@article{Wang_Wang_2014, title={Approximated Function Based Spectral Gradient Algorithm for Sparse Signal Recovery}, volume={2}, url={http://www.iapress.org/index.php/soic/article/view/20140302}, DOI={10.19139/soic.v2i1.33}, abstractNote={Numerical algorithms for the l0-norm regularized non-smooth non-convex minimization problems have recently became a topic of great interest within signal processing, compressive sensing, statistics, and machine learning. Nevertheless, the l0-norm makes the problem combinatorial and generally computationally intractable. In this paper, we construct a new surrogate function to approximate l0-norm regularization, and subsequently make the discrete optimization problem continuous and smooth. Then we use the well-known spectral gradient algorithm to solve the resulting smooth optimization problem. Experiments are provided which illustrate this method is very promising.}, number={1}, journal={Statistics, Optimization & Information Computing}, author={Wang, Weifeng and Wang, Qiuyu}, year={2014}, month={Feb.}, pages={10-20} }