贪婪信号恢复审查

D. Needell, J. Tropp, R. Vershynin
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引用次数: 99

摘要

稀疏恢复的两种主要方法是l1最小化法和贪心法。最近,Needell和Vershynin开发了正则化正交匹配追踪(ROMP),弥补了这两种方法之间的差距。ROMP是第一个提供均匀保证的稳定贪婪算法。最近,Needell和Tropp开发了稳定贪婪算法压缩抽样匹配追踪(CoSaMP)。CoSaMP提供了统一的保证,并改进了ROMP的稳定性界限和RIC要求。CoSaMP对计算成本和存储提供了严格的限制。在许多情况下,运行时间仅为O(N log N),其中N是信号的环境维数。本文综述了这些主要进展。
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Greedy signal recovery review
The two major approaches to sparse recovery are L1-minimization and greedy methods. Recently, Needell and Vershynin developed regularized orthogonal matching pursuit (ROMP) that has bridged the gap between these two approaches. ROMP is the first stable greedy algorithm providing uniform guarantees. Even more recently, Needell and Tropp developed the stable greedy algorithm compressive sampling matching pursuit (CoSaMP). CoSaMP provides uniform guarantees and improves upon the stability bounds and RIC requirements of ROMP. CoSaMP offers rigorous bounds on computational cost and storage. In many cases, the running time is just O(N log N), where N is the ambient dimension of the signal. This review summarizes these major advances.
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