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arXiv · 1308.6337

A dual algorithm for a class of augmented convex models

Hui Zhang·Lizhi Cheng·Wotao Yin
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Abstract

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.

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Hui Zhang, Lizhi Cheng, Wotao Yin. 2013-08-29. A dual algorithm for a class of augmented convex models. https://arxiv.org/abs/1308.6337

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