in the existing literature.Extensions to models with gauge functions are provided.