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

Satpathi, Siddhartha; Chakraborty, Mrityunjoy

doi:10.1016/j.acha.2016.10.001

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…In Figure 2, we compared the reconstruction percentage of different step-sizes of the proposed method with different sparsities in different isometry constants. We set the step size set and the range of sparsity as s ∈ [1,5,10,15] and K ∈ [10 100], respectively. The isometry constant parameter set was δ K ∈ [0.1, 0.2, 0.3, 0.4, 0.5, 0.6].…”

Section: Discussionmentioning

confidence: 99%

“…The computational complexity of these algorithms is significantly lower than that of the convex optimization methods; however, they require more measurement of values for exact recovery and have poor reconstruction performance in a noisy environment. To date, subspace pursuit (SP) [14] and compressive sampling matching pursuit (CoSaMP) [15,16] algorithms have been proposed by incorporating a backtracking strategy. These algorithms offer strong theoretical guarantees and provide robustness to noise.…”

Section: Introductionmentioning

confidence: 99%

“…The average runtime of different algorithm with different measurements in different sparsity conditions (n = 400, s ∈[15,10,15], δ K = 0.1 and m = 2 * K : 5 : 300, Gaussian signal).…”

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confidence: 99%

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Stochastic Gradient Matching Pursuit Algorithm Based on Sparse Estimation

Zhao

Liu

2019

Electronics

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The stochastic gradient matching pursuit algorithm requires the sparsity of the signal as prior information. However, this prior information is unknown in practical applications, which restricts the practical applications of the algorithm to some extent. An improved method was proposed to overcome this problem. First, a pre-evaluation strategy was used to evaluate the sparsity of the signal and the estimated sparsity was used as the initial sparsity. Second, if the number of columns of the candidate atomic matrix was smaller than that of the rows, the least square solution of the signal was calculated, otherwise, the least square solution of the signal was set as zero. Finally, if the current residual was greater than the previous residual, the estimated sparsity was adjusted by the fixed step-size and stage index, otherwise we did not need to adjust the estimated sparsity. The simulation results showed that the proposed method was better than other methods in terms of the aspect of reconstruction percentage in the larger sparsity environment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Gradient Matching Pursuit Algorithm Based on Sparse Estimation

Zhao

Liu

2019

Electronics

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“…(1) orthogonal matching pursuit (OMP) [17]-based OMP-like algorithms, such as compressive sampling matching pursuit (CoSaMP), subspace pursuit (SP) [18], regularized orthogonal matching pursuit (ROMP) [19], generalized orthogonal matching pursuit (GOMP) [20], sparsity adaptive matching pursuit (SAMP) [21], stabilized orthogonal matching pursuit (SOMP) [22], perturbed block orthogonal matching pursuit (PBOMP) [23] and forward backward pursuit (FBP) [24]. A common feature of these algorithms is that in each iteration, a support set is determined to approximate the correct support set according to the correlation value between the measurement vector y and the columns of A.…”

Section: Literature Reviewmentioning

confidence: 99%

Conjugate Gradient Hard Thresholding Pursuit Algorithm for Sparse Signal Recovery

Zhang

Huang

et al. 2019

Algorithms

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We propose a new iterative greedy algorithm to reconstruct sparse signals in Compressed Sensing. The algorithm, called Conjugate Gradient Hard Thresholding Pursuit (CGHTP), is a simple combination of Hard Thresholding Pursuit (HTP) and Conjugate Gradient Iterative Hard Thresholding (CGIHT). The conjugate gradient method with a fast asymptotic convergence rate is integrated into the HTP scheme that only uses simple line search, which accelerates the convergence of the iterative process. Moreover, an adaptive step size selection strategy, which constantly shrinks the step size until a convergence criterion is met, ensures that the algorithm has a stable and fast convergence rate without choosing step size. Finally, experiments on both Gaussian-signal and real-world images demonstrate the advantages of the proposed algorithm in convergence rate and reconstruction performance.

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“…Once the atom is selected, it will not be deleted until the end of the iteration. The other is a class of compressive sampling matching pursuit algorithm (CoSaMP) [24,25], the subspace tracking algorithm (SP) [26,27]. After selecting the matched atoms, they added a backtracking function to delete unstable atoms to better guarantee the quality of the reconstructed signal.…”

Section: Introductionmentioning

confidence: 99%

A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

2020

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The signal reconstruction quality has become a critical factor in compressed sensing at present. This paper proposes a matching pursuit algorithm for backtracking regularization based on energy sorting. This algorithm uses energy sorting for secondary atom screening to delete individual wrong atoms through the regularized orthogonal matching pursuit (ROMP) algorithm backtracking. The support set is continuously updated and expanded during each iteration. While the signal energy distribution is not uniform, or the energy distribution is in an extreme state, the reconstructive performance of the ROMP algorithm becomes unstable if the maximum energy is still taken as the selection criterion. The proposed method for the regularized orthogonal matching pursuit algorithm can be adopted to improve those drawbacks in signal reconstruction due to its high reconstruction efficiency. The experimental results show that the algorithm has a proper reconstruction.

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

Cited by 14 publications

References 11 publications

Stochastic Gradient Matching Pursuit Algorithm Based on Sparse Estimation

Stochastic Gradient Matching Pursuit Algorithm Based on Sparse Estimation

Conjugate Gradient Hard Thresholding Pursuit Algorithm for Sparse Signal Recovery

A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

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