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Emamnuel J. Candès∗ 写的关于压缩感知的文章，英文版-Compressive sampling write by
Emamnuel J. Candès∗
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求解l1优化问题，在压缩传感中有应用，概算发可以求解三个模型：BP，BPDN和约束版本-Solves L1 problems arsing from Compressive sensing, compressive sampling and sparse optimization
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缩感知（Compressed sensing），也被称为压缩采样(Compressive sampling)或稀疏采样(Sparse sampling)，是一种寻找欠定线性系统的稀疏解的技术。-about compress sensing
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Compressive sensing (CS) is a new approach to simultaneous sensing and compression
that enables a potentially large reduction in the sampling and computation
costs for acquisition of signals having a sparse or compressible representation in some
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e: Compressed Sensing (Compressive Sensing (CS), known as Compressed Sensing, Compressed Sampling). The theory states: compressible signal can be much lower than the Nyquist criterion for sampling data, and still be able to accurately recover the ori
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