On the number of iterations for convergence of CoSaMP and Subspace Pursuit algorithms

Abstract

In compressive sensing, one important parameter that characterizes the various greedy recovery algorithms is the iteration bound which provides the maximum number of iterations by which the algorithm is guaranteed to converge. In this letter, we present a new iteration bound for the compressive sampling matching pursuit (CoSaMP) algorithm by certain mathematical manipulations including formulation of appropriate sufficient conditions that ensure passage of a chosen support through the two selection stages of CoSaMP, “Augment” and “Update”. Subsequently, we extend the treatment to the subspace pursuit (SP) algorithm. The proposed iteration bounds for both CoSaMP and SP algorithms are seen to be improvements over their existing counterparts, revealing that both CoSaMP and SP algorithms converge in fewer iterations than suggested by results available in literature. • Presents new iteration bounds for CoSaMP and subspace pursuit (SP) algorithms. • Proposed bounds are lesser than their counterparts existing in literature. • The reduction is significant for moderately large K , i.e., system sparsity. • New results on convergence behavior of CoSaMP and SP algorithms presented.