A Compressed Sensing reconstruct algorithm based on trust region method of nonsmooth optimization

Enming Dong, Jianping Li, Jinjie Liu
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Abstract

The signal reconstruction problems of Compressed Sensing is equal to a nonsmooth optimization problem. Since the original signal is sparse, a new l 1 -Minimization reconstruction algorithm is proposed based on modified trust region method of nonsmooth optimization. The algorithm can also reconstruct signal in super-linear convergence rate. Simulation results show that the algorithm is robust in reconstructing the original signal.
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一种基于非光滑优化信赖域方法的压缩感知重构算法
压缩感知的信号重构问题等同于一个非光滑优化问题。针对原始信号的稀疏性,基于改进的非光滑优化信赖域方法,提出了一种新的1.1 -最小化重构算法。该算法还能以超线性的收敛速度重构信号。仿真结果表明,该算法对原始信号的重构具有较好的鲁棒性。
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