Title: On the number of iterations for convergence of CoSaMP and SP algorithm.
Abstract:Compressive Sampling Matching Pursuit(CoSaMP) and Subspace Pursuit(SP) are popular compressive sensing greedy recovery algorithms. In this letter, we demonstrate that the CoSaMP algorithm can successf...Compressive Sampling Matching Pursuit(CoSaMP) and Subspace Pursuit(SP) are popular compressive sensing greedy recovery algorithms. In this letter, we demonstrate that the CoSaMP algorithm can successfully reconstruct a $K$-sparse signal from a compressed measurement ${\bf y}={\bf A x}$ by a maximum of $5K$ iterations if the sensing matrix ${\bf A}$ satisfies the Restricted Isometry Constant (RIC) of $\delta_{4K} < \frac {1}{\sqrt{5}}$ and SP algorithm can reconstruct within $6K$ iteration when RIC of $\delta_{3K} < \frac {1}{\sqrt{5}}$ is satisfied. The proposed bound in convergence with respect to number of iterations shows improvement over the existing bounds for Subspace Pursuit and provides new results for CoSaMP.Read More
Publication Year: 2014
Publication Date: 2014-04-19
Language: en
Type: preprint
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Cited By Count: 2
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