A Piecewise Linear Programming Algorithm for Sparse Signal Reconstruction

查看全文 作　　者：Kuangyu [1]Liu;Xiangming [1]Xi;Zhiming [2,3]Xu;Shuning [1]Wang

机构地区：[1]Department of Automation,Tsinghua National Laboratory for Information Science and Technology(TNList),Tsinghua University,Beijing 100084,China;[2]College of Science,Air Force Engineering University,Xi'an 710051;[3]Department of Automation,Tsinghua University,Beijing 100084,China

出　　处：《Tsinghua Science and Technology》索引2017年第22卷第1期,共13页

基　　金：supported by the National Natural Science Foundation of China(Nos.61473165 and 61134012);the National Key Basic Research and Development(973)Program of China(No.2012CB720505) 摘　　要：In order to recover a signal from its compressive measurements, the compressed sensing theory seeks the sparsest signal that agrees with the measurements, which is actually an l_0 norm minimization problem. In this paper, we equivalently transform the l_0 norm minimization into a concave continuous piecewise linear programming,and propose an optimization algorithm based on a modified interior point method. Numerical experiments demonstrate that our algorithm improves the sufficient number of measurements, relaxes the restrictions of the sensing matrix to some extent, and performs robustly in the noisy scenarios. 关键词：线性规划算法分段线性规划信号重构稀疏最小化问题等价范数优化算法数值实验

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A Piecewise Linear Programming Algorithm for Sparse Signal Reconstruction

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