TY - JOUR AU - Weifeng Wang AU - Qiuyu Wang PY - 2014/02/22 Y2 - 2024/03/28 TI - Approximated Function Based Spectral Gradient Algorithm for Sparse Signal Recovery JF - Statistics, Optimization & Information Computing JA - Stat., optim. inf. comput. VL - 2 IS - 1 SE - Research Articles DO - 10.19139/soic.v2i1.33 UR - http://www.iapress.org/index.php/soic/article/view/20140302 AB - 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. ER -