In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On one hand, we show that if the sampling matrix A satisfies the restricted isometry property of order K + 1 with isometry constant δκ+ 1κ+ 1≥ 1/√κ+1/4.
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Wen et al. (2017) studied this question.
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