Title :
Greedy signal recovery review
Author :
Needell, Deanna ; Tropp, Joel ; Vershynin, Roman
Author_Institution :
Dept. of Math., Univ. of California, Davis, CA
Abstract :
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.
Keywords :
computational complexity; greedy algorithms; iterative methods; minimisation; signal reconstruction; CoSaMP; L1-minimization; ROMP; ambient signal dimension; compressive sampling matching pursuit; greedy signal recovery; regularized orthogonal matching pursuit; sparse recovery; Compressed sensing; Computational efficiency; Greedy algorithms; Iterative algorithms; Matching pursuit algorithms; Mathematics; Pursuit algorithms; Sampling methods; Sparse matrices; Stability;
Conference_Titel :
Signals, Systems and Computers, 2008 42nd Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4244-2940-0
Electronic_ISBN :
1058-6393
DOI :
10.1109/ACSSC.2008.5074572