CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
Provides an efficient, provably correct algorithm for compressive sensing, a key problem in signal processing.
CoSaMP is a new iterative algorithm for recovering compressible signals from noisy compressive samples, achieving guarantees comparable to optimization-based methods with O(N log^2 N) runtime.
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.