Solving large-scale l₁ minimization problem is very important and has attracted much attentions in recent years. In this paper, we present an efficient method for solving this problem. To this end, we first reformulate this problem as a nonnegative constrained minimization problem. Then we propose a modulus-based iterative method to solve the nonnegative constrained minimization problem. Convergence analysis and the choice of near optimal parameter are presented. Experimental results are given to illustrate feasibility and effectiveness of our proposed method.

中文翻译：

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基于模量的迭代方法用于稀疏信号恢复

解决大规模的l ₁最小化问题非常重要，并且近年来引起了很多关注。在本文中，我们提出了一种解决此问题的有效方法。为此，我们首先将此问题重新表述为非负约束最小化问题。然后我们提出了一种基于模量的迭代方法来解决非负约束最小化问题。给出了收敛性分析和接近最优参数的选择。实验结果表明了该方法的可行性和有效性。