Two modified augmented Lagrange multiplier algorithms for Toeplitz matrix compressive recovery

In this paper, we propose two modified augmented Lagrange multiplier algorithms by mean-value for Toeplitz matrix compressive recovery. In the algorithms, the mean-value modification makes the iteration matrices keep the Toeplitz structure which contributes to reduce the SVD time and CPU time. Numerical experiments show that the proposed algorithms achieve better precision than the augmented Lagrange multiplier method, especially when the matrix E is less sparse. Convergence analysis of the proposed algorithms is also given in detail.