一篇关于压缩感知的经典文章，压缩感知（Compressed sensing，简称CS，也称为Compressive sampling)理论异于近代奈奎斯特采样定理，它指出：利用随机观测矩阵可以把一个稀疏或可压缩的高维信号投影到低维空间上，然后再利用这些少量的投影通过解一个优化问题就可以以高概率重构原始稀疏信号，并且证明了这样的随机投影包含了原始稀疏信号的足够信息。-A classic article on compressed sensing, compressive sensing (Comp

简要分析了压缩感知中的几个重要定理，有一定的借鉴作用。-This paper talks about some important theories in compressive sampling.

压缩感知重构算法 压缩采样匹配追踪算法 从原子库中选择多个相关的原子,同时剔除部分原子, -Compressed sensing reconstruction algorithm compressive sampling matching pursuit algorithm library, select from the atomic number of the atom, while removing some atoms,

压缩感知信号恢复算法OMP和CoSaMP算法，常用的算法- x = CoSaMP( A, b, k ) uses the Compressive Sampling Matched Pursuit (CoSaMP) algorithm (see Needell and Tropp s 2008 paper http://arxiv.org/abs/0803.2392 ) to estimate the solution to the equation