| Summary: | In this paper, we analyze the performance guarantee of multiple orthogonal least squares (MOLS) in recovering sparse signals. Specifically, we show that the MOLS algorithm ensures the accurate recovery of any K-sparse signal, provided that a sampling matrix satisfies the restricted isometry property (RIP) with δ<sub>LK-L+2</sub> <; √L/K+2L-1 where L is the number of indices chosen in each iteration. In particular, if L=1, our result indicates that the conventional OLS algorithm exactly reconstructs any K-sparse vector under δ<sub>K+1</sub> <; 1/K+1, which is consistent with the best existing result for OLS.
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