Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (11): 210-217.

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Review on sparse optimization algorithms

YU Chunmei   

  1. College of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China
  • Online:2014-06-01 Published:2015-04-08



  1. 西南科技大学 信息工程学院,四川 绵阳 621010

Abstract: Compressed Sensing(CS) is a new theoretical frame about information acquisition and processing developed in recent years. This paper gives an introduction to the basic theory of CS, focused on sparse optimization algorithms, which are divided into three classes in this paper:active set methods, projection operator methods and classical convex programming methods. The basic idea, main research progresses, and adaptive optimization problems of each method are discussed. Finally, some open problems and research directions in sparse optimization of CS are pointed out.

Key words: compressed sensing, sparse optimization, active set methods, projection operator methods, classical convex programming methods

摘要: 压缩感知是近年来发展起来的关于信息获取与处理的全新理论框架。主要介绍压缩感知的基本理论,侧重于稀疏优化算法的综述。将稀疏优化算法分为活动集方法、投影算子法和经典凸规划法。介绍了每种方法的基本思想、研究进展和适用的优化问题。最后,探讨了稀疏优化算法目前存在的问题以及可能的研究方向。

关键词: 压缩感知, 稀疏优化, 活动集方法, 投影算子法, 经典凸规划法