CoSaMP: 从不完整和不精确样本中迭代恢复信号
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
AI总结：
针对压缩采样中从含噪样本逼近可压缩信号的问题，提出一种名为CoSaMP的迭代恢复算法，其性能与最优优化方法相当，且计算成本与存储需求有严格界限。
摘要：
压缩采样提供了一种新范式，用于获取相对于标准正交基可压缩的信号。压缩采样的主要算法挑战是从含噪样本中逼近可压缩信号。本文描述了一种新的迭代恢复算法CoSaMP，它提供了与最佳优化方法相同的保证。此外，该算法对计算成本和存储给出了严格的界限。由于它只需要与采样矩阵进行矩阵-向量乘法，因此在实际问题中可能非常高效。对于许多感兴趣的情况，运行时间仅为O(N*log^2(N))，其中N是信号的长度。
英文摘要：
Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples. This paper describes a new iterative recovery algorithm called CoSaMP that delivers the same guarantees as the best optimization-based approaches. Moreover, this algorithm offers rigorous bounds on computational cost and storage. It is likely to be extremely efficient for practical problems because it requires only matrix-vector multiplies with the sampling matrix. For many cases of interest, the running time is just O(N*log^2(N)), where N is the length of the signal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
