  Sparse signal recovery is one of the most fundamental problems in various
applications, including medical imaging and remote sensing. Many greedy
algorithms based on the family of hard thresholding operators have been
developed to solve the sparse signal recovery problem. More recently, Natural
Thresholding (NT) has been proposed with improved computational efficiency.
This paper proposes and discusses convergence guarantees for stochastic natural
thresholding algorithms by extending the NT from the deterministic version with
linear measurements to the stochastic version with a general objective
function. We also conduct various numerical experiments on linear and nonlinear
measurements to demonstrate the performance of StoNT.
