A linearly convergent algorithm for sparse signal reconstruction

Abstract

For the sparse signal reconstruction problem in compressive sensing, we propose a projection-type algorithm without any backtracking line search based on a new formulation of the problem. Under suitable conditions, global convergence and its linear convergence of the designed algorithm are established. The efficiency of the algorithm is illustrated through some numerical experiments on some sparse signal reconstruction problem.Mathematics Subject Classifications. 65H10, 90C33, 90C30.

SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition

“…CS is mainly used in the field of signal recovery [39–42]. Candés and Tao [15] proved that the CS problem can be solved by minimizing ℓ 0 norm model: $$$$\begin{equation} {\begin{cases} \min \Vert {\mathbf {x}}\Vert _0 \\[3pt] \text{s.t.}$$…”

“…CS is mainly used in the field of signal recovery [39][40][41][42]. Candés and Tao [15] proved that the CS problem can be solved by minimizing 𝓁 0 norm model:…”